From 782ed120241780b3071fb6d5b1622cab15a52c66 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Fri, 29 May 2026 23:33:35 -0400 Subject: [PATCH 01/21] initial commit --- .../03-web-search/01-setup-gateway/README.md | 133 +++++++ .../01-setup-gateway/setup_gateway.py | 351 ++++++++++++++++++ .../03-web-search/02-raw-mcp/README.md | 90 +++++ .../03-web-search/02-raw-mcp/raw_mcp_call.py | 124 +++++++ .../03-web-search/03-strands-agent/README.md | 97 +++++ .../03-strands-agent/web_search_strands.py | 109 ++++++ .../04-langchain-agent/README.md | 99 +++++ .../web_search_langchain.py | 131 +++++++ .../03-web-search/05-cleanup/README.md | 22 ++ .../03-web-search/05-cleanup/cleanup.py | 132 +++++++ .../03-web-search/README.md | 188 ++++++++++ .../images/inbound-and-outbound-auth.png | Bin 0 -> 19563 bytes .../images/tutorial-architecture.png | Bin 0 -> 31128 bytes .../03-web-search/requirements.txt | 8 + .../03-web-search/utils/__init__.py | 0 .../03-web-search/utils/gateway_auth.py | 104 ++++++ .../03-web-search/utils/web_search_agent.py | 80 ++++ .../README.md | 30 +- 18 files changed, 1696 insertions(+), 2 deletions(-) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/images/inbound-and-outbound-auth.png create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/images/tutorial-architecture.png create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/utils/__init__.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md new file mode 100644 index 000000000..7fa5208a8 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -0,0 +1,133 @@ +# Set Up AgentCore Gateway with Web Search Tool + +## Overview + +This script creates all the infrastructure needed to expose the Web Search Tool through an AgentCore Gateway. After running it, you'll have a fully functional MCP endpoint that any agent framework can connect to. + +``` +┌──────────────────┐ ┌─────────────────────────────────────┐ +│ setup_gateway │────────▶│ AWS Resources Created: │ +│ │ │ │ +│ 1. IAM Role │ │ • IAM Role (InvokeWebSearch) │ +│ 2. Cognito │ │ • Cognito User Pool + M2M Client │ +│ 3. Gateway │ │ • AgentCore Gateway (MCP) │ +│ 4. Target │ │ • Web Search Connector Target │ +└──────────────────┘ └─────────────────────────────────────┘ +``` + +## How It Works + +### Gateway Service Role + +The Gateway needs an IAM role that allows the AgentCore service to perform actions on your behalf. For the Web Search Tool, the role needs two permissions: + +- `bedrock-agentcore:InvokeGateway` — to invoke the Gateway itself +- `bedrock-agentcore:InvokeWebSearch` — to authorize web search invocations against `arn:aws:bedrock-agentcore::aws:tool/web-search.v1` + +### Cognito Authentication + +The script creates a Cognito User Pool with: +- A resource server defining an `invoke` scope +- A machine-to-machine (M2M) client using the `client_credentials` OAuth flow + +This provides JWT-based authentication for incoming requests to the Gateway. + +### Gateway Creation + +The Gateway is created with: +- **Protocol**: MCP (Model Context Protocol) +- **MCP version**: 2025-03-26 +- **Search type**: SEMANTIC (enables tool discovery across multiple targets) +- **Authorizer**: Custom JWT backed by the Cognito User Pool + +### Web Search Connector Target + +The Web Search Tool uses the `connector` target type — a fully AWS-managed integration. You specify `connectorId: "web-search"` and the Gateway handles schema management, endpoint resolution, and service authentication automatically. + +Outbound authentication uses `GATEWAY_IAM_ROLE` — the Gateway's own service role authenticates to the Web Search backend. No additional credentials needed. + +## Prerequisites + +```bash +pip install -r ../requirements.txt +``` + +Your AWS credentials must have permissions to create IAM roles, Cognito pools, and AgentCore Gateways. Your account must be allowlisted for the Web Search Tool private beta. + +> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. + +## Usage + +```bash +# Default setup +python setup_gateway.py + +# Custom gateway name +python setup_gateway.py --gateway-name my-web-search-gw + +# Explicit region +python setup_gateway.py --region us-east-1 +``` + +After completion, the script prints environment variables to export: + +```bash +export AGENTCORE_GATEWAY_URL="https://..." +export COGNITO_DOMAIN="..." +export COGNITO_CLIENT_ID="..." +export COGNITO_CLIENT_SECRET="..." +export COGNITO_SCOPE="agentcore-websearch/invoke" +export AWS_DEFAULT_REGION="us-east-1" +``` + +Export these before running the other demos. + +## IAM Permissions + +### Caller (you) + +```json +{ + "Effect": "Allow", + "Action": [ + "iam:CreateRole", + "iam:PutRolePolicy", + "iam:GetRole", + "cognito-idp:CreateUserPool", + "cognito-idp:CreateUserPoolDomain", + "cognito-idp:CreateResourceServer", + "cognito-idp:CreateUserPoolClient", + "cognito-idp:ListUserPools", + "cognito-idp:ListUserPoolClients", + "cognito-idp:DescribeUserPoolClient", + "cognito-idp:DescribeResourceServer", + "bedrock-agentcore:CreateGateway", + "bedrock-agentcore:GetGateway", + "bedrock-agentcore:CreateGatewayTarget", + "bedrock-agentcore:ListGatewayTargets" + ], + "Resource": "*" +} +``` + +### Gateway Service Role (created by this script) + +```json +{ + "Effect": "Allow", + "Action": [ + "bedrock-agentcore:InvokeGateway", + "bedrock-agentcore:InvokeWebSearch" + ], + "Resource": [ + "arn:aws:bedrock-agentcore:us-east-1::gateway/*", + "arn:aws:bedrock-agentcore:us-east-1:aws:tool/web-search.v1" + ] +} +``` + +## Files + +| File | Description | +|:-----|:------------| +| `setup_gateway.py` | Main setup script — creates all resources | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py new file mode 100644 index 000000000..3ca062cd2 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py @@ -0,0 +1,351 @@ +""" +Set up AgentCore Gateway with Web Search Tool Target. + +Creates all required infrastructure for the Web Search Tool: + 1. IAM service role for the Gateway (with InvokeWebSearch permission) + 2. Cognito User Pool with client_credentials OAuth flow + 3. AgentCore Gateway with MCP protocol and JWT authorization + 4. Web Search Tool connector target + +After running this script, export the printed environment variables to use +with the other demos in this folder. + +Prerequisites: + pip install -r ../requirements.txt + AWS credentials with permissions to create IAM roles, Cognito pools, + and AgentCore Gateways. + +IAM permissions required: + iam:CreateRole, iam:PutRolePolicy, iam:GetRole + cognito-idp:CreateUserPool, cognito-idp:CreateUserPoolDomain + cognito-idp:CreateResourceServer, cognito-idp:CreateUserPoolClient + cognito-idp:ListUserPools, cognito-idp:ListUserPoolClients + cognito-idp:DescribeUserPoolClient, cognito-idp:DescribeResourceServer + bedrock-agentcore:CreateGateway, bedrock-agentcore:GetGateway + bedrock-agentcore:CreateGatewayTarget, bedrock-agentcore:ListGatewayTargets + +Usage: + python setup_gateway.py + python setup_gateway.py --gateway-name my-gateway + python setup_gateway.py --region us-east-1 +""" + +import argparse +import json +import os +import time + +import boto3 + +# ── Configuration ───────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + + +def wait_for_status(client, gateway_id, target_status="READY", max_wait=150): + """Poll gateway status until it reaches target_status.""" + for _ in range(max_wait // 5): + status = client.get_gateway(gatewayIdentifier=gateway_id)["status"] + if status == target_status: + return status + time.sleep(5) + return status + + +def wait_for_targets(client, gateway_id, max_wait=150): + """Poll until all gateway targets are READY.""" + for _ in range(max_wait // 5): + targets = client.list_gateway_targets(gatewayIdentifier=gateway_id) + if all(item["status"] == "READY" for item in targets["items"]): + return True + time.sleep(5) + return False + + +# ── Setup Steps ─────────────────────────────────────────────────────────────── + + +def create_gateway_role(iam_client, role_name, account_id, region): + """Create the IAM service role for the Gateway.""" + assume_role_policy = { + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Principal": {"Service": "bedrock-agentcore.amazonaws.com"}, + "Action": "sts:AssumeRole", + "Condition": { + "StringEquals": {"aws:SourceAccount": account_id}, + "ArnLike": { + "aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*" + }, + }, + } + ], + } + + try: + role_response = iam_client.create_role( + RoleName=role_name, + AssumeRolePolicyDocument=json.dumps(assume_role_policy), + ) + print(f" Created role: {role_name}") + time.sleep(10) # Wait for IAM propagation + except iam_client.exceptions.EntityAlreadyExistsException: + role_response = iam_client.get_role(RoleName=role_name) + print(f" Role already exists: {role_name}") + + role_arn = role_response["Role"]["Arn"] + + # Attach permissions + iam_client.put_role_policy( + RoleName=role_name, + PolicyName="WebSearchGatewayPolicy", + PolicyDocument=json.dumps( + { + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "InvokeGateway", + "Effect": "Allow", + "Action": "bedrock-agentcore:InvokeGateway", + "Resource": f"arn:aws:bedrock-agentcore:{region}:{account_id}:gateway/*", + }, + { + "Sid": "InvokeWebSearch", + "Effect": "Allow", + "Action": "bedrock-agentcore:InvokeWebSearch", + "Resource": f"arn:aws:bedrock-agentcore:{region}:aws:tool/web-search.v1", + }, + ], + } + ), + ) + print(" Permissions attached ✓") + return role_arn + + +def create_cognito_resources(cognito_client, region): + """Create Cognito User Pool, resource server, and M2M client.""" + pool_name = "agentcore-websearch-pool" + resource_server_id = "agentcore-websearch" + scopes = [{"ScopeName": "invoke", "ScopeDescription": "Invoke gateway"}] + scope_names = [f"{resource_server_id}/{s['ScopeName']}" for s in scopes] + + # Find or create user pool + user_pool_id = None + for pool in cognito_client.list_user_pools(MaxResults=60)["UserPools"]: + if pool["Name"] == pool_name: + user_pool_id = pool["Id"] + break + + if user_pool_id is None: + create_resp = cognito_client.create_user_pool(PoolName=pool_name) + user_pool_id = create_resp["UserPool"]["Id"] + domain = user_pool_id.replace("_", "").lower() + cognito_client.create_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + print(f" Created user pool: {user_pool_id}") + else: + print(f" User pool exists: {user_pool_id}") + + # Create resource server + try: + cognito_client.describe_resource_server( + UserPoolId=user_pool_id, Identifier=resource_server_id + ) + except cognito_client.exceptions.ResourceNotFoundException: + cognito_client.create_resource_server( + UserPoolId=user_pool_id, + Identifier=resource_server_id, + Name="WebSearch Gateway Resource Server", + Scopes=scopes, + ) + print(" Resource server ensured ✓") + + # Find or create M2M client + client_id, client_secret = None, None + for client in cognito_client.list_user_pool_clients( + UserPoolId=user_pool_id, MaxResults=60 + )["UserPoolClients"]: + if client["ClientName"] == "agentcore-websearch-client": + desc = cognito_client.describe_user_pool_client( + UserPoolId=user_pool_id, ClientId=client["ClientId"] + ) + client_id = client["ClientId"] + client_secret = desc["UserPoolClient"]["ClientSecret"] + break + + if client_id is None: + created = cognito_client.create_user_pool_client( + UserPoolId=user_pool_id, + ClientName="agentcore-websearch-client", + GenerateSecret=True, + AllowedOAuthFlows=["client_credentials"], + AllowedOAuthScopes=scope_names, + AllowedOAuthFlowsUserPoolClient=True, + SupportedIdentityProviders=["COGNITO"], + ExplicitAuthFlows=["ALLOW_REFRESH_TOKEN_AUTH"], + ) + client_id = created["UserPoolClient"]["ClientId"] + client_secret = created["UserPoolClient"]["ClientSecret"] + print(f" Created client: {client_id}") + else: + print(f" Client exists: {client_id}") + + domain = user_pool_id.replace("_", "").lower() + discovery_url = ( + f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}" + f"/.well-known/openid-configuration" + ) + scope_string = " ".join(scope_names) + + return { + "user_pool_id": user_pool_id, + "client_id": client_id, + "client_secret": client_secret, + "discovery_url": discovery_url, + "domain": domain, + "scope": scope_string, + } + + +def create_gateway(gateway_client, name, role_arn, cognito_config): + """Create the AgentCore Gateway with MCP protocol.""" + create_response = gateway_client.create_gateway( + name=name, + roleArn=role_arn, + protocolType="MCP", + protocolConfiguration={ + "mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"} + }, + authorizerType="CUSTOM_JWT", + authorizerConfiguration={ + "customJWTAuthorizer": { + "allowedClients": [cognito_config["client_id"]], + "discoveryUrl": cognito_config["discovery_url"], + } + }, + description="AgentCore Gateway with Web Search Tool", + ) + + gateway_id = create_response["gatewayId"] + gateway_url = create_response["gatewayUrl"] + print(f" Gateway ID: {gateway_id}") + print(f" Gateway URL: {gateway_url}") + + status = wait_for_status(gateway_client, gateway_id) + print(f" Gateway status: {status}") + + return gateway_id, gateway_url + + +def create_web_search_target(gateway_client, gateway_id): + """Create the Web Search Tool connector target.""" + target_response = gateway_client.create_gateway_target( + name="web-search-tool", + gatewayIdentifier=gateway_id, + targetConfiguration={ + "mcp": { + "connector": { + "source": {"connectorId": "web-search"}, + "configurations": [ + {"name": "WebSearch", "parameterValues": {}} + ], + } + } + }, + credentialProviderConfigurations=[ + {"credentialProviderType": "GATEWAY_IAM_ROLE"} + ], + ) + + target_id = target_response["targetId"] + print(f" Target ID: {target_id}") + + if wait_for_targets(gateway_client, gateway_id): + print(" Target status: READY ✓") + else: + print(" WARNING: Target did not reach READY state within timeout") + + return target_id + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Set up AgentCore Gateway with Web Search Tool target" + ) + parser.add_argument( + "--gateway-name", + default="web-search-gateway", + help="Name for the Gateway (default: web-search-gateway)", + ) + parser.add_argument( + "--region", + default=REGION, + help="AWS region (default: us-east-1)", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + region = args.region + + print("=" * 60) + print("AgentCore Web Search Tool — Gateway Setup") + print("=" * 60) + + # Get account ID + sts_client = boto3.client("sts", region_name=region) + account_id = sts_client.get_caller_identity()["Account"] + print(f"\nAccount: {account_id}") + print(f"Region: {region}") + + # Step 1: IAM Role + print("\n[1/4] Creating Gateway service role...") + iam_client = boto3.client("iam") + role_name = f"agentcore-{args.gateway_name}-role" + role_arn = create_gateway_role(iam_client, role_name, account_id, region) + + # Step 2: Cognito + print("\n[2/4] Setting up Cognito authentication...") + cognito_client = boto3.client("cognito-idp", region_name=region) + cognito_config = create_cognito_resources(cognito_client, region) + + # Step 3: Gateway + print("\n[3/4] Creating AgentCore Gateway...") + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + gateway_id, gateway_url = create_gateway( + gateway_client, args.gateway_name, role_arn, cognito_config + ) + + # Step 4: Web Search Target + print("\n[4/4] Creating Web Search Tool target...") + create_web_search_target(gateway_client, gateway_id) + + # Print environment variables for other demos + print("\n" + "=" * 60) + print("SETUP COMPLETE") + print("=" * 60) + print("\nExport these environment variables to use with other demos:\n") + print(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"") + print(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"") + print(f"export COGNITO_CLIENT_ID=\"{cognito_config['client_id']}\"") + print(f"export COGNITO_CLIENT_SECRET=\"{cognito_config['client_secret']}\"") + print(f"export COGNITO_SCOPE=\"{cognito_config['scope']}\"") + print(f"export AWS_DEFAULT_REGION=\"{region}\"") + print(f"\n# Gateway ID (for cleanup): {gateway_id}") + print(f"# IAM Role: {role_name}") + print(f"# Cognito Pool: {cognito_config['user_pool_id']}") + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md new file mode 100644 index 000000000..8d84d79d2 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -0,0 +1,90 @@ +# Raw MCP Tool Discovery and Invocation + +## Overview + +This demo calls the AgentCore Gateway directly using the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target are working correctly. + +``` +┌──────────────────┐ tools/list ┌─────────────────────────────────┐ +│ raw_mcp_call.py │ ──────────────▶ │ AgentCore Gateway │ +│ │ │ │ +│ MCPClient │ tools/call │ Target: web-search (connector) │ +│ (Streamable │ ──────────────▶ │ │ +│ HTTP) │◀────────────── │ → WebSearch results │ +└──────────────────┘ results └─────────────────────────────────┘ +``` + +## How It Works + +### Tool Discovery (`tools/list`) + +The MCP `tools/list` call returns all tools available on the Gateway. For a Gateway with only the Web Search connector target, you'll see one tool: + +```json +{ + "name": "WebSearch", + "description": "Search the web for current information", + "inputSchema": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query (max 200 characters)" + } + }, + "required": ["query"] + } +} +``` + +### Tool Invocation (`tools/call`) + +Calling the tool with a query returns structured results: + +```json +{ + "results": [ + { + "text": "Snippet from the web page...", + "url": "https://example.com/article", + "title": "Article Title", + "publishedDate": "2026-05-28" + } + ] +} +``` + +### Authentication Flow + +1. The script obtains an OAuth token from Cognito (`client_credentials` flow) +2. The token is passed as a `Bearer` header on the MCP Streamable HTTP transport +3. The Gateway validates the JWT and authorizes the request + +## Prerequisites + +```bash +pip install -r ../requirements.txt +``` + +Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. + +## Usage + +```bash +# Default query +python raw_mcp_call.py + +# Custom query +python raw_mcp_call.py --query "Latest Python release" +python raw_mcp_call.py --query "AWS re:Invent 2026 announcements" +``` + +## IAM Permissions + +No direct IAM permissions needed — authentication is handled via the Cognito OAuth token. The Gateway's service role provides the `bedrock-agentcore:InvokeWebSearch` permission. + +## Files + +| File | Description | +|:-----|:------------| +| `raw_mcp_call.py` | Main demo script — tool discovery and invocation | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py new file mode 100644 index 000000000..ff8c879b9 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py @@ -0,0 +1,124 @@ +""" +Raw MCP Tool Discovery and Invocation against AgentCore Gateway. + +Demonstrates direct MCP protocol calls without an agent framework: + 1. Connect to the Gateway using MCP Streamable HTTP transport + 2. Call tools/list to discover the WebSearch tool and its schema + 3. Call tools/call to invoke WebSearch with a test query + 4. Display the structured results + +This is useful for verifying your Gateway setup before integrating +with an agent framework. + +Prerequisites: + pip install -r ../requirements.txt + Export environment variables from 01-setup-gateway/setup_gateway.py + +Environment variables required: + AGENTCORE_GATEWAY_URL — Gateway MCP endpoint + COGNITO_DOMAIN — Cognito domain prefix + COGNITO_CLIENT_ID — Cognito app client ID + COGNITO_CLIENT_SECRET — Cognito app client secret + COGNITO_SCOPE — OAuth scope string + +IAM permissions required: + bedrock-agentcore:InvokeGateway (via OAuth token) + +Usage: + python raw_mcp_call.py + python raw_mcp_call.py --query "Latest Python release" +""" + +import argparse +import json +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.gateway_auth import create_streamable_http_transport + +from strands.tools.mcp.mcp_client import MCPClient + +# ── Configuration ───────────────────────────────────────────────────────────── + +DEFAULT_QUERY = "What is today's news around the world?" + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Raw MCP tool discovery and invocation against AgentCore Gateway" + ) + parser.add_argument( + "--query", + default=DEFAULT_QUERY, + help=f"Search query (default: '{DEFAULT_QUERY}')", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + + print("=" * 60) + print("AgentCore Web Search Tool — Raw MCP Calls") + print("=" * 60) + + # Create MCP client + transport_factory = create_streamable_http_transport() + mcp_client = MCPClient(transport_factory) + + with mcp_client: + # Step 1: Discover tools + print("\n[1] Discovering tools (tools/list)...\n") + tools = mcp_client.list_tools_sync() + print(f" Found {len(tools)} tool(s):\n") + + for tool in tools: + spec = tool.tool_spec + print(f" Name: {spec['name']}") + print(f" Description: {spec.get('description', 'N/A')}") + schema_str = json.dumps(spec.get("inputSchema", {}), indent=2) + # Truncate long schemas for readability + if len(schema_str) > 200: + schema_str = schema_str[:200] + "..." + print(f" Input: {schema_str}") + print() + + # Step 2: Invoke WebSearch + print("=" * 60) + print(f"[2] Calling WebSearch: '{args.query}'") + print("=" * 60 + "\n") + + # Find the WebSearch tool + ws_tools = [t for t in tools if "WebSearch" in t.tool_name] + if not ws_tools: + print(" ERROR: WebSearch tool not found. Check your Gateway target.") + return + + ws_tool_name = ws_tools[0].tool_name + result = mcp_client.call_tool_sync( + "raw-mcp-demo", ws_tool_name, {"query": args.query} + ) + + # Step 3: Display results + print("[3] Results:\n") + for content in result.get("content", result if isinstance(result, list) else [result]): + if isinstance(content, dict) and "text" in content: + try: + parsed = json.loads(content["text"]) + print(json.dumps(parsed, indent=2)) + except (ValueError, TypeError): + print(content["text"][:2000]) + else: + print(content) + + print("\n" + "=" * 60) + print("Demo complete!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md new file mode 100644 index 000000000..40a829661 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md @@ -0,0 +1,97 @@ +# Web Search with a Strands AI Agent + +## Overview + +This demo shows the complete agent integration: a Strands agent automatically discovers and invokes the Web Search Tool to answer real-time questions with cited sources. + +``` +┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ +│ User │ ─────────────────────────────▶ │ Strands Agent │ +│ │◀───────────────────────────── │ (Claude Sonnet 4) │ +│ │ "Here's what I found: [...]" │ │ +└────────────┘ │ tools: [WebSearch] │ + │ │ │ + └───────┼──────────────────┘ + │ MCP tools/call + ▼ + ┌──────────────────────────┐ + │ AgentCore Gateway │ + │ → Web Search Connector │ + │ → Structured results │ + └──────────────────────────┘ +``` + +## How It Works + +### Tool Discovery + +The agent connects to the Gateway via MCP Streamable HTTP and calls `tools/list`. The Gateway returns the `WebSearch` tool with its input schema. Strands automatically registers it as an available tool for the LLM. + +### Agent Loop + +When you ask a question: + +1. Strands sends the query + tool schema to Claude Sonnet 4 +2. Claude decides to call `WebSearch` with a concise search query +3. Strands invokes the tool via MCP `tools/call` on the Gateway +4. The Gateway routes to the Web Search connector and returns results +5. Results are fed back to Claude as a tool result +6. Claude synthesizes a final answer with cited sources + +### Shared Agent (`utils/web_search_agent.py`) + +The agent factory handles MCP client setup and tool discovery: + +```python +from utils.web_search_agent import create_agent, create_mcp_client + +mcp_client = create_mcp_client() + +with mcp_client: + agent = create_agent(mcp_client) + result = agent("What is the latest version of Python?") + print(result.message) +``` + +The `create_mcp_client()` function handles OAuth token retrieval and transport creation. The `create_agent()` function discovers tools and configures the Strands agent with an appropriate system prompt. + +## Prerequisites + +```bash +pip install -r ../requirements.txt +``` + +Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. + +Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1. + +## Usage + +```bash +# Run default demo queries +python web_search_strands.py + +# Custom query +python web_search_strands.py --query "What are the latest AI announcements?" +python web_search_strands.py --query "Current price of Bitcoin" +``` + +## IAM Permissions + +```json +{ + "Effect": "Allow", + "Action": "bedrock:InvokeModel", + "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-20250514-v1:0" +} +``` + +Gateway invocation is authorized via the Cognito OAuth token — no additional IAM permissions needed for the caller. + +## Files + +| File | Description | +|:-----|:------------| +| `web_search_strands.py` | Main demo script — Strands agent with web search | +| `../utils/web_search_agent.py` | Shared agent factory with MCP client setup | +| `../utils/gateway_auth.py` | OAuth token retrieval and transport creation | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py new file mode 100644 index 000000000..a382e930a --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py @@ -0,0 +1,109 @@ +""" +Web Search with a Strands AI Agent. + +Demonstrates a Strands agent that uses the Web Search Tool via AgentCore +Gateway to answer real-time questions: + 1. Connect to the Gateway using MCP Streamable HTTP transport + 2. Discover the WebSearch tool via tools/list + 3. Create a Strands agent with the discovered tools + 4. Ask a question — the agent invokes WebSearch automatically + 5. Get a grounded response with cited sources + +Prerequisites: + pip install -r ../requirements.txt + Export environment variables from 01-setup-gateway/setup_gateway.py + Access to Claude Sonnet 4 in us-east-1 + +Environment variables required: + AGENTCORE_GATEWAY_URL — Gateway MCP endpoint + COGNITO_DOMAIN — Cognito domain prefix + COGNITO_CLIENT_ID — Cognito app client ID + COGNITO_CLIENT_SECRET — Cognito app client secret + COGNITO_SCOPE — OAuth scope string + +IAM permissions required: + bedrock:InvokeModel (for Claude Sonnet 4) + +Usage: + python web_search_strands.py + python web_search_strands.py --query "What are the latest AI announcements?" +""" + +import argparse +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.web_search_agent import create_agent, create_mcp_client + +# ── Configuration ───────────────────────────────────────────────────────────── + +DEFAULT_QUERIES = [ + "What is today's news around the world?", + "What are the latest developments in quantum computing?", +] + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def run_query(agent, query: str): + """Run a single query through the agent and print the response.""" + print(f"\n{'=' * 60}") + print(f"Query: {query}") + print("=" * 60) + + response = agent(query) + + print("\n[Agent Response]") + print("-" * 60) + if hasattr(response, "message"): + content = response.message.get("content", []) + for block in content: + if block.get("text"): + print(block["text"]) + else: + print(str(response)) + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Strands agent with Web Search Tool via AgentCore Gateway" + ) + parser.add_argument( + "--query", + default=None, + help="Custom query (runs default queries if omitted)", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + + print("=" * 60) + print("AgentCore Web Search Tool — Strands Agent") + print("=" * 60) + + mcp_client = create_mcp_client() + + with mcp_client: + # Discover tools + tools = mcp_client.list_tools_sync() + print(f"\nDiscovered {len(tools)} tool(s) from Gateway") + + # Create agent + agent = create_agent(mcp_client) + + # Run queries + queries = [args.query] if args.query else DEFAULT_QUERIES + for q in queries: + run_query(agent, q) + + print("\n" + "=" * 60) + print("Demo complete!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md new file mode 100644 index 000000000..e289f69b5 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md @@ -0,0 +1,99 @@ +# Web Search with a LangChain Agent + +## Overview + +This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. + +``` +┌────────────┐ "Latest AWS announcements?" ┌──────────────────────────┐ +│ User │ ─────────────────────────────▶ │ LangChain Agent │ +│ │◀───────────────────────────── │ (ChatBedrockConverse) │ +│ │ "Here's what I found: [...]" │ │ +└────────────┘ │ tools from MCP client │ + │ │ │ + └───────┼──────────────────┘ + │ MultiServerMCPClient + ▼ + ┌──────────────────────────┐ + │ AgentCore Gateway │ + │ → Web Search Connector │ + └──────────────────────────┘ +``` + +## How It Works + +### MCP Client Setup + +`langchain-mcp-adapters` provides `MultiServerMCPClient` which connects to one or more MCP servers and converts their tools into LangChain-compatible tool objects: + +```python +from langchain_mcp_adapters.client import MultiServerMCPClient + +async with MultiServerMCPClient({ + "web-search": { + "transport": "streamable_http", + "url": gateway_url, + "headers": {"Authorization": f"Bearer {token}"}, + } +}) as client: + tools = client.get_tools() + # tools is a list of LangChain Tool objects +``` + +### Agent Creation + +The agent uses LangGraph's `create_react_agent` with `ChatBedrockConverse` as the LLM: + +```python +from langchain_aws import ChatBedrockConverse +from langgraph.prebuilt import create_react_agent + +model = ChatBedrockConverse( + model="us.anthropic.claude-sonnet-4-20250514-v1:0", + region_name="us-east-1", +) +agent = create_react_agent(model, tools=tools) +result = await agent.ainvoke({"messages": [{"role": "user", "content": query}]}) +``` + +### Async Execution + +LangChain's MCP adapter uses async I/O. The demo wraps the agent call in `asyncio.run()` for CLI usage. + +## Prerequisites + +```bash +pip install -r ../requirements.txt +``` + +Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. + +Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1. + +## Usage + +```bash +# Default query +python web_search_langchain.py + +# Custom query +python web_search_langchain.py --query "Latest AWS announcements" +python web_search_langchain.py --query "Python 3.13 new features" +``` + +## IAM Permissions + +```json +{ + "Effect": "Allow", + "Action": "bedrock:InvokeModel", + "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-20250514-v1:0" +} +``` + +## Files + +| File | Description | +|:-----|:------------| +| `web_search_langchain.py` | Main demo script — LangChain agent with web search | +| `../utils/gateway_auth.py` | OAuth token retrieval (shared with other demos) | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py new file mode 100644 index 000000000..4a18bd4cb --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py @@ -0,0 +1,131 @@ +""" +Web Search with a LangChain Agent. + +Demonstrates a LangChain agent that uses the Web Search Tool via AgentCore +Gateway to answer real-time questions: + 1. Connect to the Gateway using langchain-mcp-adapters MultiServerMCPClient + 2. Discover tools from the Gateway + 3. Create a LangChain agent with ChatBedrockConverse + 4. Ask a question — the agent invokes WebSearch automatically + 5. Get a grounded response with cited sources + +Prerequisites: + pip install -r ../requirements.txt + Export environment variables from 01-setup-gateway/setup_gateway.py + Access to Claude Sonnet 4 in us-east-1 + +Environment variables required: + AGENTCORE_GATEWAY_URL — Gateway MCP endpoint + COGNITO_DOMAIN — Cognito domain prefix + COGNITO_CLIENT_ID — Cognito app client ID + COGNITO_CLIENT_SECRET — Cognito app client secret + COGNITO_SCOPE — OAuth scope string + +IAM permissions required: + bedrock:InvokeModel (for Claude Sonnet 4) + +Usage: + python web_search_langchain.py + python web_search_langchain.py --query "Latest AWS announcements" +""" + +import argparse +import asyncio +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.gateway_auth import get_oauth_token + +from langchain_aws import ChatBedrockConverse +from langchain_mcp_adapters.client import MultiServerMCPClient +from langgraph.prebuilt import create_react_agent + +# ── Configuration ───────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") +MODEL_ID = os.getenv( + "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-20250514-v1:0" +) +GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_URL", "") + +DEFAULT_QUERY = "What is today's news around the world?" + + +# ── Agent ───────────────────────────────────────────────────────────────────── + + +async def run_agent(query: str): + """Create and run a LangChain agent with Web Search tools.""" + # Get OAuth token for Gateway authentication + token = get_oauth_token() + + # Configure the LLM + model = ChatBedrockConverse( + model=MODEL_ID, + region_name=REGION, + temperature=0.7, + max_tokens=1024, + ) + + # Connect to the Gateway as an MCP client + async with MultiServerMCPClient( + { + "web-search": { + "transport": "streamable_http", + "url": GATEWAY_URL, + "headers": {"Authorization": f"Bearer {token}"}, + } + } + ) as client: + tools = client.get_tools() + print(f" Discovered {len(tools)} tool(s)") + + # Create and run the agent + agent = create_react_agent(model, tools=tools) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": query}]} + ) + + # Print the final response + print("\n[Agent Response]") + print("-" * 60) + final_message = result["messages"][-1] + if hasattr(final_message, "content"): + print(final_message.content) + else: + print(str(final_message)) + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def parse_args(): + parser = argparse.ArgumentParser( + description="LangChain agent with Web Search Tool via AgentCore Gateway" + ) + parser.add_argument( + "--query", + default=DEFAULT_QUERY, + help=f"Search query (default: '{DEFAULT_QUERY}')", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + + print("=" * 60) + print("AgentCore Web Search Tool — LangChain Agent") + print("=" * 60) + print(f"\nQuery: {args.query}\n") + + asyncio.run(run_agent(args.query)) + + print("\n" + "=" * 60) + print("Demo complete!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md new file mode 100644 index 000000000..5d3ad7ae8 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md @@ -0,0 +1,22 @@ +# Resource Cleanup + +## Overview + +Deletes all AWS resources created by the setup script: Gateway targets, Gateway, Cognito User Pool, and IAM role. + +## Usage + +Use the resource IDs printed by `01-setup-gateway/setup_gateway.py`: + +```bash +python cleanup.py \ + --gateway-id gw-abc123def456 \ + --user-pool-id us-east-1_AbCdEfGh \ + --role-name agentcore-web-search-gateway-role +``` + +## Files + +| File | Description | +|:-----|:------------| +| `cleanup.py` | Deletes Gateway, Cognito, and IAM resources | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py new file mode 100644 index 000000000..82119344a --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py @@ -0,0 +1,132 @@ +""" +Clean up all resources created by the Web Search Tool setup. + +Deletes in order: + 1. Gateway targets and Gateway + 2. Cognito User Pool (domain, clients, pool) + 3. IAM Role and inline policies + +Prerequisites: + pip install -r ../requirements.txt + AWS credentials with permissions to delete the resources. + +Usage: + python cleanup.py --gateway-id --user-pool-id --role-name + python cleanup.py --gateway-id gw-abc123 --user-pool-id us-east-1_AbCdEf --role-name agentcore-web-search-gateway-role +""" + +import argparse +import os +import time + +import boto3 + +# ── Configuration ───────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") + + +# ── Cleanup Steps ───────────────────────────────────────────────────────────── + + +def delete_gateway(gateway_client, gateway_id): + """Delete all targets and the gateway itself.""" + print("\n[1/3] Deleting Gateway resources...") + try: + targets = gateway_client.list_gateway_targets( + gatewayIdentifier=gateway_id, maxResults=100 + ) + for item in targets["items"]: + gateway_client.delete_gateway_target( + gatewayIdentifier=gateway_id, targetId=item["targetId"] + ) + print(f" Deleted target: {item['name']}") + + time.sleep(10) + gateway_client.delete_gateway(gatewayIdentifier=gateway_id) + print(f" Deleted gateway: {gateway_id}") + except Exception as e: + print(f" Error deleting gateway: {e}") + + +def delete_cognito(cognito_client, user_pool_id): + """Delete the Cognito User Pool and its domain.""" + print("\n[2/3] Deleting Cognito resources...") + try: + domain = user_pool_id.replace("_", "").lower() + cognito_client.delete_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + cognito_client.delete_user_pool(UserPoolId=user_pool_id) + print(f" Deleted user pool: {user_pool_id}") + except Exception as e: + print(f" Error deleting Cognito: {e}") + + +def delete_iam_role(iam_client, role_name): + """Delete the IAM role and its inline policies.""" + print("\n[3/3] Deleting IAM resources...") + try: + # Delete inline policies + policies = iam_client.list_role_policies(RoleName=role_name) + for policy_name in policies["PolicyNames"]: + iam_client.delete_role_policy( + RoleName=role_name, PolicyName=policy_name + ) + print(f" Deleted policy: {policy_name}") + + iam_client.delete_role(RoleName=role_name) + print(f" Deleted role: {role_name}") + except Exception as e: + print(f" Error deleting IAM role: {e}") + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Clean up Web Search Tool Gateway resources" + ) + parser.add_argument( + "--gateway-id", required=True, help="Gateway ID to delete" + ) + parser.add_argument( + "--user-pool-id", required=True, help="Cognito User Pool ID to delete" + ) + parser.add_argument( + "--role-name", required=True, help="IAM role name to delete" + ) + parser.add_argument( + "--region", default=REGION, help="AWS region (default: us-east-1)" + ) + return parser.parse_args() + + +def main(): + args = parse_args() + region = args.region + + print("=" * 60) + print("AgentCore Web Search Tool — Resource Cleanup") + print("=" * 60) + print(f"\nRegion: {region}") + print(f"Gateway ID: {args.gateway_id}") + print(f"User Pool ID: {args.user_pool_id}") + print(f"Role Name: {args.role_name}") + + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + cognito_client = boto3.client("cognito-idp", region_name=region) + iam_client = boto3.client("iam") + + delete_gateway(gateway_client, args.gateway_id) + delete_cognito(cognito_client, args.user_pool_id) + delete_iam_role(iam_client, args.role_name) + + print("\n" + "=" * 60) + print("✅ All resources cleaned up successfully!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md new file mode 100644 index 000000000..5eb7e691e --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -0,0 +1,188 @@ +# AgentCore Web Search Tool + +## Overview + +[Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. + +![Web Search Tool Architecture](images/tutorial-architecture.png) + +## How the Web Search Tool Works + +### Gateway + Connector Architecture + +The Web Search Tool uses the **connector** target type — a fully AWS-managed integration that requires no schema, no endpoint configuration, and no outbound credential setup. You specify `connectorId: "web-search"` and the Gateway handles everything else. + +``` +┌─────────────┐ OAuth Token ┌──────────────────────────────────┐ +│ Your Agent │ ───────────────▶ │ AgentCore Gateway (MCP) │ +│ (Strands, │ │ │ +│ LangChain, │ tools/list │ ┌────────────────────────────┐ │ +│ or any MCP │ ───────────────▶ │ │ Web Search Connector │ │ +│ client) │ │ │ connectorId: "web-search" │ │ +│ │ tools/call │ │ │ │ +│ │ ───────────────▶ │ │ → Real-time web results │ │ +│ │◀─────────────── │ └────────────────────────────┘ │ +└─────────────┘ results └──────────────────────────────────┘ +``` + +### Key Capabilities + +- **Real-time information access** — Retrieve current web results with titles, URLs, snippets, and publication dates +- **Zero infrastructure management** — No search APIs to provision or scaling to configure +- **Framework agnostic** — Works with Strands Agents, LangChain, LangGraph, CrewAI, or any MCP-compatible client +- **Structured results** — Results returned in both MCP `content` (text) and `structuredContent` (typed JSON) formats + +### How It Works + +1. **Gateway setup** — Create an AgentCore Gateway and add a Web Search Tool target using `connectorId: "web-search"` +2. **Tool discovery** — Your agent calls `tools/list` on the Gateway endpoint and discovers `WebSearch` with its input schema +3. **Search invocation** — Your agent calls `tools/call` with a natural language query (up to 200 characters) +4. **Structured results** — The tool returns results with text snippets, URLs, titles, and publication dates +5. **Grounded response** — Your agent uses the results to compose a response with cited sources + +### Response Format + +```json +{ + "results": [ + { + "text": "Snippet from the web page...", + "url": "https://example.com/article", + "title": "Article Title", + "publishedDate": "2026-05-28" + } + ] +} +``` + +| Field | Type | Required | Description | +|:------|:-----|:---------|:------------| +| `text` | string | Yes | Text content or snippet of the search result | +| `url` | string | No | URL of the source webpage | +| `title` | string | No | Title of the source webpage | +| `publishedDate` | string | No | Publication date of the result | + +> **Note:** Queries longer than 200 characters may not return results. Keep queries concise. + +### Authentication + +- **Inbound**: Amazon Cognito with `client_credentials` OAuth flow (can use other OAuth providers) +- **Outbound**: Automatic — the Gateway uses its own IAM role to authenticate to the Web Search backend + +## Demos + +| Folder | Framework | What You'll Learn | +|:-------|:----------|:------------------| +| [01-setup-gateway/](01-setup-gateway/) | boto3 (SDK) | Create IAM role, Cognito, Gateway, and Web Search target | +| [02-raw-mcp/](02-raw-mcp/) | MCP protocol | Direct tool discovery and invocation without an agent | +| [03-strands-agent/](03-strands-agent/) | Strands Agents | Full agent loop with automatic tool selection and cited responses | +| [04-langchain-agent/](04-langchain-agent/) | LangChain + LangGraph | Same integration using LangChain's MCP adapter | +| [05-cleanup/](05-cleanup/) | boto3 (SDK) | Delete all resources created by the setup | + +## Quick Start + +```bash +pip install -r requirements.txt + +# Step 1: Create Gateway and Web Search target +python 01-setup-gateway/setup_gateway.py + +# Step 2: Export the environment variables printed by setup +export AGENTCORE_GATEWAY_URL="..." +export COGNITO_DOMAIN="..." +export COGNITO_CLIENT_ID="..." +export COGNITO_CLIENT_SECRET="..." +export COGNITO_SCOPE="agentcore-websearch/invoke" +export AWS_DEFAULT_REGION="us-east-1" + +# Step 3: Verify with raw MCP calls +python 02-raw-mcp/raw_mcp_call.py + +# Step 4: Run with Strands agent +python 03-strands-agent/web_search_strands.py + +# Step 5: Run with LangChain agent +python 04-langchain-agent/web_search_langchain.py + +# Cleanup when done +python 05-cleanup/cleanup.py --gateway-id --user-pool-id --role-name +``` + +## Shared Utilities + +Demos 02–04 share authentication and agent utilities in `utils/`: + +```python +from utils.gateway_auth import get_oauth_token, create_streamable_http_transport +from utils.web_search_agent import create_agent, create_mcp_client +``` + +- `gateway_auth.py` — OAuth token retrieval from Cognito and MCP transport factory +- `web_search_agent.py` — Strands agent factory with Web Search tools + +## IAM Permissions + +### Caller (setup script) + +```json +{ + "Effect": "Allow", + "Action": [ + "iam:CreateRole", + "iam:PutRolePolicy", + "iam:GetRole", + "cognito-idp:CreateUserPool", + "cognito-idp:CreateUserPoolDomain", + "cognito-idp:CreateResourceServer", + "cognito-idp:CreateUserPoolClient", + "bedrock-agentcore:CreateGateway", + "bedrock-agentcore:GetGateway", + "bedrock-agentcore:CreateGatewayTarget", + "bedrock-agentcore:ListGatewayTargets" + ], + "Resource": "*" +} +``` + +### Caller (agent demos) + +```json +{ + "Effect": "Allow", + "Action": "bedrock:InvokeModel", + "Resource": "*" +} +``` + +### Gateway Service Role (created by setup) + +```json +{ + "Effect": "Allow", + "Action": [ + "bedrock-agentcore:InvokeGateway", + "bedrock-agentcore:InvokeWebSearch" + ], + "Resource": [ + "arn:aws:bedrock-agentcore:us-east-1::gateway/*", + "arn:aws:bedrock-agentcore:us-east-1:aws:tool/web-search.v1" + ] +} +``` + +## Region Availability + +The Web Search Tool connector is currently available in **us-east-1** only. + +## Files + +| File | Description | +|:-----|:------------| +| `requirements.txt` | Python dependencies for all sub-demos | +| `utils/gateway_auth.py` | Shared OAuth and MCP transport utilities | +| `utils/web_search_agent.py` | Shared Strands agent factory | +| `01-setup-gateway/` | Gateway and target creation script | +| `02-raw-mcp/` | Direct MCP protocol demo | +| `03-strands-agent/` | Strands agent demo | +| `04-langchain-agent/` | LangChain agent demo | +| `05-cleanup/` | Resource deletion script | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/images/inbound-and-outbound-auth.png b/01-features/03-connect-your-agent-to-anything/03-web-search/images/inbound-and-outbound-auth.png new file mode 100644 index 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a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/__init__.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py new file mode 100644 index 000000000..214b2ef0f --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py @@ -0,0 +1,104 @@ +""" +Shared Gateway authentication utilities for Web Search Tool demos. + +Provides helpers for: + - Obtaining OAuth tokens from Cognito (client_credentials flow) + - Creating MCP Streamable HTTP transports authenticated against the Gateway + +All demos in this folder share these utilities to avoid duplicating +auth boilerplate. +""" + +import os + +import requests +from mcp.client.streamable_http import streamablehttp_client + + +# ── Configuration ───────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") +GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_URL", "") +COGNITO_DOMAIN = os.getenv("COGNITO_DOMAIN", "") +COGNITO_CLIENT_ID = os.getenv("COGNITO_CLIENT_ID", "") +COGNITO_CLIENT_SECRET = os.getenv("COGNITO_CLIENT_SECRET", "") +COGNITO_SCOPE = os.getenv("COGNITO_SCOPE", "") + + +# ── Token retrieval ─────────────────────────────────────────────────────────── + + +def get_oauth_token( + domain: str = "", + client_id: str = "", + client_secret: str = "", + scope: str = "", + region: str = "", +) -> str: + """Retrieve a fresh OAuth token from Cognito using client_credentials flow. + + Args: + domain: Cognito domain prefix (e.g., "us-east-1abcdef12"). + client_id: Cognito app client ID. + client_secret: Cognito app client secret. + scope: OAuth scope string (e.g., "agentcore-websearch/invoke"). + region: AWS region for the Cognito endpoint. + + Returns: + Access token string. + """ + domain = domain or COGNITO_DOMAIN + client_id = client_id or COGNITO_CLIENT_ID + client_secret = client_secret or COGNITO_CLIENT_SECRET + scope = scope or COGNITO_SCOPE + region = region or REGION + + if not all([domain, client_id, client_secret]): + raise ValueError( + "Cognito credentials not configured. Set COGNITO_DOMAIN, " + "COGNITO_CLIENT_ID, and COGNITO_CLIENT_SECRET environment variables." + ) + + url = f"https://{domain}.auth.{region}.amazoncognito.com/oauth2/token" + response = requests.post( + url, + headers={"Content-Type": "application/x-www-form-urlencoded"}, + data={ + "grant_type": "client_credentials", + "client_id": client_id, + "client_secret": client_secret, + "scope": scope, + }, + ) + response.raise_for_status() + return response.json()["access_token"] + + +# ── MCP transport factory ───────────────────────────────────────────────────── + + +def create_streamable_http_transport(gateway_url: str = "", **token_kwargs): + """Create an MCP Streamable HTTP transport authenticated with a Bearer token. + + Args: + gateway_url: The AgentCore Gateway MCP endpoint URL. + **token_kwargs: Passed to get_oauth_token() for credential overrides. + + Returns: + A callable suitable for MCPClient initialization. + """ + gateway_url = gateway_url or GATEWAY_URL + if not gateway_url: + raise ValueError( + "Gateway URL not configured. Set AGENTCORE_GATEWAY_URL environment variable." + ) + + token = get_oauth_token(**token_kwargs) + + def _transport(): + return streamablehttp_client( + gateway_url, + headers={"Authorization": f"Bearer {token}"}, + ) + + return _transport diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py new file mode 100644 index 000000000..f656cd315 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py @@ -0,0 +1,80 @@ +""" +Shared Strands agent using AgentCore Web Search Tool via Gateway. + +Used as the common demo agent across web search sub-demos: + - 02-strands-agent/web_search_strands.py + +The agent connects to an AgentCore Gateway that exposes the Web Search Tool +as an MCP-compliant connector target. Tools are discovered dynamically via +the MCP tools/list endpoint. +""" + +import os + +from strands import Agent +from strands.models import BedrockModel +from strands.tools.mcp.mcp_client import MCPClient + +from utils.gateway_auth import create_streamable_http_transport + +# ── Configuration ───────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") +MODEL_ID = os.getenv( + "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-20250514-v1:0" +) + +SYSTEM_PROMPT = """You are a helpful research assistant with access to real-time web search. + +PRINCIPLES: +- Use the WebSearch tool to find current information when answering questions +- Always cite your sources with URLs when providing information from search results +- If search results are insufficient, say so rather than guessing +- Keep queries concise (under 200 characters) for best results +- Synthesize information from multiple results when possible + +RESPONSE FORMAT: +- Provide clear, well-structured answers +- Include source URLs for verification +- Note the publication date of sources when available +""" + + +# ── Factory ──────────────────────────────────────────────────────────────────── + + +def create_mcp_client(gateway_url: str = "", **token_kwargs) -> MCPClient: + """Create an MCPClient connected to the AgentCore Gateway. + + Args: + gateway_url: The Gateway MCP endpoint URL. + **token_kwargs: Passed to gateway_auth for credential overrides. + + Returns: + MCPClient instance (must be used as a context manager). + """ + transport_factory = create_streamable_http_transport( + gateway_url=gateway_url, **token_kwargs + ) + return MCPClient(transport_factory) + + +def create_agent(mcp_client: MCPClient) -> Agent: + """Create a Strands agent with Web Search tools from the Gateway. + + The mcp_client must already be entered as a context manager (i.e., + call this inside a `with mcp_client:` block). + + Args: + mcp_client: An active MCPClient connected to the Gateway. + + Returns: + Strands Agent configured with discovered tools. + """ + tools = mcp_client.list_tools_sync() + model = BedrockModel(model_id=MODEL_ID, region_name=REGION) + return Agent( + model=model, + tools=tools, + system_prompt=SYSTEM_PROMPT, + ) diff --git a/01-features/03-connect-your-agent-to-anything/README.md b/01-features/03-connect-your-agent-to-anything/README.md index ac9a9f94f..2a5db4e85 100644 --- a/01-features/03-connect-your-agent-to-anything/README.md +++ b/01-features/03-connect-your-agent-to-anything/README.md @@ -1,6 +1,6 @@ # Connect Your Agent to Anything -Give your agents access to powerful built-in tool environments — sandboxed code execution and headless browser automation — managed and scaled by Amazon Bedrock AgentCore. +Give your agents access to powerful built-in tool environments — sandboxed code execution, headless browser automation, and real-time web search — managed and scaled by Amazon Bedrock AgentCore. ## Top-level layout @@ -8,10 +8,11 @@ Give your agents access to powerful built-in tool environments — sandboxed cod |:-------|:--------------| | [`01-code-interpreter/`](./01-code-interpreter/) | Sandboxed Python execution environment — run code, execute shell commands, upload and read files, use the AWS CLI, all in an isolated per-session sandbox | | [`02-browser/`](./02-browser/) | Fully managed headless Chromium browser — drive it with Nova Act, Browser-Use, Strands, or raw Playwright via the Chrome DevTools Protocol | +| [`03-web-search/`](./03-web-search/) | Real-time web search as an MCP-compliant tool — ground your agents in current information via AgentCore Gateway with zero infrastructure to manage | ## How these tools work -Both tools follow the same pattern: AgentCore provisions an isolated sandbox session on demand, your agent calls tool APIs within that session, and the session terminates when you stop it. No infrastructure to manage. +Code Interpreter and Browser follow the same pattern: AgentCore provisions an isolated sandbox session on demand, your agent calls tool APIs within that session, and the session terminates when you stop it. Web Search uses a different pattern — it's exposed as an MCP-compliant connector through AgentCore Gateway, so your agent discovers and invokes it via standard MCP protocol calls. All three require no infrastructure to manage. ### Code Interpreter @@ -44,6 +45,24 @@ with browser_session("us-west-2") as client: # Pass ws_url + headers to Nova Act, Browser-Use, Playwright, or Strands ``` +### Web Search Tool + +- **What it is**: A fully managed web search connector exposed through AgentCore Gateway via MCP +- **Use it for**: Agents that need real-time information — current events, latest releases, fact-checking, competitive intelligence +- **Entry point**: Create a Gateway with `connectorId: "web-search"`, then connect any MCP client + +```python +from strands.tools.mcp.mcp_client import MCPClient +from mcp.client.streamable_http import streamablehttp_client + +transport = lambda: streamablehttp_client(gateway_url, headers={"Authorization": f"Bearer {token}"}) +mcp_client = MCPClient(transport) + +with mcp_client: + tools = mcp_client.list_tools_sync() # Discovers WebSearch + result = mcp_client.call_tool_sync("demo", "WebSearch", {"query": "latest AI news"}) +``` + ## Quick Start ```bash @@ -57,10 +76,17 @@ playwright install chromium python 02-browser/01-nova-act/getting_started.py \ --nova-act-key $NOVA_ACT_API_KEY \ --prompt "Search Amazon for MacBooks" + +# Web Search Tool +pip install -r 03-web-search/requirements.txt +python 03-web-search/01-setup-gateway/setup_gateway.py +# Export the printed env vars, then: +python 03-web-search/03-strands-agent/web_search_strands.py ``` ## Resources - [Code Interpreter — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/code-interpreter-overview.html) - [Browser Tool — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/browser-tool-overview.html) +- [AgentCore Gateway — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) - [boto3 Data Plane Reference (`bedrock-agentcore`)](https://docs.aws.amazon.com/boto3/latest/reference/services/bedrock-agentcore.html) From 943136dd040b92235a87cc2db7f72cf76d3e8340 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Wed, 3 Jun 2026 00:34:26 -0400 Subject: [PATCH 02/21] Add AgentCore Web Search Tool samples and workshop content - 01-features: new 03-web-search folder with setup, raw MCP, Strands, and LangChain samples; updated requirements.txt with pinned versions - 06-workshops: new 03-Agent-Core-web-search workshop with 6 notebooks covering gateway setup, Strands agent, LangChain agent, CVE scanner, earnings brief, and iterative research pattern - 06-workshops/05-AgentCore-tools/README.md: added Web Search Tool section --- .../03-web-search/01-setup-gateway/README.md | 10 + .../03-web-search/02-raw-mcp/README.md | 2 +- .../03-web-search/02-raw-mcp/raw_mcp_call.py | 2 +- .../03-strands-agent/web_search_strands.py | 2 +- .../web_search_langchain.py | 46 +- .../03-web-search/README.md | 24 +- .../03-web-search/requirements.txt | 9 +- .../03-web-search/utils/web_search_agent.py | 12 +- .../01-web-search-gateway-setup.ipynb | 603 ++++++++++++++++++ .../01-gateway-setup-and-raw-mcp/README.md | 61 ++ .../images/.gitkeep | 0 .../requirements.txt | 4 + .../02-web-search-strands-agent.ipynb | 305 +++++++++ .../02-web-search-with-strands/README.md | 51 ++ .../images/.gitkeep | 0 .../requirements.txt | 4 + .../03-web-search-langchain-agent.ipynb | 279 ++++++++ .../03-web-search-with-langchain/README.md | 51 ++ .../images/.gitkeep | 0 .../requirements.txt | 7 + .../01-cve-scanner/01-cve-scanner.ipynb | 303 +++++++++ .../01-cve-scanner/README.md | 62 ++ .../01-cve-scanner/images/.gitkeep | 0 .../01-cve-scanner/requirements.txt | 4 + .../02-earnings-brief/02-earnings-brief.ipynb | 318 +++++++++ .../02-earnings-brief/README.md | 63 ++ .../02-earnings-brief/images/.gitkeep | 0 .../02-earnings-brief/requirements.txt | 4 + .../03-iterative-research.ipynb | 324 ++++++++++ .../03-iterative-research/README.md | 73 +++ .../03-iterative-research/images/.gitkeep | 0 .../03-iterative-research/requirements.txt | 4 + .../04-advanced-examples/README.md 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06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/requirements.txt diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md index 7fa5208a8..3196208d0 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -80,6 +80,16 @@ export COGNITO_SCOPE="agentcore-websearch/invoke" export AWS_DEFAULT_REGION="us-east-1" ``` +```bash +#export AGENTCORE_GATEWAY_URL="https://naga-web-search-gw-jpeyqm6nni.gateway.bedrock-agentcore.us-east-1.amazonaws.com/mcp" +#export COGNITO_DOMAIN="us-east-16ay8pbcw2" +#export COGNITO_CLIENT_ID="n0gmrlojshfmc059dck71583f" +#export COGNITO_CLIENT_SECRET="s9bt641phqs1t7bie4lvg80sah9st50f6c3ooeksf0ri6d7f5po" +#export COGNITO_SCOPE="agentcore-websearch/invoke" +#export AWS_DEFAULT_REGION="us-east-1" +``` + + Export these before running the other demos. ## IAM Permissions diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md index 8d84d79d2..aad3b9048 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -2,7 +2,7 @@ ## Overview -This demo calls the AgentCore Gateway directly using the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target are working correctly. +This demo calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target infrastructure are working correctly. ``` ┌──────────────────┐ tools/list ┌─────────────────────────────────┐ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py index ff8c879b9..3d66b2479 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py @@ -41,7 +41,7 @@ # ── Configuration ───────────────────────────────────────────────────────────── -DEFAULT_QUERY = "What is today's news around the world?" +DEFAULT_QUERY = "what is tesla stock price right now?" # ── Main ────────────────────────────────────────────────────────────────────── diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py index a382e930a..87d6bddc0 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py @@ -101,7 +101,7 @@ def main(): run_query(agent, q) print("\n" + "=" * 60) - print("Demo complete!") + print("Web Search with a Strands AI Agent Demo complete!") print("=" * 60) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py index 4a18bd4cb..096d84619 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py @@ -20,6 +20,8 @@ COGNITO_CLIENT_ID — Cognito app client ID COGNITO_CLIENT_SECRET — Cognito app client secret COGNITO_SCOPE — OAuth scope string + BEDROCK_MODEL_ID — (optional) Bedrock inference profile ID or ARN; + defaults to us.anthropic.claude-sonnet-4-5-20250514-v1:0 IAM permissions required: bedrock:InvokeModel (for Claude Sonnet 4) @@ -39,13 +41,13 @@ from langchain_aws import ChatBedrockConverse from langchain_mcp_adapters.client import MultiServerMCPClient -from langgraph.prebuilt import create_react_agent +from langgraph.prebuilt import create_react_agent # noqa: E402 — stable in langgraph>=1.0 # ── Configuration ───────────────────────────────────────────────────────────── REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") MODEL_ID = os.getenv( - "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-20250514-v1:0" + "BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6" ) GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_URL", "") @@ -69,7 +71,7 @@ async def run_agent(query: str): ) # Connect to the Gateway as an MCP client - async with MultiServerMCPClient( + client = MultiServerMCPClient( { "web-search": { "transport": "streamable_http", @@ -77,24 +79,24 @@ async def run_agent(query: str): "headers": {"Authorization": f"Bearer {token}"}, } } - ) as client: - tools = client.get_tools() - print(f" Discovered {len(tools)} tool(s)") - - # Create and run the agent - agent = create_react_agent(model, tools=tools) - result = await agent.ainvoke( - {"messages": [{"role": "user", "content": query}]} - ) - - # Print the final response - print("\n[Agent Response]") - print("-" * 60) - final_message = result["messages"][-1] - if hasattr(final_message, "content"): - print(final_message.content) - else: - print(str(final_message)) + ) + tools = await client.get_tools() + print(f" Discovered {len(tools)} tool(s)") + + # Create and run the agent + agent = create_react_agent(model, tools=tools) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": query}]} + ) + + # Print the final response + print("\n[Agent Response]") + print("-" * 60) + final_message = result["messages"][-1] + if hasattr(final_message, "content"): + print(final_message.content) + else: + print(str(final_message)) # ── Main ────────────────────────────────────────────────────────────────────── @@ -123,7 +125,7 @@ def main(): asyncio.run(run_agent(args.query)) print("\n" + "=" * 60) - print("Demo complete!") + print("Web Search with a LangChain Agent Demo Complete.!") print("=" * 60) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index 5eb7e691e..3256feaf8 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -76,9 +76,28 @@ The Web Search Tool uses the **connector** target type — a fully AWS-managed i | [01-setup-gateway/](01-setup-gateway/) | boto3 (SDK) | Create IAM role, Cognito, Gateway, and Web Search target | | [02-raw-mcp/](02-raw-mcp/) | MCP protocol | Direct tool discovery and invocation without an agent | | [03-strands-agent/](03-strands-agent/) | Strands Agents | Full agent loop with automatic tool selection and cited responses | -| [04-langchain-agent/](04-langchain-agent/) | LangChain + LangGraph | Same integration using LangChain's MCP adapter | +| [04-langchain-agent/](04-langchain-agent/) | LangChain + LangGraph | Full agent loop integration using LangChain's MCP adapter | | [05-cleanup/](05-cleanup/) | boto3 (SDK) | Delete all resources created by the setup | +## Prerequisites + +Before running the agent demos (steps 3–4 in Quick Start), your AWS account must have model access enabled for the Bedrock model you intend to use. + +**Default model**: `us.anthropic.claude-sonnet-4-5-20250514-v1:0` (cross-region inference profile) + +To use a different model, export `BEDROCK_MODEL_ID` before running any agent demo: + +```bash +export BEDROCK_MODEL_ID="us.anthropic.claude-3-5-sonnet-20241022-v2:0" +``` + +> **Note:** On-demand model IDs (e.g. `anthropic.claude-sonnet-4-5-20250929-v1:0`) are not supported directly — you must use a cross-region inference profile ID (prefixed with `us.`, `eu.`, or `ap.`) or an inference profile ARN. See [Supported models and Regions for cross-Region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) for the full list. + +To enable model access in your account: +1. Open the [Amazon Bedrock console](https://console.aws.amazon.com/bedrock/) +2. Go to **Model access** in the left navigation +3. Request access for the model you want to use + ## Quick Start ```bash @@ -95,6 +114,9 @@ export COGNITO_CLIENT_SECRET="..." export COGNITO_SCOPE="agentcore-websearch/invoke" export AWS_DEFAULT_REGION="us-east-1" +# Optional: override the default Bedrock model +export BEDROCK_MODEL_ID="us.anthropic.claude-3-5-sonnet-20241022-v2:0" + # Step 3: Verify with raw MCP calls python 02-raw-mcp/raw_mcp_call.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt index a27d9618d..f7769b56a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt @@ -2,7 +2,8 @@ boto3 strands-agents>=0.1.8 mcp requests -langchain -langchain-mcp-adapters -langchain-aws -langgraph +langchain>=1.3.4 +langchain-core>=1.4.0 +langchain-mcp-adapters>=0.2.2 +langchain-aws>=1.5.0 +langgraph>=1.2.4 diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py index f656cd315..5fb37f322 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py @@ -2,11 +2,18 @@ Shared Strands agent using AgentCore Web Search Tool via Gateway. Used as the common demo agent across web search sub-demos: - - 02-strands-agent/web_search_strands.py + - 03-strands-agent/web_search_strands.py The agent connects to an AgentCore Gateway that exposes the Web Search Tool as an MCP-compliant connector target. Tools are discovered dynamically via the MCP tools/list endpoint. + +Environment variables: + BEDROCK_MODEL_ID — (optional) Bedrock inference profile ID or ARN; + must be a cross-region inference profile (e.g. us.*, eu.*, ap.*) + or an inference profile ARN — on-demand model IDs are not supported. + Defaults to us.anthropic.claude-sonnet-4-5-20250514-v1:0 + AWS_DEFAULT_REGION — AWS region (default: us-east-1) """ import os @@ -21,7 +28,8 @@ REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") MODEL_ID = os.getenv( - "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-20250514-v1:0" + "BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6" + ) SYSTEM_PROMPT = """You are a helpful research assistant with access to real-time web search. diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/01-web-search-gateway-setup.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/01-web-search-gateway-setup.ipynb new file mode 100644 index 000000000..f6a7f293e --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/01-web-search-gateway-setup.ipynb @@ -0,0 +1,603 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4015a0a7", + "metadata": {}, + "source": [ + "# AgentCore Web Search Tool — Gateway Setup and Raw MCP Calls\n", + "\n", + "## Overview\n", + "\n", + "The **Amazon Bedrock AgentCore Web Search Tool** eliminates a core limitation of AI agents: knowledge frozen at training time. This notebook creates all the infrastructure needed and verifies it with direct MCP protocol calls — no agent framework yet, just raw tool discovery and invocation.\n", + "\n", + "You will:\n", + "1. Create the IAM service role for the Gateway\n", + "2. Set up Amazon Cognito for inbound authentication\n", + "3. Create an AgentCore Gateway with MCP protocol\n", + "4. Add a Web Search Tool connector target\n", + "5. Verify with direct `tools/list` and `tools/call` MCP calls\n", + "\n", + "The environment variables printed at the end are used by notebooks 02 and 03.\n", + "\n", + "![Tutorial Architecture](../images/tutorial-architecture.png)\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Gateway target type | Connector (`web-search`) |\n", + "| Inbound Auth IdP | Amazon Cognito (`client_credentials` flow) |\n", + "| Outbound Auth | Automatic (Gateway IAM Role) |\n", + "| Tutorial vertical | Genral Purpose - applicable in any industry |\n", + "| Example complexity | Easy |\n", + "| SDK used | boto3 |\n", + "\n", + "### Authentication Architecture\n", + "\n", + "![Inbound and Outbound Auth](../images/inbound-and-outbound-auth.png)\n", + "\n", + "- **Inbound**: Cognito validates the OAuth token your agent passes to the Gateway\n", + "- **Outbound**: The Gateway IAM role authenticates to the Web Search backend automatically" + ] + }, + { + "cell_type": "markdown", + "id": "d5fd60fd", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "- Jupyter notebook (Python kernel)\n", + "- Python 3.10+ or later\n", + "- AWS credentials - choode one \n", + " - **Account ID + Role Name** — the notebook will call `sts:AssumeRole` to get temporary credentials (recommended for cross-account or Isengard access)\n", + " - **Pre-configured credentials** — via AWS CLI profile, environment variables, or instance role\n", + "- AWS account with access to Bedrock AgentCore Gateway, Cognito, and IAM\n", + "- Access to Bedrock inference models in us-east-1\n", + "\n", + "> **Region**: The Web Search Tool connector is available in **us-east-1** only.\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": [\n", + " \"iam:CreateRole\", \"iam:PutRolePolicy\", \"iam:GetRole\",\n", + " \"cognito-idp:CreateUserPool\", \"cognito-idp:CreateUserPoolDomain\",\n", + " \"cognito-idp:CreateResourceServer\", \"cognito-idp:CreateUserPoolClient\",\n", + " \"cognito-idp:ListUserPools\", \"cognito-idp:ListUserPoolClients\",\n", + " \"cognito-idp:DescribeUserPoolClient\", \"cognito-idp:DescribeResourceServer\",\n", + " \"bedrock-agentcore:CreateGateway\", \"bedrock-agentcore:GetGateway\",\n", + " \"bedrock-agentcore:CreateGatewayTarget\", \"bedrock-agentcore:ListGatewayTargets\"\n", + " ],\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "id": "ebf78fad", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c228fe71", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "d420a08e", + "metadata": {}, + "source": [ + "## 2. Configure AWS Region and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "822151f1", + "metadata": {}, + "outputs": [], + "source": [ + "import boto3\n", + "import json\n", + "import os\n", + "import time\n", + "import requests\n", + "\n", + "# Web Search connector is only available in us-east-1\n", + "REGION = \"us-east-1\"\n", + "os.environ[\"AWS_DEFAULT_REGION\"] = REGION\n", + "\n", + "# Verify credentials\n", + "sts_client = boto3.client(\"sts\", region_name=REGION)\n", + "identity = sts_client.get_caller_identity()\n", + "ACCOUNT_ID = identity[\"Account\"]\n", + "print(f\"Account ID : {ACCOUNT_ID}\")\n", + "print(f\"Identity : {identity['Arn']}\")\n", + "print(f\"Region : {REGION}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d53c4327", + "metadata": {}, + "source": [ + "## 3. Create the Gateway Service Role\n", + "\n", + "The Gateway needs an IAM role allowing the AgentCore service to perform actions on your behalf. Two permissions are required:\n", + "- `bedrock-agentcore:InvokeGateway` — invoke the Gateway itself\n", + "- `bedrock-agentcore:InvokeWebSearch` — authorize web search calls to `arn:aws:bedrock-agentcore::aws:tool/web-search.v1`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab8c7616", + "metadata": {}, + "outputs": [], + "source": [ + "GATEWAY_NAME = \"web-search-gateway\"\n", + "GATEWAY_ROLE_NAME = f\"agentcore-{GATEWAY_NAME}-role\"\n", + "\n", + "iam_client = boto3.client(\"iam\")\n", + "\n", + "assume_role_policy = {\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [{\n", + " \"Effect\": \"Allow\",\n", + " \"Principal\": {\"Service\": \"bedrock-agentcore.amazonaws.com\"},\n", + " \"Action\": \"sts:AssumeRole\",\n", + " \"Condition\": {\n", + " \"StringEquals\": {\"aws:SourceAccount\": ACCOUNT_ID},\n", + " \"ArnLike\": {\"aws:SourceArn\": f\"arn:aws:bedrock-agentcore:{REGION}:{ACCOUNT_ID}:*\"}\n", + " }\n", + " }]\n", + "}\n", + "\n", + "try:\n", + " role_resp = iam_client.create_role(\n", + " RoleName=GATEWAY_ROLE_NAME,\n", + " AssumeRolePolicyDocument=json.dumps(assume_role_policy)\n", + " )\n", + " print(f\"Created role: {GATEWAY_ROLE_NAME}\")\n", + " time.sleep(10) # Allow IAM to propagate\n", + "except iam_client.exceptions.EntityAlreadyExistsException:\n", + " role_resp = iam_client.get_role(RoleName=GATEWAY_ROLE_NAME)\n", + " print(f\"Role already exists: {GATEWAY_ROLE_NAME}\")\n", + "\n", + "GATEWAY_ROLE_ARN = role_resp[\"Role\"][\"Arn\"]\n", + "\n", + "iam_client.put_role_policy(\n", + " RoleName=GATEWAY_ROLE_NAME,\n", + " PolicyName=\"WebSearchGatewayPolicy\",\n", + " PolicyDocument=json.dumps({\n", + " \"Version\": \"2012-10-17\",\n", + " \"Statement\": [\n", + " {\n", + " \"Sid\": \"InvokeGateway\",\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock-agentcore:InvokeGateway\",\n", + " \"Resource\": f\"arn:aws:bedrock-agentcore:{REGION}:{ACCOUNT_ID}:gateway/*\"\n", + " },\n", + " {\n", + " \"Sid\": \"InvokeWebSearch\",\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock-agentcore:InvokeWebSearch\",\n", + " \"Resource\": f\"arn:aws:bedrock-agentcore:{REGION}:aws:tool/web-search.v1\"\n", + " }\n", + " ]\n", + " })\n", + ")\n", + "print(f\"Gateway Role ARN: {GATEWAY_ROLE_ARN}\")\n", + "print(\"Permissions attached ✓\")" + ] + }, + { + "cell_type": "markdown", + "id": "3acd6c11", + "metadata": {}, + "source": [ + "## 4. Configure Inbound Authentication with Amazon Cognito\n", + "\n", + "We create a Cognito User Pool with a machine-to-machine (M2M) client using the `client_credentials` OAuth flow. The Gateway will validate JWT tokens issued by this pool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c74df02d", + "metadata": {}, + "outputs": [], + "source": [ + "cognito = boto3.client(\"cognito-idp\", region_name=REGION)\n", + "\n", + "USER_POOL_NAME = \"agentcore-websearch-pool\"\n", + "RESOURCE_SERVER_ID = \"agentcore-websearch\"\n", + "SCOPES = [{\"ScopeName\": \"invoke\", \"ScopeDescription\": \"Invoke gateway\"}]\n", + "SCOPE_NAMES = [f\"{RESOURCE_SERVER_ID}/{s['ScopeName']}\" for s in SCOPES]\n", + "SCOPE_STRING = \" \".join(SCOPE_NAMES)\n", + "\n", + "# Find or create user pool\n", + "USER_POOL_ID = None\n", + "for pool in cognito.list_user_pools(MaxResults=60)[\"UserPools\"]:\n", + " if pool[\"Name\"] == USER_POOL_NAME:\n", + " USER_POOL_ID = pool[\"Id\"]\n", + " break\n", + "\n", + "if USER_POOL_ID is None:\n", + " resp = cognito.create_user_pool(PoolName=USER_POOL_NAME)\n", + " USER_POOL_ID = resp[\"UserPool\"][\"Id\"]\n", + " domain = USER_POOL_ID.replace(\"_\", \"\").lower()\n", + " cognito.create_user_pool_domain(Domain=domain, UserPoolId=USER_POOL_ID)\n", + " print(f\"Created user pool: {USER_POOL_ID}\")\n", + "else:\n", + " print(f\"User pool exists: {USER_POOL_ID}\")\n", + "\n", + "# Create resource server\n", + "try:\n", + " cognito.describe_resource_server(UserPoolId=USER_POOL_ID, Identifier=RESOURCE_SERVER_ID)\n", + "except cognito.exceptions.ResourceNotFoundException:\n", + " cognito.create_resource_server(\n", + " UserPoolId=USER_POOL_ID, Identifier=RESOURCE_SERVER_ID,\n", + " Name=\"WebSearch Gateway Resource Server\", Scopes=SCOPES\n", + " )\n", + "print(\"Resource server ensured ✓\")\n", + "\n", + "# Find or create M2M client\n", + "CLIENT_ID, CLIENT_SECRET = None, None\n", + "for c in cognito.list_user_pool_clients(UserPoolId=USER_POOL_ID, MaxResults=60)[\"UserPoolClients\"]:\n", + " if c[\"ClientName\"] == \"agentcore-websearch-client\":\n", + " desc = cognito.describe_user_pool_client(UserPoolId=USER_POOL_ID, ClientId=c[\"ClientId\"])\n", + " CLIENT_ID = c[\"ClientId\"]\n", + " CLIENT_SECRET = desc[\"UserPoolClient\"][\"ClientSecret\"]\n", + " break\n", + "\n", + "if CLIENT_ID is None:\n", + " created = cognito.create_user_pool_client(\n", + " UserPoolId=USER_POOL_ID, ClientName=\"agentcore-websearch-client\",\n", + " GenerateSecret=True, AllowedOAuthFlows=[\"client_credentials\"],\n", + " AllowedOAuthScopes=SCOPE_NAMES, AllowedOAuthFlowsUserPoolClient=True,\n", + " SupportedIdentityProviders=[\"COGNITO\"],\n", + " ExplicitAuthFlows=[\"ALLOW_REFRESH_TOKEN_AUTH\"]\n", + " )\n", + " CLIENT_ID = created[\"UserPoolClient\"][\"ClientId\"]\n", + " CLIENT_SECRET = created[\"UserPoolClient\"][\"ClientSecret\"]\n", + " print(f\"Created client: {CLIENT_ID}\")\n", + "else:\n", + " print(f\"Client exists: {CLIENT_ID}\")\n", + "\n", + "COGNITO_DOMAIN = USER_POOL_ID.replace(\"_\", \"\").lower()\n", + "DISCOVERY_URL = f\"https://cognito-idp.{REGION}.amazonaws.com/{USER_POOL_ID}/.well-known/openid-configuration\"\n", + "print(f\"Discovery URL: {DISCOVERY_URL}\")" + ] + }, + { + "cell_type": "markdown", + "id": "eed44e04", + "metadata": {}, + "source": [ + "## 5. Create the AgentCore Gateway" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "542cb13c", + "metadata": {}, + "outputs": [], + "source": [ + "gateway_client = boto3.client(\"bedrock-agentcore-control\", region_name=REGION)\n", + "\n", + "create_resp = gateway_client.create_gateway(\n", + " name=GATEWAY_NAME,\n", + " roleArn=GATEWAY_ROLE_ARN,\n", + " protocolType=\"MCP\",\n", + " protocolConfiguration={\n", + " \"mcp\": {\"supportedVersions\": [\"2025-03-26\"], \"searchType\": \"SEMANTIC\"}\n", + " },\n", + " authorizerType=\"CUSTOM_JWT\",\n", + " authorizerConfiguration={\n", + " \"customJWTAuthorizer\": {\n", + " \"allowedClients\": [CLIENT_ID],\n", + " \"discoveryUrl\": DISCOVERY_URL\n", + " }\n", + " },\n", + " description=\"AgentCore Gateway with Web Search Tool\"\n", + ")\n", + "\n", + "GATEWAY_ID = create_resp[\"gatewayId\"]\n", + "GATEWAY_URL = create_resp[\"gatewayUrl\"]\n", + "print(f\"Gateway ID : {GATEWAY_ID}\")\n", + "print(f\"Gateway URL : {GATEWAY_URL}\")\n", + "\n", + "# Wait for READY\n", + "for _ in range(30):\n", + " status = gateway_client.get_gateway(gatewayIdentifier=GATEWAY_ID)[\"status\"]\n", + " if status == \"READY\":\n", + " break\n", + " time.sleep(5)\n", + "print(f\"Gateway status: {status}\")" + ] + }, + { + "cell_type": "markdown", + "id": "64b4bad3", + "metadata": {}, + "source": [ + "## 6. Add the Web Search Connector Target\n", + "\n", + "The Web Search Tool uses the `connector` target type. Specify `connectorId: \"web-search\"` and the Gateway handles schema management, endpoint resolution, and outbound auth automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e2d8208", + "metadata": {}, + "outputs": [], + "source": [ + "target_resp = gateway_client.create_gateway_target(\n", + " name=\"web-search-tool\",\n", + " gatewayIdentifier=GATEWAY_ID,\n", + " targetConfiguration={\n", + " \"mcp\": {\n", + " \"connector\": {\n", + " \"source\": {\"connectorId\": \"web-search\"},\n", + " \"configurations\": [{\"name\": \"WebSearch\", \"parameterValues\": {}}]\n", + " }\n", + " }\n", + " },\n", + " credentialProviderConfigurations=[\n", + " {\"credentialProviderType\": \"GATEWAY_IAM_ROLE\"}\n", + " ]\n", + ")\n", + "\n", + "TARGET_ID = target_resp[\"targetId\"]\n", + "print(f\"Target ID: {TARGET_ID}\")\n", + "\n", + "# Wait for target READY\n", + "for _ in range(30):\n", + " targets = gateway_client.list_gateway_targets(gatewayIdentifier=GATEWAY_ID)\n", + " statuses = [t[\"status\"] for t in targets[\"items\"]]\n", + " print(f\" Target statuses: {statuses}\")\n", + " if all(s == \"READY\" for s in statuses):\n", + " print(\"\\n✅ All targets READY\")\n", + " break\n", + " time.sleep(5)" + ] + }, + { + "cell_type": "markdown", + "id": "6c104338", + "metadata": {}, + "source": [ + "## 7. Get an OAuth Token\n", + "\n", + "Retrieve a Cognito access token using the `client_credentials` flow. This token is passed as a Bearer header on every MCP call." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dde14821", + "metadata": {}, + "outputs": [], + "source": [ + "def get_token():\n", + " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\n", + " \"grant_type\": \"client_credentials\",\n", + " \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET,\n", + " \"scope\": SCOPE_STRING\n", + " }\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "# Allow time for Cognito domain propagation\n", + "print(\"Waiting for Cognito domain propagation...\")\n", + "time.sleep(15)\n", + "\n", + "token = get_token()\n", + "print(f\"Token obtained ✓ (first 20 chars: {token[:20]}...)\")" + ] + }, + { + "cell_type": "markdown", + "id": "6ba40bc7", + "metadata": {}, + "source": [ + "## 8. Verify: Raw MCP Tool Discovery (`tools/list`)\n", + "\n", + "Call the Gateway directly using MCP — no agent framework. This confirms the WebSearch tool is available and shows its input schema." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06ea7f3", + "metadata": {}, + "outputs": [], + "source": [ + "from mcp.client.streamable_http import streamablehttp_client\n", + "from strands.tools.mcp.mcp_client import MCPClient\n", + "\n", + "\n", + "def create_transport():\n", + " return streamablehttp_client(\n", + " GATEWAY_URL,\n", + " headers={\"Authorization\": f\"Bearer {token}\"}\n", + " )\n", + "\n", + "\n", + "mcp_client = MCPClient(create_transport)\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " print(f\"Discovered {len(tools)} tool(s):\\n\")\n", + " for tool in tools:\n", + " spec = tool.tool_spec\n", + " print(f\" Name : {spec['name']}\")\n", + " print(f\" Description : {spec.get('description', 'N/A')}\")\n", + " schema = json.dumps(spec.get('inputSchema', {}), indent=4)\n", + " print(f\" Input schema: {schema[:300]}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "6032a124", + "metadata": {}, + "source": [ + "## 9. Verify: Raw MCP Tool Invocation (`tools/call`)\n", + "\n", + "Invoke the WebSearch tool directly and inspect the structured response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "817679e6", + "metadata": {}, + "outputs": [], + "source": [ + "TEST_QUERY = \"What is today's news around the world?\"\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " ws_tool_name = next(t.tool_name for t in tools if \"WebSearch\" in t.tool_name)\n", + "\n", + " print(f\"Invoking: {ws_tool_name}\")\n", + " print(f\"Query : {TEST_QUERY}\\n\")\n", + "\n", + " result = mcp_client.call_tool_sync(\"verify-call\", ws_tool_name, {\"query\": TEST_QUERY})\n", + "\n", + " for content in result.get(\"content\", [result] if not isinstance(result, list) else result):\n", + " if isinstance(content, dict) and \"text\" in content:\n", + " try:\n", + " parsed = json.loads(content[\"text\"])\n", + " print(json.dumps(parsed, indent=2))\n", + " except (ValueError, TypeError):\n", + " print(content[\"text\"][:2000])\n", + " else:\n", + " print(content)" + ] + }, + { + "cell_type": "markdown", + "id": "22a08536", + "metadata": {}, + "source": [ + "## 10. Save Environment Variables for Notebooks 02 and 03\n", + "\n", + "Export these before running the next notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63754959", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"# Copy and export these environment variables:\\n\")\n", + "print(f'export AGENTCORE_GATEWAY_URL=\"{GATEWAY_URL}\"')\n", + "print(f'export COGNITO_DOMAIN=\"{COGNITO_DOMAIN}\"')\n", + "print(f'export COGNITO_CLIENT_ID=\"{CLIENT_ID}\"')\n", + "print(f'export COGNITO_CLIENT_SECRET=\"{CLIENT_SECRET}\"')\n", + "print(f'export COGNITO_SCOPE=\"{SCOPE_STRING}\"')\n", + "print(f'export AWS_DEFAULT_REGION=\"{REGION}\"')\n", + "print(f'\\n# For cleanup:')\n", + "print(f'# Gateway ID : {GATEWAY_ID}')\n", + "print(f'# User Pool ID : {USER_POOL_ID}')\n", + "print(f'# IAM Role : {GATEWAY_ROLE_NAME}')" + ] + }, + { + "cell_type": "markdown", + "id": "da25e94b", + "metadata": {}, + "source": [ + "## Cleanup (Optional)\n", + "\n", + "Run this cell only when you are done with **all** tutorials in this workshop. The Gateway and Cognito pool are reused by notebooks 02 and 03." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90d7f934", + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to delete all resources\n", + "\n", + "# # Delete Gateway targets and Gateway\n", + "# for item in gateway_client.list_gateway_targets(gatewayIdentifier=GATEWAY_ID, maxResults=100)[\"items\"]:\n", + "# gateway_client.delete_gateway_target(gatewayIdentifier=GATEWAY_ID, targetId=item[\"targetId\"])\n", + "# print(f\"Deleted target: {item['name']}\")\n", + "# time.sleep(10)\n", + "# gateway_client.delete_gateway(gatewayIdentifier=GATEWAY_ID)\n", + "# print(f\"Deleted gateway: {GATEWAY_ID}\")\n", + "\n", + "# # Delete Cognito\n", + "# cognito.delete_user_pool_domain(Domain=COGNITO_DOMAIN, UserPoolId=USER_POOL_ID)\n", + "# cognito.delete_user_pool(UserPoolId=USER_POOL_ID)\n", + "# print(f\"Deleted user pool: {USER_POOL_ID}\")\n", + "\n", + "# # Delete IAM role\n", + "# iam_client.delete_role_policy(RoleName=GATEWAY_ROLE_NAME, PolicyName=\"WebSearchGatewayPolicy\")\n", + "# iam_client.delete_role(RoleName=GATEWAY_ROLE_NAME)\n", + "# print(f\"Deleted role: {GATEWAY_ROLE_NAME}\")\n", + "\n", + "print(\"Uncomment the lines above to delete resources.\")" + ] + }, + { + "cell_type": "markdown", + "id": "a912b8ec", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you:\n", + "- Created an AgentCore Gateway with MCP protocol and Cognito JWT authentication\n", + "- Added a Web Search connector target — no schema or endpoint configuration needed\n", + "- Verified the setup with raw `tools/list` and `tools/call` MCP invocations\n", + "\n", + "**Next**: Run notebook `02-web-search-strands-agent.ipynb` to use this Gateway with a Strands AI agent." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md new file mode 100644 index 000000000..98f8bb52b --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md @@ -0,0 +1,61 @@ +# Web Search Tool — Gateway Setup and Raw MCP Calls + +## Overview + +In this tutorial you will learn how to: + +1. Create the IAM service role for the Gateway +2. Configure Amazon Cognito for inbound authentication +3. Create an AgentCore Gateway with MCP protocol +4. Add a Web Search Tool connector target +5. Verify the setup with direct MCP `tools/list` and `tools/call` invocations + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Gateway target type | Connector (`web-search`) | +| Tutorial vertical | Cross-vertical | +| Example complexity | Easy | +| SDK used | boto3 | + +### Tutorial Architecture + +
+ +
+ +### Inbound and Outbound Authentication + +
+ +
+ +The Gateway uses two authentication layers: +- **Inbound**: Amazon Cognito validates the OAuth token passed by your agent +- **Outbound**: The Gateway's IAM role authenticates automatically to the Web Search backend — no additional credentials needed + +## Notebook + +→ [`01-web-search-gateway-setup.ipynb`](01-web-search-gateway-setup.ipynb) + +## Prerequisites + +- AWS account allowlisted for the Web Search Tool private beta +- Python 3.10+ and Jupyter +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions + +```bash +pip install -r requirements.txt +``` + +> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. + +## What You'll Build + +After completing this tutorial you'll have: +- An AgentCore Gateway with MCP protocol +- A Web Search connector target in READY state +- Environment variables ready for tutorials 02 and 03 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt new file mode 100644 index 000000000..1d8cae582 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt @@ -0,0 +1,4 @@ +boto3 +mcp +requests +strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb new file mode 100644 index 000000000..13093a9c0 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Web Search Tool with Strands Agent\n", + "\n", + "## Overview\n", + "\n", + "This notebook connects a Strands AI agent to the AgentCore Gateway created in notebook 01. The agent automatically discovers the Web Search tool via MCP `tools/list`, then invokes it when answering questions that require current information.\n", + "\n", + "You will:\n", + "1. Connect to the Gateway using MCP Streamable HTTP transport\n", + "2. Discover the WebSearch tool at runtime\n", + "3. Create a Strands agent and ask real-time questions\n", + "4. Inspect the agent's tool calls and grounded responses\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Agentic framework | Strands Agents |\n", + "| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) |\n", + "| Tutorial vertical | Cross-vertical |\n", + "| Example complexity | Easy |\n", + "| SDK used | boto3, strands-agents |\n", + "\n", + "### How It Works\n", + "\n", + "```\n", + "User question\n", + " │\n", + " ▼\n", + "Strands Agent (Claude Sonnet 4)\n", + " │ decides to call WebSearch\n", + " ▼\n", + "MCP tools/call → AgentCore Gateway → Web Search Backend\n", + " │\n", + " ▼\n", + "Structured results { text, url, title, publishedDate }\n", + " │\n", + " ▼\n", + "Final answer with cited sources\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Complete notebook `01-web-search-gateway-setup.ipynb` first and set the following environment variables:\n", + "\n", + "```bash\n", + "export AGENTCORE_GATEWAY_URL=\"...\"\n", + "export COGNITO_DOMAIN=\"...\"\n", + "export COGNITO_CLIENT_ID=\"...\"\n", + "export COGNITO_CLIENT_SECRET=\"...\"\n", + "export COGNITO_SCOPE=\"agentcore-websearch/invoke\"\n", + "export AWS_DEFAULT_REGION=\"us-east-1\"\n", + "```\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock:InvokeModel\",\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from mcp.client.streamable_http import streamablehttp_client\n", + "from strands import Agent\n", + "from strands.models import BedrockModel\n", + "from strands.tools.mcp.mcp_client import MCPClient\n", + "\n", + "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", + "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", + "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", + "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", + "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", + "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", + "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", + "\n", + "print(f\"Gateway URL : {GATEWAY_URL}\")\n", + "print(f\"Region : {REGION}\")\n", + "print(f\"Model : {MODEL_ID}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Create OAuth Token and MCP Transport" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_token():\n", + " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\n", + " \"grant_type\": \"client_credentials\",\n", + " \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET,\n", + " \"scope\": SCOPE_STRING\n", + " }\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "\n", + "def create_transport():\n", + " \"\"\"Create a fresh MCP Streamable HTTP transport with a valid token.\"\"\"\n", + " token = get_token()\n", + " return streamablehttp_client(\n", + " GATEWAY_URL,\n", + " headers={\"Authorization\": f\"Bearer {token}\"}\n", + " )\n", + "\n", + "\n", + "mcp_client = MCPClient(create_transport)\n", + "print(\"MCP client created ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Discover Tools and Create the Agent\n", + "\n", + "The agent discovers tools dynamically at runtime via `tools/list`. Strands registers them automatically — no manual tool definitions needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SYSTEM_PROMPT = \"\"\"You are a helpful research assistant with access to real-time web search.\n", + "\n", + "PRINCIPLES:\n", + "- Use WebSearch to find current information before answering\n", + "- Always cite your sources with URLs\n", + "- Keep queries concise (under 200 characters)\n", + "- Synthesize information from multiple results when possible\n", + "\"\"\"\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " print(f\"Discovered {len(tools)} tool(s): {[t.tool_name for t in tools]}\")\n", + "\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", + " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", + " print(f\"Agent ready with tools: {agent.tool_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Test the Agent on a Current Events Question\n", + "\n", + "The agent will automatically invoke WebSearch and return a grounded answer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "QUERY = \"What is today's news around the world?\"\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", + " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", + "\n", + " print(f\"Query: {QUERY}\")\n", + " print(\"-\" * 60)\n", + " response = agent(QUERY)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"AGENT RESPONSE\")\n", + "print(\"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Try More Queries\n", + "\n", + "The WebSearch tool handles queries up to 200 characters. Here are a few more examples showing different query types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demo_queries = [\n", + " \"What are the latest developments in quantum computing in 2026?\",\n", + " \"What is the current price of gold?\",\n", + " \"What new AWS services were announced recently?\"\n", + "]\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", + " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", + "\n", + " for query in demo_queries:\n", + " print(f\"\\n{'=' * 60}\")\n", + " print(f\"Query: {query}\")\n", + " print(\"=\" * 60)\n", + " response = agent(query)\n", + " if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you:\n", + "- Connected a Strands agent to the AgentCore Gateway via MCP Streamable HTTP\n", + "- Discovered the WebSearch tool dynamically at runtime\n", + "- Got grounded, cited answers to real-time questions\n", + "\n", + "**Next**: Run notebook `03-web-search-langchain-agent.ipynb` for the same integration using LangChain and LangGraph." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md new file mode 100644 index 000000000..d51a409ba --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md @@ -0,0 +1,51 @@ +# Web Search Tool with Strands Agent + +## Overview + +In this tutorial you will learn how to: + +1. Connect a Strands agent to an AgentCore Gateway via MCP Streamable HTTP +2. Discover the Web Search tool dynamically at runtime +3. Ask real-time questions — the agent invokes WebSearch automatically +4. Receive grounded responses with cited source URLs + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Agentic framework | Strands Agents | +| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) | +| Tutorial vertical | Cross-vertical | +| Example complexity | Easy | +| SDK used | boto3, strands-agents | + +### Tutorial Architecture + +``` +┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ +│ User │ ─────────────────────────────▶ │ Strands Agent │ +│ │◀───────────────────────────── │ Claude Sonnet 4 │ +│ │ Grounded answer + citations │ │ +└────────────┘ │ tools: [WebSearch] │ + │ │ MCP tools/call │ + └───────┼──────────────────┘ + ▼ + ┌──────────────────────────┐ + │ AgentCore Gateway │ + │ Web Search Connector │ + └──────────────────────────┘ +``` + +## Notebook + +→ [`02-web-search-strands-agent.ipynb`](02-web-search-strands-agent.ipynb) + +## Prerequisites + +Complete tutorial `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. + +```bash +pip install -r requirements.txt +``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt new file mode 100644 index 000000000..1d8cae582 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt @@ -0,0 +1,4 @@ +boto3 +mcp +requests +strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb new file mode 100644 index 000000000..704725da1 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Web Search Tool with LangChain Agent\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the ReAct agent loop.\n", + "\n", + "You will:\n", + "1. Connect to the Gateway using `MultiServerMCPClient` from `langchain-mcp-adapters`\n", + "2. Discover Web Search tools via MCP\n", + "3. Build a ReAct agent with `ChatBedrockConverse`\n", + "4. Run web-grounded Q&A\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Agentic framework | LangChain + LangGraph |\n", + "| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) |\n", + "| Tutorial vertical | Cross-vertical |\n", + "| Example complexity | Easy |\n", + "| SDK used | boto3, langchain-aws, langchain-mcp-adapters |\n", + "\n", + "### How It Works\n", + "\n", + "```\n", + "User question\n", + " │\n", + " ▼\n", + "LangGraph ReAct Agent (ChatBedrockConverse)\n", + " │ calls WebSearch tool\n", + " ▼\n", + "MultiServerMCPClient → AgentCore Gateway → Web Search Backend\n", + " │\n", + " ▼\n", + "Structured results → Final answer with citations\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Complete notebook `01-web-search-gateway-setup.ipynb` first and set the environment variables it prints.\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock:InvokeModel\",\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "import os\n", + "import requests\n", + "\n", + "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", + "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", + "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", + "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", + "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", + "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", + "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", + "\n", + "print(f\"Gateway URL : {GATEWAY_URL}\")\n", + "print(f\"Region : {REGION}\")\n", + "print(f\"Model : {MODEL_ID}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Token Helper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_token():\n", + " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\n", + " \"grant_type\": \"client_credentials\",\n", + " \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET,\n", + " \"scope\": SCOPE_STRING\n", + " }\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "token = get_token()\n", + "print(\"Token obtained ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Create LangChain Agent with MCP Tools\n", + "\n", + "`MultiServerMCPClient` connects to one or more MCP servers and converts their tools into LangChain-compatible tool objects. `create_react_agent` builds a LangGraph ReAct agent with those tools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_aws import ChatBedrockConverse\n", + "from langchain_mcp_adapters.client import MultiServerMCPClient\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "\n", + "async def run_agent(query: str) -> str:\n", + " \"\"\"Run the LangChain agent with a web search query.\"\"\"\n", + " model = ChatBedrockConverse(\n", + " model=MODEL_ID,\n", + " region_name=REGION,\n", + " temperature=0.7,\n", + " max_tokens=1024\n", + " )\n", + "\n", + " async with MultiServerMCPClient({\n", + " \"web-search\": {\n", + " \"transport\": \"streamable_http\",\n", + " \"url\": GATEWAY_URL,\n", + " \"headers\": {\"Authorization\": f\"Bearer {get_token()}\"}\n", + " }\n", + " }) as client:\n", + " tools = client.get_tools()\n", + " print(f\"Discovered {len(tools)} tool(s): {[t.name for t in tools]}\")\n", + "\n", + " agent = create_react_agent(model, tools=tools)\n", + " result = await agent.ainvoke({\n", + " \"messages\": [{\"role\": \"user\", \"content\": query}]\n", + " })\n", + "\n", + " final_message = result[\"messages\"][-1]\n", + " return final_message.content if hasattr(final_message, \"content\") else str(final_message)\n", + "\n", + "\n", + "print(\"Agent function defined ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Ask a Real-Time Question" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "QUERY = \"What is today's news around the world?\"\n", + "\n", + "print(f\"Query: {QUERY}\")\n", + "print(\"-\" * 60)\n", + "\n", + "answer = await run_agent(QUERY)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"LANGCHAIN AGENT RESPONSE\")\n", + "print(\"=\" * 60)\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Try More Queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demo_queries = [\n", + " \"What are the latest AI model releases in 2026?\",\n", + " \"What is the current EUR/USD exchange rate?\",\n", + " \"What new features were announced in Python 3.14?\"\n", + "]\n", + "\n", + "for query in demo_queries:\n", + " print(f\"\\n{'=' * 60}\")\n", + " print(f\"Query: {query}\")\n", + " print(\"=\" * 60)\n", + " answer = await run_agent(query)\n", + " print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you:\n", + "- Used `langchain-mcp-adapters` to connect to the AgentCore Gateway\n", + "- Built a LangGraph ReAct agent with `ChatBedrockConverse`\n", + "- Got grounded, cited answers using the same Gateway from notebook 01\n", + "\n", + "The Gateway is framework-agnostic — Strands, LangChain, or any MCP client works the same way.\n", + "\n", + "**Next**: Explore the advanced examples in `04-advanced-examples/` for multi-turn search patterns." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md new file mode 100644 index 000000000..3ce70c848 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md @@ -0,0 +1,51 @@ +# Web Search Tool with LangChain Agent + +## Overview + +In this tutorial you will learn how to: + +1. Connect a LangChain agent to an AgentCore Gateway using `langchain-mcp-adapters` +2. Discover Web Search tools via the MCP protocol +3. Build a ReAct agent with `ChatBedrockConverse` and LangGraph +4. Run web-grounded Q&A with the same Gateway set up in tutorial 01 + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Agentic framework | LangChain + LangGraph | +| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) | +| Tutorial vertical | Cross-vertical | +| Example complexity | Easy | +| SDK used | boto3, langchain-aws, langchain-mcp-adapters | + +### Tutorial Architecture + +``` +┌────────────┐ question ┌─────────────────────────────────────────┐ +│ User │ ───────────▶ │ LangGraph ReAct Agent │ +│ │◀─────────── │ ChatBedrockConverse (Claude Sonnet 4) │ +│ │ answer │ │ +└────────────┘ │ MultiServerMCPClient │ + │ │ streamable_http │ + └───────┼─────────────────────────────────┘ + ▼ + ┌──────────────────────┐ + │ AgentCore Gateway │ + │ Web Search │ + └──────────────────────┘ +``` + +## Notebook + +→ [`03-web-search-langchain-agent.ipynb`](03-web-search-langchain-agent.ipynb) + +## Prerequisites + +Complete tutorial `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. + +```bash +pip install -r requirements.txt +``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt new file mode 100644 index 000000000..9b6ebd50e --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt @@ -0,0 +1,7 @@ +boto3 +mcp +requests +langchain +langchain-mcp-adapters +langchain-aws +langgraph diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb new file mode 100644 index 000000000..8c043f202 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CVE Scanner — Multi-turn Security Vulnerability Search\n", + "\n", + "## Overview\n", + "\n", + "This notebook builds a security agent that scans a dependency list for recent CVEs using live web search. The agent issues one focused search per package, then synthesizes severity across all results into a prioritized remediation report.\n", + "\n", + "**Why live search is critical here**: CVE databases update daily. A newly published exploit or patch changes the risk profile of a dependency within hours — training data is always stale for this.\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Agentic framework | Strands Agents |\n", + "| LLM model | Anthropic Claude Sonnet 4 |\n", + "| Tutorial vertical | Security / DevOps |\n", + "| Example complexity | Intermediate |\n", + "| SDK used | boto3, strands-agents |\n", + "\n", + "### Multi-turn Search Pattern\n", + "\n", + "```\n", + "Input: requirements.txt\n", + " │\n", + " ▼\n", + "Parse packages + versions\n", + " │\n", + " ▼\n", + "For each package:\n", + " Search 1: \"CVE vulnerability\"\n", + " Search 2: \" latest version security patch\"\n", + " │\n", + " ▼\n", + "Aggregate + score by CVSS severity\n", + " │\n", + " ▼\n", + "Output: Prioritized remediation report\n", + " CRITICAL requests 2.28.0 → CVE-XXXX, upgrade to 2.32.3\n", + " HIGH urllib3 1.26.5 → CVE-YYYY, upgrade to 2.2.2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock:InvokeModel\",\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configuration and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from mcp.client.streamable_http import streamablehttp_client\n", + "from strands import Agent\n", + "from strands.models import BedrockModel\n", + "from strands.tools.mcp.mcp_client import MCPClient\n", + "\n", + "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", + "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", + "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", + "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", + "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", + "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", + "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", + "\n", + "\n", + "def get_token():\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "\n", + "def create_transport():\n", + " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", + "\n", + "\n", + "print(\"Configuration loaded ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Define the Dependency List\n", + "\n", + "This is the list of packages to scan. Update it with your actual dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sample dependency list — replace with your actual requirements\n", + "DEPENDENCIES = [\n", + " \"requests==2.28.0\",\n", + " \"urllib3==1.26.5\",\n", + " \"cryptography==3.4.8\",\n", + " \"pillow==9.0.0\",\n", + " \"django==3.2.0\"\n", + "]\n", + "\n", + "print(f\"Packages to scan ({len(DEPENDENCIES)}):\")\n", + "for dep in DEPENDENCIES:\n", + " print(f\" {dep}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. CVE Scanner Agent\n", + "\n", + "The agent uses the CVE Scanner system prompt, which instructs it to search for each dependency separately and return a structured severity report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CVE_SYSTEM_PROMPT = \"\"\"You are a security analyst agent with access to real-time web search.\n", + "\n", + "When given a list of Python dependencies, you will:\n", + "1. For each package and version, search for known CVEs and security advisories\n", + "2. Search for the latest patched version of each package\n", + "3. Assign a severity level: CRITICAL, HIGH, MEDIUM, LOW, or NONE\n", + "4. Produce a remediation report sorted by severity\n", + "\n", + "SEARCH STRATEGY:\n", + "- Use queries like \"CVE \" and \" security vulnerability 2025 2026\"\n", + "- Keep each query under 200 characters\n", + "- Search each package independently before synthesizing\n", + "\n", + "OUTPUT FORMAT:\n", + "For each package, output a row:\n", + " | == | | Upgrade to: | \n", + "\n", + "End with a summary of total findings by severity level.\n", + "\"\"\"\n", + "\n", + "mcp_client = MCPClient(create_transport)\n", + "print(\"CVE scanner agent ready ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Run the CVE Scan\n", + "\n", + "The agent will issue multiple searches — one per package — and synthesize a prioritized report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dep_list = \"\\n\".join(f\" - {d}\" for d in DEPENDENCIES)\n", + "scan_prompt = f\"\"\"Scan the following Python dependencies for security vulnerabilities and CVEs.\n", + "Search for each package individually, then produce a prioritized remediation report.\n", + "\n", + "Dependencies:\n", + "{dep_list}\n", + "\"\"\"\n", + "\n", + "print(\"Starting CVE scan...\")\n", + "print(\"-\" * 60)\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.3, max_tokens=2048)\n", + " agent = Agent(model=model, tools=tools, system_prompt=CVE_SYSTEM_PROMPT)\n", + " response = agent(scan_prompt)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"CVE SCAN REPORT\")\n", + "print(\"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Scan a Custom requirements.txt\n", + "\n", + "Pass the content of your actual `requirements.txt` file to scan your real dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To scan your own requirements.txt:\n", + "# with open(\"path/to/your/requirements.txt\") as f:\n", + "# requirements_content = f.read()\n", + "\n", + "# custom_prompt = f\"\"\"Scan these Python dependencies for CVEs:\\n\\n{requirements_content}\"\"\"\n", + "# with mcp_client:\n", + "# tools = mcp_client.list_tools_sync()\n", + "# model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.3, max_tokens=2048)\n", + "# agent = Agent(model=model, tools=tools, system_prompt=CVE_SYSTEM_PROMPT)\n", + "# response = agent(custom_prompt)\n", + "\n", + "print(\"Uncomment the cells above to scan your own requirements.txt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you built a security scanning agent that:\n", + "- Issues one targeted search per dependency (multi-turn pattern)\n", + "- Finds CVEs that postdate any model training cutoff\n", + "- Produces a prioritized remediation report\n", + "\n", + "The key insight: **each search is informed by the dependency from the parsed list** — this is the \"search for each item\" multi-turn pattern.\n", + "\n", + "**Next**: See `02-earnings-brief.ipynb` for the chained search pattern where each result informs the next query." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md new file mode 100644 index 000000000..72c4f815d --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md @@ -0,0 +1,62 @@ +# CVE Scanner — Multi-turn Security Vulnerability Search + +## Overview + +In this example you will build a security agent that scans a list of Python dependencies for known vulnerabilities using live web search. The agent issues one search per package, synthesizes severity across all results, and produces a prioritized remediation report. + +**Why live search is critical here**: CVE databases update daily. A newly published exploit or patch changes the risk profile of a dependency within hours. Training data is always stale for this. + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Agentic framework | Strands Agents | +| LLM model | Anthropic Claude Sonnet 4 | +| Tutorial vertical | Security / DevOps | +| Example complexity | Intermediate | +| SDK used | boto3, strands-agents | + +### What the Agent Does + +``` +Input: requirements.txt contents + │ + ▼ +┌────────────────────────────────┐ +│ For each dependency: │ +│ 1. Search "CVE " │ +│ 2. Search " latest │ +│ version security patch" │ +│ 3. Extract severity + fix │ +└────────────────────────────────┘ + │ + ▼ +Output: Prioritized table + CRITICAL requests 2.28.0 → CVE-2024-XXXX, upgrade to 2.32.3 + HIGH urllib3 1.26.5 → CVE-2024-YYYY, upgrade to 2.2.2 + ... +``` + +### Multi-turn Search Pattern + +The agent runs a search loop driven by the dependency list: +1. Parse the dependency list into packages and versions +2. For each package: search for CVEs, then search for the current patched version +3. Aggregate results and score by CVSS severity +4. Generate a remediation report sorted by risk + +Each search query is informed by the package name and version from the previous parse step — this is the core multi-turn pattern. + +## Notebook + +→ [`01-cve-scanner.ipynb`](01-cve-scanner.ipynb) + +## Prerequisites + +Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. + +```bash +pip install -r requirements.txt +``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt new file mode 100644 index 000000000..1d8cae582 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt @@ -0,0 +1,4 @@ +boto3 +mcp +requests +strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb new file mode 100644 index 000000000..40a08e675 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Earnings Brief Agent — Chained Multi-turn Research\n", + "\n", + "## Overview\n", + "\n", + "This notebook builds a financial research agent that, given a stock ticker, automatically searches for analyst estimates, recent competitor results, and sector news — then generates a structured pre-earnings call brief.\n", + "\n", + "**Why live search is critical here**: Analyst consensus, guidance revisions, and competitor earnings are filed days before the call. This information does not exist in any model's training data.\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Agentic framework | Strands Agents |\n", + "| LLM model | Anthropic Claude Sonnet 4 |\n", + "| Tutorial vertical | Finance |\n", + "| Example complexity | Intermediate |\n", + "| SDK used | boto3, strands-agents |\n", + "\n", + "### Chained Search Pattern\n", + "\n", + "```\n", + "Input: Ticker (e.g., \"AMZN\")\n", + " │\n", + " ▼\n", + "Search 1: Analyst EPS + revenue estimates\n", + " │ results inform → what consensus expects\n", + " ▼\n", + "Search 2: Guidance revisions since last quarter\n", + " │ results inform → any surprise risk\n", + " ▼\n", + "Search 3: Top competitors' recent earnings\n", + " │ results inform → sector context\n", + " ▼\n", + "Search 4: Macro / sector news this quarter\n", + " │\n", + " ▼\n", + "Output: Structured pre-earnings brief\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock:InvokeModel\",\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configuration and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from mcp.client.streamable_http import streamablehttp_client\n", + "from strands import Agent\n", + "from strands.models import BedrockModel\n", + "from strands.tools.mcp.mcp_client import MCPClient\n", + "\n", + "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", + "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", + "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", + "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", + "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", + "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", + "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", + "\n", + "\n", + "def get_token():\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "\n", + "def create_transport():\n", + " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", + "\n", + "\n", + "print(\"Configuration loaded ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Choose a Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Change this to any US-listed company ticker\n", + "TICKER = \"AMZN\"\n", + "COMPANY_NAME = \"Amazon\" # Used in search queries for better results\n", + "\n", + "print(f\"Generating pre-earnings brief for: {TICKER} ({COMPANY_NAME})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define the Earnings Brief Agent\n", + "\n", + "The system prompt instructs the agent to follow the four-search research sequence and produce a structured output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EARNINGS_SYSTEM_PROMPT = \"\"\"You are a financial research analyst with access to real-time web search.\n", + "\n", + "When given a stock ticker and company name, produce a pre-earnings call brief by:\n", + "\n", + "RESEARCH SEQUENCE (do these searches in order):\n", + "1. Search: \" earnings estimate analyst consensus Q 2026\"\n", + "2. Search: \" guidance revision outlook 2026\"\n", + "3. Search: \" competitors earnings results recent\"\n", + "4. Search: \" sector macro headwinds tailwinds 2026\"\n", + "\n", + "CRITICAL RULES:\n", + "- Keep each search query under 200 characters\n", + "- Each search informs the next — note what you learned before issuing the next query\n", + "- Cite URLs for every claim\n", + "\n", + "OUTPUT FORMAT (use markdown):\n", + "\n", + "## Pre-Earnings Brief: — \n", + "\n", + "### Analyst Consensus\n", + "\n", + "\n", + "### Guidance and Surprise Risk\n", + "\n", + "\n", + "### Competitor Context\n", + "\n", + "\n", + "### Macro and Sector Factors\n", + "\n", + "\n", + "### What to Watch\n", + "<3 key metrics or statements to monitor during the call>\n", + "\n", + "### Sources\n", + "\n", + "\"\"\"\n", + "\n", + "mcp_client = MCPClient(create_transport)\n", + "print(\"Earnings brief agent ready ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Generate the Pre-Earnings Brief\n", + "\n", + "The agent will run 4 chained searches — each one informed by the previous result — then synthesize the brief." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "brief_prompt = f\"\"\"Generate a pre-earnings call brief for {TICKER} ({COMPANY_NAME}).\n", + "\n", + "Follow the research sequence: analyst estimates → guidance revisions → competitor context → macro factors.\n", + "Produce the full structured brief with cited sources.\n", + "\"\"\"\n", + "\n", + "print(f\"Researching {TICKER}...\")\n", + "print(\"-\" * 60)\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", + " agent = Agent(model=model, tools=tools, system_prompt=EARNINGS_SYSTEM_PROMPT)\n", + " response = agent(brief_prompt)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(f\"PRE-EARNINGS BRIEF: {TICKER}\")\n", + "print(\"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Try Another Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Change these to generate a brief for a different company\n", + "TICKER_2 = \"TSLA\"\n", + "COMPANY_2 = \"Tesla\"\n", + "\n", + "brief_prompt_2 = f\"\"\"Generate a pre-earnings call brief for {TICKER_2} ({COMPANY_2}).\n", + "Follow the research sequence and produce the full structured brief with cited sources.\n", + "\"\"\"\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", + " agent = Agent(model=model, tools=tools, system_prompt=EARNINGS_SYSTEM_PROMPT)\n", + " response = agent(brief_prompt_2)\n", + "\n", + "print(f\"\\n{'=' * 60}\")\n", + "print(f\"PRE-EARNINGS BRIEF: {TICKER_2}\")\n", + "print(\"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you built a financial research agent that:\n", + "- Runs 4 chained searches where each result shapes the next query\n", + "- Synthesizes analyst data, competitive context, and macro factors\n", + "- Produces a structured brief with cited sources\n", + "\n", + "The key insight: **the output of each search is used to form a better next query** — this is the chained multi-turn pattern.\n", + "\n", + "**Next**: See `03-iterative-research.ipynb` for the generalized search → reflect → search loop that underpins both this example and the CVE scanner." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md new file mode 100644 index 000000000..c7ae55b5f --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md @@ -0,0 +1,63 @@ +# Earnings Brief Agent — Chained Multi-turn Research + +## Overview + +In this example you will build a financial research agent that, given a company ticker, automatically searches for analyst estimates, recent competitor results, and sector news — then generates a structured pre-earnings call brief. + +**Why live search is critical here**: Analyst consensus, guidance revisions, and competitor earnings are filed days before the call. This information does not exist in any model's training data. + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Agentic framework | Strands Agents | +| LLM model | Anthropic Claude Sonnet 4 | +| Tutorial vertical | Finance | +| Example complexity | Intermediate | +| SDK used | boto3, strands-agents | + +### What the Agent Does + +``` +Input: Ticker symbol (e.g., "AMZN") + earnings date + │ + ▼ +┌─────────────────────────────────────────────┐ +│ Search 1: Analyst EPS and revenue estimates │ +│ Search 2: Guidance revisions since last Q │ +│ Search 3: Top 2 competitors' recent results │ +│ Search 4: Macro / sector news this quarter │ +└─────────────────────────────────────────────┘ + │ + ▼ +Output: Structured brief + ## Pre-Earnings Brief: AMZN — Q2 2026 + ### Analyst Consensus + ### Key Risks and Tailwinds + ### Competitor Context + ### What to Watch +``` + +### Chained Search Pattern + +Each search is informed by the results of the previous one: +1. Analyst consensus → identifies what the market expects +2. Guidance revision search → narrows to surprises vs. prior guidance +3. Competitor search → adds comparative context +4. Macro/sector search → explains external factors the model might miss + +The agent reflects after each search and decides what to look for next — this is the chained multi-turn pattern. + +## Notebook + +→ [`02-earnings-brief.ipynb`](02-earnings-brief.ipynb) + +## Prerequisites + +Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. + +```bash +pip install -r requirements.txt +``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt new file mode 100644 index 000000000..1d8cae582 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt @@ -0,0 +1,4 @@ +boto3 +mcp +requests +strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb new file mode 100644 index 000000000..34cf5092a --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iterative Research Agent — Search → Reflect → Search Loop\n", + "\n", + "## Overview\n", + "\n", + "This notebook builds a research agent that answers complex questions through an explicit multi-turn loop: plan, search, reflect on gaps, search again — repeating until confident. This is the foundational pattern behind the CVE scanner and earnings brief examples.\n", + "\n", + "By making the loop explicit with a configurable max-iterations and confidence threshold, you can tune it for depth vs. cost.\n", + "\n", + "### Tutorial Details\n", + "\n", + "| Information | Details |\n", + "|:------------|:--------|\n", + "| Tutorial type | Interactive (Jupyter Notebook) |\n", + "| AgentCore components | AgentCore Gateway |\n", + "| Agentic framework | Strands Agents |\n", + "| LLM model | Anthropic Claude Sonnet 4 |\n", + "| Tutorial vertical | Cross-vertical |\n", + "| Example complexity | Advanced |\n", + "| SDK used | boto3, strands-agents |\n", + "\n", + "### The Research Loop\n", + "\n", + "```\n", + "Question\n", + " │\n", + " ▼\n", + "PLAN: Break into sub-questions\n", + " │\n", + " ▼\n", + "SEARCH: Execute highest-priority query ◀──────────────┐\n", + " │ │\n", + " ▼ │\n", + "REFLECT: What gaps remain? │\n", + " ├── Gaps found → refine query, iterate (max N) ────┘\n", + " └── Confident → synthesize final answer\n", + "```\n", + "\n", + "### When Single-Shot Search Isn't Enough\n", + "\n", + "Use this pattern when:\n", + "- The question requires comparing multiple sources\n", + "- Initial results reveal follow-up questions\n", + "- You need to reconcile conflicting information\n", + "- The topic has multiple dimensions that all matter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", + "\n", + "### Required IAM permissions\n", + "\n", + "~~~json\n", + "{\n", + " \"Effect\": \"Allow\",\n", + " \"Action\": \"bedrock:InvokeModel\",\n", + " \"Resource\": \"*\"\n", + "}\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Install Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configuration and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from mcp.client.streamable_http import streamablehttp_client\n", + "from strands import Agent\n", + "from strands.models import BedrockModel\n", + "from strands.tools.mcp.mcp_client import MCPClient\n", + "\n", + "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", + "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", + "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", + "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", + "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", + "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", + "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", + "\n", + "\n", + "def get_token():\n", + " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", + " resp = requests.post(\n", + " url,\n", + " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", + " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", + " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", + " )\n", + " resp.raise_for_status()\n", + " return resp.json()[\"access_token\"]\n", + "\n", + "\n", + "def create_transport():\n", + " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", + "\n", + "\n", + "# Tunable parameters\n", + "MAX_SEARCH_ITERATIONS = 4 # Maximum number of search rounds before forcing synthesis\n", + "CONFIDENCE_THRESHOLD = 0.8 # The agent uses this conceptually — expressed in the prompt\n", + "\n", + "print(f\"Max iterations : {MAX_SEARCH_ITERATIONS}\")\n", + "print(f\"Configuration : ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Define the Iterative Research Agent\n", + "\n", + "The system prompt explicitly encodes the plan → search → reflect → search loop with a stopping condition." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ITERATIVE_RESEARCH_PROMPT = f\"\"\"You are a thorough research analyst with access to real-time web search.\n", + "\n", + "RESEARCH LOOP (repeat up to {MAX_SEARCH_ITERATIONS} iterations):\n", + "\n", + "STEP 1 — PLAN:\n", + " Break the question into sub-questions. List them in priority order.\n", + " Identify what you can answer confidently vs. what requires search.\n", + "\n", + "STEP 2 — SEARCH:\n", + " Execute the highest-priority unanswered sub-question as a web search.\n", + " Keep queries under 200 characters.\n", + " After each search, note: \"Learned: . Remaining gaps: .\"\n", + "\n", + "STEP 3 — REFLECT:\n", + " Ask yourself: \"Do I have enough to answer the original question confidently?\"\n", + " - If NO and iterations remain: formulate the next search based on gaps, go to STEP 2\n", + " - If YES or iterations exhausted: go to STEP 4\n", + "\n", + "STEP 4 — SYNTHESIZE:\n", + " Write a comprehensive answer that:\n", + " - Directly addresses the original question\n", + " - Integrates findings from all search rounds\n", + " - Cites URLs for all factual claims\n", + " - Notes any remaining uncertainties\n", + "\n", + "SHOW YOUR WORK: Make the plan, search queries, and reflections visible in your response.\n", + "This helps users understand the research process and verify the results.\n", + "\"\"\"\n", + "\n", + "mcp_client = MCPClient(create_transport)\n", + "print(\"Iterative research agent ready ✓\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Research a Complex Question\n", + "\n", + "This question requires multiple search angles — a single query can't answer it reliably." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RESEARCH_QUESTION = \"\"\"\n", + "What are the key differences between Claude Sonnet 4 and GPT-4.5 for enterprise code generation use cases,\n", + "and which one has better community adoption among developers as of 2026?\n", + "\"\"\"\n", + "\n", + "print(f\"Research question: {RESEARCH_QUESTION.strip()}\")\n", + "print(\"-\" * 60)\n", + "print(\"Running iterative research loop...\")\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", + " agent = Agent(model=model, tools=tools, system_prompt=ITERATIVE_RESEARCH_PROMPT)\n", + " response = agent(RESEARCH_QUESTION.strip())\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"RESEARCH REPORT\")\n", + "print(\"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Try Questions That Require Multiple Search Angles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "multi_angle_questions = [\n", + " \"What regulatory changes in the EU in 2026 affect AI startup compliance, and what are the key deadlines?\",\n", + " \"How has the adoption of Model Context Protocol changed agent development practices in 2025-2026?\",\n", + " \"What are the current best practices for deploying LLM agents in production, based on recent industry reports?\"\n", + "]\n", + "\n", + "# Run one example (uncomment others as needed)\n", + "selected_question = multi_angle_questions[0]\n", + "\n", + "print(f\"Question: {selected_question}\")\n", + "print(\"-\" * 60)\n", + "\n", + "with mcp_client:\n", + " tools = mcp_client.list_tools_sync()\n", + " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", + " agent = Agent(model=model, tools=tools, system_prompt=ITERATIVE_RESEARCH_PROMPT)\n", + " response = agent(selected_question)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "if hasattr(response, \"message\"):\n", + " for block in response.message.get(\"content\", []):\n", + " if block.get(\"text\"):\n", + " print(block[\"text\"])\n", + "else:\n", + " print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Tuning the Loop\n", + "\n", + "Adjust `MAX_SEARCH_ITERATIONS` at the top of this notebook to control depth vs. latency/cost:\n", + "\n", + "| Setting | Effect | Use when |\n", + "|:--------|:-------|:---------|\n", + "| 2 | Fast, shallow | Simple factual questions |\n", + "| 4 (default) | Balanced | Most research tasks |\n", + "| 6+ | Deep, thorough | Complex comparative analysis |\n", + "\n", + "Each additional iteration costs one WebSearch call and one LLM call." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook you built a research agent that:\n", + "- Plans its research before searching\n", + "- Reflects after each search to identify gaps\n", + "- Issues targeted follow-up queries until confident\n", + "- Synthesizes findings with cited sources\n", + "\n", + "This is the foundational multi-turn pattern. Both the CVE scanner (search per item) and earnings brief (chained search) are specializations of this loop, tuned for their specific data structures.\n", + "\n", + "### Summary: Three Multi-turn Patterns\n", + "\n", + "| Pattern | Trigger | Example |\n", + "|:--------|:--------|:--------|\n", + "| Search per item | Fixed list of inputs | CVE scanner |\n", + "| Chained search | Each result shapes next query | Earnings brief |\n", + "| Iterative (this) | Reflect on gaps, search until confident | General deep research |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md new file mode 100644 index 000000000..e187e650f --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md @@ -0,0 +1,73 @@ +# Iterative Research Agent — Search → Reflect → Search Loop + +## Overview + +In this example you will build a research agent that answers complex questions by planning its research, executing searches, reflecting on what it still doesn't know, and issuing follow-up queries — repeating until it can answer with confidence. + +This is a technique demonstration. The CVE Scanner and Earnings Brief examples both use this underlying pattern; this notebook makes it explicit and reusable. + +### Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebook) | +| AgentCore components | AgentCore Gateway | +| Agentic framework | Strands Agents | +| LLM model | Anthropic Claude Sonnet 4 | +| Tutorial vertical | Cross-vertical | +| Example complexity | Advanced | +| SDK used | boto3, strands-agents | + +### The Research Loop + +``` +Question + │ + ▼ +┌──────────────────────────────────────────┐ +│ Plan: What do I need to know? │ +│ → Break question into sub-questions │ +└──────────────┬───────────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────┐ +│ Search: Execute highest-priority query │◀──┐ +└──────────────┬───────────────────────────┘ │ + │ │ + ▼ │ +┌──────────────────────────────────────────┐ │ +│ Reflect: What gaps remain? │ │ +│ → If gaps exist: refine query, repeat ──┼───┘ +│ → If confident: synthesize │ +└──────────────┬───────────────────────────┘ + │ + ▼ + Final answer with citations +``` + +### Why This Pattern Matters + +Single-shot search works for simple factual queries. For questions that require: +- Comparing multiple sources +- Reconciling conflicting information +- Drilling into details from a high-level result + +...you need the reflect-and-refine loop. This notebook teaches that loop explicitly with configurable max iterations and a confidence threshold. + +### Example Questions + +- "What are the key differences between Claude 4 and GPT-4.5 for code generation?" +- "What regulatory changes in 2026 affect EU-based AI startups?" +- "What happened to Silicon Valley Bank and what changed in US banking regulation after?" + +## Notebook + +→ [`03-iterative-research.ipynb`](03-iterative-research.ipynb) + +## Prerequisites + +Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. + +```bash +pip install -r requirements.txt +``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/images/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt new file mode 100644 index 000000000..1d8cae582 --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt @@ -0,0 +1,4 @@ +boto3 +mcp +requests +strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md new file mode 100644 index 000000000..f902c4fef --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md @@ -0,0 +1,29 @@ +# Advanced Web Search Examples + +## Overview + +These examples go beyond single-shot search to demonstrate patterns where live web data is genuinely critical — not optional. Each example shows a realistic use case where stale training data would break the outcome. + +## Examples + +| Folder | Use Case | Key Pattern | +|:-------|:---------|:------------| +| [`01-cve-scanner/`](01-cve-scanner/) | Security vulnerability scan across a dependency list | Multi-turn: one search per package, chained reasoning | +| [`02-earnings-brief/`](02-earnings-brief/) | Pre-earnings call research brief | Multi-turn: analyst estimates → competitors → sector news → synthesis | +| [`03-iterative-research/`](03-iterative-research/) | Deep Q&A via search → reflect → search loop | Technique demo: explicit multi-turn research pattern | + +## When to Use Each Pattern + +### CVE Scanner — "search for each item in a list" +The agent receives a list of dependencies, searches for CVEs for each one, and produces a prioritized remediation report. The loop is driven by the input list, and each search result is independent. + +### Earnings Brief — "chained research where each search informs the next" +The agent starts with analyst consensus, finds gaps, searches competitors, then searches for macro context. The queries are generated dynamically — each result shapes what to search for next. + +### Iterative Research — "search until confident" +The agent explicitly plans its research, executes searches, reflects on gaps, and issues follow-up queries until it can answer with confidence. This is the foundational pattern the other two use cases build on. + +## Prerequisites + +All examples require a running AgentCore Gateway with Web Search connector target. +Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md new file mode 100644 index 000000000..00f6d3dca --- /dev/null +++ b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md @@ -0,0 +1,127 @@ +# Amazon Bedrock AgentCore Web Search Tool + +## Overview + +Amazon Bedrock AgentCore Web Search Tool exposes live web search as a fully managed, MCP-compliant tool through AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no search APIs to provision, no infrastructure to manage. + +This tutorial series covers setup, basic agent integration, and advanced multi-turn patterns where real-time web data is genuinely critical to the outcome. + +
+ +
+ +## How It Works + +The Web Search Tool uses the **connector** target type in AgentCore Gateway. You specify `connectorId: "web-search"` and the Gateway handles schema management, endpoint resolution, and outbound authentication automatically. + +``` +Your Agent (Strands / LangChain / any MCP client) + │ + │ OAuth Bearer token (inbound auth) + ▼ +AgentCore Gateway (MCP endpoint) + │ + │ IAM role (outbound auth — automatic) + ▼ +Web Search Backend + │ + ▼ +Structured results: { text, url, title, publishedDate } +``` + +**Inbound auth**: Amazon Cognito with `client_credentials` flow — your agent gets a token and passes it to the Gateway. + +**Outbound auth**: Fully automatic — the Gateway's IAM role authenticates to the Web Search backend. Nothing to configure. + +
+ +
+ +## Key Capabilities + +- **Real-time information** — live web results with titles, URLs, snippets, and publication dates +- **Zero infrastructure** — no search APIs, no hosting, no scaling configuration +- **Framework agnostic** — Strands Agents, LangChain, LangGraph, or any MCP client +- **MCP-native** — tool discovery and invocation via standard `tools/list` and `tools/call` + +## Tutorial Details + +| Information | Details | +|:------------|:--------| +| Tutorial type | Interactive (Jupyter Notebooks) | +| AgentCore components | AgentCore Gateway | +| Gateway target type | Connector (`web-search`) | +| Agentic frameworks | Strands Agents, LangChain + LangGraph | +| Inbound auth | Amazon Cognito (`client_credentials` flow) | +| Outbound auth | Automatic (Gateway IAM role) | +| LLM model | Anthropic Claude Sonnet 4 | +| Region | us-east-1 (Web Search connector availability) | +| SDK used | boto3 | + +## Tutorials Overview + +### Foundation Tutorials + +Run these in order — each tutorial builds on the previous one. + +| Tutorial | What You'll Learn | +|:---------|:-----------------| +| [01 — Gateway Setup and Raw MCP](01-gateway-setup-and-raw-mcp/) | Create IAM role, Cognito, Gateway, and Web Search target. Verify with direct MCP calls. | +| [02 — Web Search with Strands](02-web-search-with-strands/) | Full agent loop using Strands Agents and Claude Sonnet 4. | +| [03 — Web Search with LangChain](03-web-search-with-langchain/) | Same integration using LangChain + LangGraph. | + +### Advanced Examples + +These examples require the Gateway from tutorial 01. They demonstrate patterns where live web search is genuinely critical — not optional. + +| Example | Use Case | Pattern | +|:--------|:---------|:--------| +| [CVE Scanner](04-advanced-examples/01-cve-scanner/) | Scan dependencies for recent security vulnerabilities | Multi-turn: one search per package | +| [Earnings Brief](04-advanced-examples/02-earnings-brief/) | Pre-earnings call research brief for any ticker | Chained: each search informs the next | +| [Iterative Research](04-advanced-examples/03-iterative-research/) | Deep Q&A via search → reflect → search loop | Technique: explicit multi-turn pattern | + +## Prerequisites + +- AWS account allowlisted for the Web Search Tool private beta +- Python 3.10+ and Jupyter Notebook (or JupyterLab) +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- Access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1 + +```bash +pip install -r requirements.txt +``` + +> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. + +## Quick Start + +```bash +# Step 1: Set up Gateway (run once — reused by all other tutorials) +jupyter notebook 01-gateway-setup-and-raw-mcp/01-web-search-gateway-setup.ipynb + +# Step 2: Try with Strands +jupyter notebook 02-web-search-with-strands/02-web-search-strands-agent.ipynb + +# Step 3: Try with LangChain +jupyter notebook 03-web-search-with-langchain/03-web-search-langchain-agent.ipynb + +# Advanced: CVE Scanner +jupyter notebook 04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb + +# Advanced: Earnings Brief +jupyter notebook 04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb + +# Advanced: Iterative Research Pattern +jupyter notebook 04-advanced-examples/03-iterative-research/03-iterative-research.ipynb +``` + +## Files + +| Path | Description | +|:-----|:------------| +| `requirements.txt` | Shared Python dependencies | +| `images/` | Architecture diagrams | +| 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z3%+?B^VoAuRf3ct{uGK>eHWY^R(*Q+Z@eW%BJh&*qE6gL91k`QTSZ3cbiwWO|14** zXl;17_Q~EI8^1<}?m|Ei3}GFK13QWPNX0_$fMi}zB6#?pHXutE1YW zIV_}h3$M@W4h%HCFhlb{f_JH>{gnS-0x3E)iyvBpB~G?CX5EH*txJ#*)W-VH>8MU!28Tper`sWpN>>K32dHit_hUL{_iS zbkzK6{14;y|2gPaJ&=BC4(U@vS*U*`rx@waP(s3vEyz@~DS5_W<6v5T+G=F&XV1PBoEkB8WqKH&Itmi**+7R%aJp7X@PA6t=0.1.8 +mcp +requests +langchain +langchain-mcp-adapters +langchain-aws +langgraph diff --git a/06-workshops/05-AgentCore-tools/README.md b/06-workshops/05-AgentCore-tools/README.md index d30e601fc..cd0ee00fc 100644 --- a/06-workshops/05-AgentCore-tools/README.md +++ b/06-workshops/05-AgentCore-tools/README.md @@ -2,10 +2,11 @@ ## Overview Amazon Bedrock AgentCore Tools provide enterprise-grade capabilities that enhance AI agents' ability to perform complex -tasks securely and efficiently. This suite includes two primary tools: +tasks securely and efficiently. This suite includes three tools: -- Amazon Bedrock AgentCore Code Interpreter and +- Amazon Bedrock AgentCore Code Interpreter - Amazon Bedrock AgentCore Browser Tool +- Amazon Bedrock AgentCore Web Search Tool ## Amazon Bedrock AgentCore Code Interpreter @@ -48,7 +49,32 @@ tasks securely and efficiently. This suite includes two primary tools: - Large-scale data processing and monitoring - Secure code execution for AI agents in enterprise settings +## Amazon Bedrock AgentCore Web Search Tool + +### Key Features + +1. **Real-time Information Access**: Retrieve current web results with titles, URLs, snippets, and publication dates — no frozen training data. + +2. **Zero Infrastructure Management**: No search APIs to provision, no scaling to configure. Expose web search through AgentCore Gateway as a fully managed MCP connector. + +3. **Framework Agnostic**: Works with Strands Agents, LangChain, LangGraph, CrewAI, or any MCP-compatible client. + +### Benefits + +- **Grounded Responses**: Agents cite live sources rather than hallucinating outdated facts. +- **Critical for Time-Sensitive Use Cases**: CVE scanning, earnings research, regulatory monitoring — all require data that doesn't exist in training sets. +- **MCP-Native Discovery**: Agents discover and invoke the tool via standard `tools/list` and `tools/call` — no custom integration code. + +## Use Cases + +- Complex data analysis and visualization in secure environments +- Automated web interactions for form filling, data extraction, and multi-step processes +- Large-scale data processing and monitoring +- Secure code execution for AI agents in enterprise settings +- Real-time web grounding for agents that need current information + ## Tutorials Overview 1. [Amazon Bedrock AgentCore Code Interpreter](01-Agent-Core-code-interpreter) 2. [Amazon Bedrock AgentCore Browser Tool](02-Agent-Core-browser-tool) +3. [Amazon Bedrock AgentCore Web Search Tool](03-Agent-Core-web-search) From cf286d243d0ef8ac98183880b7d1b3f5376ad101 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Fri, 5 Jun 2026 17:41:03 -0400 Subject: [PATCH 03/21] feat: add deep-research-agent with auto-provisioning and search privacy notices - Add deep-research-agent use case with iterative Plan/Search/Reflect/Synthesize loop - Add gateway_setup.py with auto-detect/prompt/provision flow (no hard prerequisites) - Add search privacy callout to all 03-web-search README files - Fix model ID default to use cross-region inference profile - Add user-friendly error handling for auth failures --- .../03-web-search/01-setup-gateway/README.md | 2 + .../03-web-search/02-raw-mcp/README.md | 2 + .../03-web-search/03-strands-agent/README.md | 6 +- .../04-langchain-agent/README.md | 7 +- .../03-web-search/README.md | 4 +- 02-use-cases/deep-research-agent/README.md | 258 ++++++++ .../deep_research_agent.py | 381 ++++++++++++ .../deep-research-agent/gateway_setup.py | 583 ++++++++++++++++++ .../deep-research-agent/requirements.txt | 6 + 9 files changed, 1243 insertions(+), 6 deletions(-) create mode 100644 02-use-cases/deep-research-agent/README.md create mode 100644 02-use-cases/deep-research-agent/deep_research_agent.py create mode 100644 02-use-cases/deep-research-agent/gateway_setup.py create mode 100644 02-use-cases/deep-research-agent/requirements.txt diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md index 3196208d0..0c97c7185 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -4,6 +4,8 @@ This script creates all the infrastructure needed to expose the Web Search Tool through an AgentCore Gateway. After running it, you'll have a fully functional MCP endpoint that any agent framework can connect to. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. + ``` ┌──────────────────┐ ┌─────────────────────────────────────┐ │ setup_gateway │────────▶│ AWS Resources Created: │ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md index aad3b9048..73182422b 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -4,6 +4,8 @@ This demo calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target infrastructure are working correctly. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. + ``` ┌──────────────────┐ tools/list ┌─────────────────────────────────┐ │ raw_mcp_call.py │ ──────────────▶ │ AgentCore Gateway │ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md index 40a829661..28060b839 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md @@ -4,6 +4,8 @@ This demo shows the complete agent integration: a Strands agent automatically discovers and invokes the Web Search Tool to answer real-time questions with cited sources. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. + ``` ┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ │ User │ ─────────────────────────────▶ │ Strands Agent │ @@ -63,7 +65,7 @@ pip install -r ../requirements.txt Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. -Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1. +Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. ## Usage @@ -82,7 +84,7 @@ python web_search_strands.py --query "Current price of Bitcoin" { "Effect": "Allow", "Action": "bedrock:InvokeModel", - "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-20250514-v1:0" + "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-6" } ``` diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md index e289f69b5..cb69c8ef6 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md @@ -4,6 +4,7 @@ This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. ``` ┌────────────┐ "Latest AWS announcements?" ┌──────────────────────────┐ │ User │ ─────────────────────────────▶ │ LangChain Agent │ @@ -49,7 +50,7 @@ from langchain_aws import ChatBedrockConverse from langgraph.prebuilt import create_react_agent model = ChatBedrockConverse( - model="us.anthropic.claude-sonnet-4-20250514-v1:0", + model="us.anthropic.claude-sonnet-4-6", region_name="us-east-1", ) agent = create_react_agent(model, tools=tools) @@ -68,7 +69,7 @@ pip install -r ../requirements.txt Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. -Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1. +Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. ## Usage @@ -87,7 +88,7 @@ python web_search_langchain.py --query "Python 3.13 new features" { "Effect": "Allow", "Action": "bedrock:InvokeModel", - "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-20250514-v1:0" + "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-6" } ``` diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index 3256feaf8..ff094209a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -4,6 +4,8 @@ [Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. + ![Web Search Tool Architecture](images/tutorial-architecture.png) ## How the Web Search Tool Works @@ -132,7 +134,7 @@ python 05-cleanup/cleanup.py --gateway-id --user-pool-id --role-name < ## Shared Utilities -Demos 02–04 share authentication and agent utilities in `utils/`: +Demos 02–04 share utilities in `utils/`: ```python from utils.gateway_auth import get_oauth_token, create_streamable_http_transport diff --git a/02-use-cases/deep-research-agent/README.md b/02-use-cases/deep-research-agent/README.md new file mode 100644 index 000000000..2839b0c05 --- /dev/null +++ b/02-use-cases/deep-research-agent/README.md @@ -0,0 +1,258 @@ +# Deep Research Agent + +An intelligent research agent powered by Amazon Bedrock AgentCore and Claude Sonnet 4 that answers complex, multi-faceted questions through an iterative Plan → Search → Reflect → Synthesize loop. + +## Overview + +Single-shot web search works for simple factual queries. For questions that require comparing multiple sources, reconciling conflicting information, or drilling into details revealed by earlier results, you need a reflect-and-refine loop. The Deep Research Agent makes that loop explicit and configurable. + +``` +Question + │ + ▼ +┌──────────────────────────────────────────┐ +│ PLAN: Break into prioritised │ +│ sub-questions │ +└──────────────┬───────────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────┐ +│ SEARCH: Execute highest-priority query │◀──┐ +└──────────────┬───────────────────────────┘ │ + │ │ + ▼ │ +┌──────────────────────────────────────────┐ │ +│ REFLECT: What gaps remain? │ │ +│ → Gaps found: refine query, repeat ─────┼───┘ +│ → Confident: synthesize │ +└──────────────┬───────────────────────────┘ + │ + ▼ + Comprehensive answer with citations +``` + +| Information | Details | +|:------------|:--------| +| Use case type | Research / Question answering | +| Agent type | Single agent | +| AgentCore components | AgentCore Gateway, AgentCore Runtime | +| Agentic framework | Strands Agents | +| LLM model | Anthropic Claude Sonnet 4 | +| Use case vertical | Cross-vertical | +| Example complexity | Intermediate | +| SDK used | bedrock-agentcore, strands-agents, mcp | + +## When to Use This Pattern + +| Scenario | Single-shot search | Deep Research Agent | +|:---------|:-------------------|:--------------------| +| Simple factual lookup | ✅ Sufficient | Overkill | +| Multi-source comparison | ❌ Incomplete | ✅ Ideal | +| Conflicting information | ❌ Misses nuance | ✅ Reconciles | +| Follow-up questions emerge | ❌ Single pass | ✅ Iterates | +| Complex regulatory / technical topics | ❌ Shallow | ✅ Deep | + +## Example Questions + +- "What are the key differences between Claude Sonnet 4 and GPT-4.5 for enterprise code generation, and which has stronger developer community adoption?" +- "What regulatory changes in the EU in 2026 affect AI startup compliance, and what are the key deadlines?" +- "How has the adoption of Model Context Protocol changed agent development practices in 2025–2026?" +- "What are the current best practices for deploying LLM agents in production, based on recent industry reports?" +- "What are the trade-offs between RAG and fine-tuning for enterprise LLM applications?" + +## Architecture + +``` +┌─────────────┐ Research question ┌──────────────────────────────────┐ +│ User / │ ───────────────────▶ │ Deep Research Agent │ +│ Client │ │ (Claude Sonnet 4 via Strands) │ +│ │◀─────────────────── │ │ +│ │ Cited research │ Plan → Search → Reflect loop │ +└─────────────┘ report │ │ │ + └───────┼──────────────────────────┘ + │ MCP tools/call + ▼ + ┌──────────────────────────────────┐ + │ AgentCore Gateway │ + │ → Web Search Connector │ + │ → Real-time web results │ + └──────────────────────────────────┘ +``` + +## Features + +- **Configurable depth** — set `--max-iter` (or `DEEP_RESEARCH_MAX_ITER`) to tune depth vs. cost +- **Transparent reasoning** — plan, search queries, and reflections are visible in the output +- **Cited answers** — every factual claim is backed by a source URL +- **AgentCore Runtime** — production-ready hosting via `BedrockAgentCoreApp` +- **CLI + runtime modes** — run locally with `--query` or deploy to AgentCore Runtime + +## Tuning the Loop + +| `--max-iter` | Effect | Use when | +|:-------------|:-------|:---------| +| 2 | Fast, shallow | Simple factual questions | +| 4 (default) | Balanced | Most research tasks | +| 6+ | Deep, thorough | Complex comparative analysis | + +Each additional iteration costs one WebSearch call and one LLM call. + +## Prerequisites + +- AWS account with Amazon Bedrock enabled in **us-east-1** +- **Claude Sonnet 4** model access enabled in Bedrock (Bedrock Console → Model Access) +- AWS credentials with `bedrock:InvokeModel` permission +- **No Gateway pre-setup required** — the agent auto-detects or provisions one for you + +### IAM permissions for auto-provisioning (first run only) + +If no existing Gateway is found, the agent will offer to create one. This requires: + +``` +iam:CreateRole, iam:PutRolePolicy, iam:GetRole +cognito-idp:CreateUserPool, cognito-idp:CreateUserPoolDomain +cognito-idp:CreateResourceServer, cognito-idp:CreateUserPoolClient +cognito-idp:ListUserPools, cognito-idp:ListUserPoolClients +cognito-idp:DescribeUserPoolClient, cognito-idp:DescribeResourceServer +bedrock-agentcore:CreateGateway, bedrock-agentcore:GetGateway +bedrock-agentcore:CreateGatewayTarget, bedrock-agentcore:ListGatewayTargets +bedrock-agentcore:ListGateways +``` + +On subsequent runs, if you export the printed variables, only `bedrock:InvokeModel` is needed. + +## Quick Start + +### 1. Install dependencies + +```bash +cd 02-use-cases/deep-research-agent +pip install -r requirements.txt +``` + +### 2. Run locally + +```bash +# Interactive mode — auto-detects or provisions Gateway, then prompts for a question +python deep_research_agent.py + +# Direct question +python deep_research_agent.py --query "What are the trade-offs between RAG and fine-tuning for enterprise LLMs?" + +# Deep mode — 6 iterations for complex comparative questions +python deep_research_agent.py --query "Compare Claude Sonnet 4 and GPT-4.5 for code generation" --max-iter 6 +``` + +On first run without environment variables, the agent will: +1. Scan your account for an existing Gateway with a Web Search target +2. If found, reuse it automatically +3. If not found, prompt you to create one (~60 seconds) +4. Print `export` commands to skip this step on future runs + +### 3. (Optional) Pre-configure environment variables + +If you already have a Gateway or want to skip auto-detection: + +```bash +export AGENTCORE_GATEWAY_URL="https://..." +export COGNITO_DOMAIN="us-east-1xxxxxxxx" +export COGNITO_CLIENT_ID="..." +export COGNITO_CLIENT_SECRET="..." +export COGNITO_SCOPE="agentcore-websearch/invoke" +export AWS_DEFAULT_REGION="us-east-1" +``` + +### 4. Deploy to AgentCore Runtime + +For runtime deployments, environment variables **must** be pre-configured in the +container environment (no interactive provisioning in runtime mode): + +```bash +# The BedrockAgentCoreApp entrypoint makes this deployment-ready +python deep_research_agent.py # starts the runtime server when deployed +``` + +When deployed, invoke via the AgentCore Runtime API: + +```json +{ + "prompt": "What are the current best practices for deploying LLM agents in production?", + "max_iter": 4 +} +``` + +## Cleanup + +When you're done, remove all provisioned resources using the shared cleanup script: + +```bash +python ../../01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py \ + --gateway-id \ + --user-pool-id \ + --role-name +``` + +The gateway ID, role name, and user pool ID are printed during provisioning. After cleanup, unset environment variables: + +```bash +unset AGENTCORE_GATEWAY_URL COGNITO_DOMAIN COGNITO_CLIENT_ID COGNITO_CLIENT_SECRET COGNITO_SCOPE +``` + +## IAM Permissions + +### Caller (agent runtime) + +```json +{ + "Effect": "Allow", + "Action": "bedrock:InvokeModel", + "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-20250514-v1:0" +} +``` + +### Gateway authentication + +Gateway invocation is authorised via the Cognito OAuth token — no additional IAM permissions needed for the caller beyond `bedrock:InvokeModel`. + +### Auto-provisioning (first run only) + +See the Prerequisites section above for the full list of permissions needed to create Gateway infrastructure. These are only required once — after provisioning, the agent only needs `bedrock:InvokeModel`. + +## Files + +| File | Description | +|:-----|:------------| +| `deep_research_agent.py` | Main agent — Plan/Search/Reflect loop, AgentCore Runtime entrypoint, and CLI | +| `gateway_setup.py` | Auto-detection and provisioning of Gateway + Web Search infrastructure | +| `requirements.txt` | Python dependencies | +| `README.md` | This file | + +## How It Works + +### The Research Loop + +The system prompt encodes the loop directly. Claude: + +1. **Plans** — decomposes the question into ordered sub-questions and identifies what needs searching +2. **Searches** — calls the `WebSearch` tool with the highest-priority unanswered sub-question (queries kept under 200 characters for best results) +3. **Reflects** — after each result, explicitly notes what was learned and what gaps remain; decides whether to search again or synthesize +4. **Synthesizes** — once confident (or when `max_iter` is reached), writes a comprehensive answer with cited URLs and noted uncertainties + +### Gateway Integration + +The agent connects to AgentCore Gateway via MCP Streamable HTTP. The Gateway exposes the Web Search connector as a standard MCP `WebSearch` tool. Tool discovery (`tools/list`) and invocation (`tools/call`) happen automatically through the Strands `MCPClient`. + +### Auth Flow + +``` +deep_research_agent.py + └── get_oauth_token() + └── POST /oauth2/token → Cognito (client_credentials) + └── Bearer token attached to every MCP request → Gateway +``` + +## Related Resources + +- [`01-features/03-connect-your-agent-to-anything/03-web-search/`](../../01-features/03-connect-your-agent-to-anything/03-web-search/) — Gateway setup, raw MCP, and basic agent demos +- [`06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/`](../../06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/) — Jupyter Notebook version of this pattern (workshop format) +- [AgentCore Gateway documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) diff --git a/02-use-cases/deep-research-agent/deep_research_agent.py b/02-use-cases/deep-research-agent/deep_research_agent.py new file mode 100644 index 000000000..a8cd0a1a5 --- /dev/null +++ b/02-use-cases/deep-research-agent/deep_research_agent.py @@ -0,0 +1,381 @@ +""" +Deep Research Agent — Plan → Search → Reflect → Synthesize Loop. + +An intelligent research agent powered by Amazon Bedrock AgentCore and Claude Sonnet 4 +that answers complex, multi-faceted questions through an iterative research loop: + + 1. PLAN — break the question into prioritised sub-questions + 2. SEARCH — execute the highest-priority unanswered sub-question via WebSearch + 3. REFLECT — assess gaps; if unresolved and iterations remain, go back to SEARCH + 4. SYNTHESIZE — write a comprehensive, cited answer once confident + +Single-shot search fails for questions that require comparing multiple sources, +reconciling conflicting information, or drilling into details revealed by earlier +results. This agent makes the reflect-and-refine loop explicit and configurable. + +Prerequisites: + pip install -r requirements.txt + + Gateway + Web Search configuration is handled automatically: + - If environment variables are set, they are used directly + - If not, the agent scans for an existing Gateway in your account + - If none found, it offers to create one interactively + +Optional environment variables: + AGENTCORE_GATEWAY_URL — Gateway MCP endpoint (auto-detected if missing) + COGNITO_DOMAIN — Cognito domain prefix (auto-detected if missing) + COGNITO_CLIENT_ID — Cognito app client ID (auto-detected if missing) + COGNITO_CLIENT_SECRET — Cognito app client secret (auto-detected if missing) + COGNITO_SCOPE — OAuth scope string (auto-detected if missing) + BEDROCK_MODEL_ID — Bedrock inference profile ID or ARN + (defaults to us.anthropic.claude-sonnet-4-6) + DEEP_RESEARCH_MAX_ITER — Maximum search iterations per question (default: 4) + AWS_DEFAULT_REGION — AWS region (default: us-east-1) + +IAM permissions required: + bedrock:InvokeModel (for Claude Sonnet 4) + + provisioning permissions if auto-creating Gateway (see gateway_setup.py) + +Usage: + # Run interactively (auto-detects or provisions gateway, prompts for question) + python deep_research_agent.py + + # Pass a question directly + python deep_research_agent.py --query "What are the trade-offs between RAG and fine-tuning for enterprise LLMs?" + + # Increase depth for complex comparative questions + python deep_research_agent.py --query "..." --max-iter 6 +""" + +import argparse +import logging +import os + +import requests +from bedrock_agentcore.runtime import BedrockAgentCoreApp +from mcp.client.streamable_http import streamablehttp_client +from strands import Agent +from strands.models import BedrockModel +from strands.tools.mcp.mcp_client import MCPClient + +from gateway_setup import ensure_gateway + +# ── Configuration ────────────────────────────────────────────────────────────── + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") +MODEL_ID = os.getenv( + "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-6" +) +MAX_SEARCH_ITERATIONS = int(os.getenv("DEEP_RESEARCH_MAX_ITER", "4")) + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Initialize the AgentCore Runtime app +app = BedrockAgentCoreApp() + + +# ── Auth helpers ─────────────────────────────────────────────────────────────── + + +def get_oauth_token() -> str: + """Retrieve a fresh OAuth token from Cognito using client_credentials flow.""" + cognito_domain = os.environ.get("COGNITO_DOMAIN", "") + cognito_client_id = os.environ.get("COGNITO_CLIENT_ID", "") + cognito_client_secret = os.environ.get("COGNITO_CLIENT_SECRET", "") + cognito_scope = os.environ.get("COGNITO_SCOPE", "") + region = os.environ.get("AWS_DEFAULT_REGION", REGION) + + if not all([cognito_domain, cognito_client_id, cognito_client_secret]): + raise ValueError( + "Cognito credentials not configured. Run the agent interactively " + "to auto-provision, or set COGNITO_DOMAIN, COGNITO_CLIENT_ID, " + "and COGNITO_CLIENT_SECRET environment variables." + ) + url = f"https://{cognito_domain}.auth.{region}.amazoncognito.com/oauth2/token" + resp = requests.post( + url, + headers={"Content-Type": "application/x-www-form-urlencoded"}, + data={ + "grant_type": "client_credentials", + "client_id": cognito_client_id, + "client_secret": cognito_client_secret, + "scope": cognito_scope, + }, + ) + if resp.status_code != 200: + error_detail = resp.text + raise RuntimeError( + f"Authentication failed (HTTP {resp.status_code}). " + f"Cognito returned: {error_detail}\n" + f" Check your COGNITO_CLIENT_ID and COGNITO_CLIENT_SECRET are correct.\n" + f" Token URL: {url}" + ) + return resp.json()["access_token"] + + +def create_mcp_transport(): + """Create an authenticated MCP Streamable HTTP transport for the Gateway.""" + gateway_url = os.environ.get("AGENTCORE_GATEWAY_URL", "") + if not gateway_url: + raise ValueError( + "Gateway URL not configured. Run the agent interactively to " + "auto-provision, or set AGENTCORE_GATEWAY_URL environment variable." + ) + token = get_oauth_token() + return streamablehttp_client( + gateway_url, + headers={"Authorization": f"Bearer {token}"}, + ) + + +# ── System prompt ────────────────────────────────────────────────────────────── + + +def build_system_prompt(max_iterations: int) -> str: + """Build the deep-research system prompt with the configured iteration cap. + + Args: + max_iterations: Maximum number of search-reflect cycles before forcing synthesis. + + Returns: + System prompt string for the Strands agent. + """ + return f"""You are a thorough research analyst with access to real-time web search. + +RESEARCH LOOP (repeat up to {max_iterations} iterations): + +STEP 1 — PLAN: + Break the question into sub-questions. List them in priority order. + Identify what you can answer confidently vs. what requires search. + +STEP 2 — SEARCH: + Execute the highest-priority unanswered sub-question as a web search. + Keep queries under 200 characters. + After each search, note: "Learned: . Remaining gaps: ." + +STEP 3 — REFLECT: + Ask yourself: "Do I have enough to answer the original question confidently?" + - If NO and iterations remain: formulate the next search based on gaps, go to STEP 2 + - If YES or iterations exhausted: go to STEP 4 + +STEP 4 — SYNTHESIZE: + Write a comprehensive answer that: + - Directly addresses the original question + - Integrates findings from all search rounds + - Cites URLs for all factual claims + - Notes any remaining uncertainties + +SHOW YOUR WORK: Make the plan, search queries, and reflections visible in your +response. This helps users understand the research process and verify the results. +""" + + +# ── Agent factory ────────────────────────────────────────────────────────────── + + +def create_deep_research_agent(max_iterations: int = MAX_SEARCH_ITERATIONS) -> tuple: + """Create an MCPClient and a Strands deep-research agent. + + Returns the (mcp_client, agent) pair. The caller is responsible for using + mcp_client as a context manager. + + Args: + max_iterations: Maximum search-reflect cycles before forcing synthesis. + + Returns: + Tuple of (MCPClient, Agent). + """ + mcp_client = MCPClient(create_mcp_transport) + + with mcp_client: + tools = mcp_client.list_tools_sync() + logger.info("Discovered %d tool(s) from Gateway", len(tools)) + + model = BedrockModel( + model_id=MODEL_ID, + region_name=REGION, + temperature=0.5, + max_tokens=4096, + ) + agent = Agent( + model=model, + tools=tools, + system_prompt=build_system_prompt(max_iterations), + ) + + return mcp_client, agent + + +# ── Response extraction ──────────────────────────────────────────────────────── + + +def extract_text(response) -> str: + """Extract the text content from a Strands agent response.""" + if hasattr(response, "message"): + parts = [] + for block in response.message.get("content", []): + if block.get("text"): + parts.append(block["text"]) + return "\n".join(parts) + return str(response) + + +# ── AgentCore Runtime entrypoint ─────────────────────────────────────────────── + + +@app.entrypoint +def deep_research_runtime(payload): + """AgentCore Runtime handler — receives a payload and returns the research report. + + In runtime mode, environment variables MUST be pre-configured (no interactive + provisioning). The runtime expects AGENTCORE_GATEWAY_URL and Cognito vars to + be set in the container environment. + + Args: + payload: Dict containing the research question under any of: + 'prompt', 'query', 'message', or 'inputText'. + + Returns: + str: The full research report with citations. + """ + query = ( + payload.get("prompt") + or payload.get("query") + or payload.get("message") + or payload.get("inputText") + or payload.get("input") + ) + + if not query: + return "No research question provided. Include your question under the 'prompt' key." + + max_iter = int(payload.get("max_iter", MAX_SEARCH_ITERATIONS)) + logger.info("Deep research request: %s (max_iter=%d)", query, max_iter) + + # In runtime mode, ensure gateway config is available (non-interactive) + ensure_gateway(interactive=False) + + mcp_client = MCPClient(create_mcp_transport) + + with mcp_client: + tools = mcp_client.list_tools_sync() + model = BedrockModel( + model_id=MODEL_ID, + region_name=REGION, + temperature=0.5, + max_tokens=4096, + ) + agent = Agent( + model=model, + tools=tools, + system_prompt=build_system_prompt(max_iter), + ) + response = agent(query) + + return extract_text(response) + + +# ── CLI entrypoint ───────────────────────────────────────────────────────────── + + +def parse_args(): + parser = argparse.ArgumentParser( + description=( + "Deep Research Agent — iterative Plan/Search/Reflect/Synthesize " + "loop via AgentCore Gateway Web Search Tool" + ) + ) + parser.add_argument( + "--query", + default=None, + help="Research question. If omitted, the agent prompts interactively.", + ) + parser.add_argument( + "--max-iter", + type=int, + default=MAX_SEARCH_ITERATIONS, + help=( + f"Maximum search iterations per question (default: {MAX_SEARCH_ITERATIONS}). " + "Higher values give deeper results at the cost of more WebSearch + LLM calls. " + "Recommended: 2 (fast), 4 (balanced), 6+ (deep comparative analysis)." + ), + ) + return parser.parse_args() + + +def main(): + args = parse_args() + + print("=" * 60) + print("AgentCore Web Search Tool — Deep Research Agent") + print("=" * 60) + print(f"Max search iterations : {args.max_iter}") + print(f"Model : {MODEL_ID}") + + # ── Ensure Gateway is configured (detect / reuse / provision) ────────── + ensure_gateway(interactive=True) + + print() + query = args.query + if not query: + query = input("Enter your research question: ").strip() + if not query: + print("No question provided. Exiting.") + return + + print(f"\nResearch question: {query}") + print("-" * 60) + print("Running iterative research loop...\n") + + mcp_client = MCPClient(create_mcp_transport) + + try: + with mcp_client: + tools = mcp_client.list_tools_sync() + print(f"Discovered {len(tools)} tool(s) from Gateway\n") + + model = BedrockModel( + model_id=MODEL_ID, + region_name=REGION, + temperature=0.5, + max_tokens=4096, + ) + agent = Agent( + model=model, + tools=tools, + system_prompt=build_system_prompt(args.max_iter), + ) + response = agent(query) + except Exception as e: + error_msg = str(e) + if "authentication" in error_msg.lower() or "401" in error_msg or "400" in error_msg: + print(f"\n❌ Authentication error: {error_msg}") + print("\n Possible causes:") + print(" • COGNITO_CLIENT_SECRET is incorrect or expired") + print(" • COGNITO_CLIENT_ID does not match the User Pool client") + print(" • COGNITO_DOMAIN is wrong") + print("\n To fix: re-run the Gateway setup or correct your environment variables.") + elif "initialization" in error_msg.lower(): + print(f"\n❌ Failed to connect to the Gateway: {error_msg}") + print("\n Possible causes:") + print(" • Invalid Cognito credentials (check COGNITO_CLIENT_SECRET)") + print(" • Gateway URL is unreachable (check AGENTCORE_GATEWAY_URL)") + print(" • Gateway is not in READY state") + print("\n To fix: verify your environment variables and Gateway status.") + else: + print(f"\n❌ Error: {error_msg}") + raise SystemExit(1) + + print("\n" + "=" * 60) + print("RESEARCH REPORT") + print("=" * 60) + print(extract_text(response)) + print("\n" + "=" * 60) + print("Deep Research Agent demo complete.") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/02-use-cases/deep-research-agent/gateway_setup.py b/02-use-cases/deep-research-agent/gateway_setup.py new file mode 100644 index 000000000..7ad94b587 --- /dev/null +++ b/02-use-cases/deep-research-agent/gateway_setup.py @@ -0,0 +1,583 @@ +""" +Gateway auto-detection and provisioning for the Deep Research Agent. + +Implements a Detect → Prompt → Auto-provision flow: + 1. Check if AGENTCORE_GATEWAY_URL is already set (use it directly) + 2. If not, scan the account for an existing Gateway with a web-search target + 3. If found, auto-configure from the existing Gateway + 4. If not found, prompt the user and create one on the fly + +This removes the hard prerequisite of running a separate setup script before +using the Deep Research Agent. + +Environment variables (all optional — auto-provisioned if missing): + AGENTCORE_GATEWAY_URL — Gateway MCP endpoint + COGNITO_DOMAIN — Cognito domain prefix + COGNITO_CLIENT_ID — Cognito app client ID + COGNITO_CLIENT_SECRET — Cognito app client secret + COGNITO_SCOPE — OAuth scope string + AWS_DEFAULT_REGION — AWS region (default: us-east-1) + +IAM permissions required for auto-provisioning: + iam:CreateRole, iam:PutRolePolicy, iam:GetRole + cognito-idp:CreateUserPool, cognito-idp:CreateUserPoolDomain + cognito-idp:CreateResourceServer, cognito-idp:CreateUserPoolClient + cognito-idp:ListUserPools, cognito-idp:ListUserPoolClients + cognito-idp:DescribeUserPoolClient, cognito-idp:DescribeResourceServer + bedrock-agentcore:CreateGateway, bedrock-agentcore:GetGateway + bedrock-agentcore:CreateGatewayTarget, bedrock-agentcore:ListGatewayTargets + bedrock-agentcore:ListGateways +""" + +import json +import logging +import os +import time + +import boto3 + +logger = logging.getLogger(__name__) + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") +GATEWAY_NAME_PREFIX = "deep-research" + + +# ── Data class for gateway config ────────────────────────────────────────────── + + +class GatewayConfig: + """Holds all connection details for an AgentCore Gateway with Web Search.""" + + def __init__( + self, + gateway_url: str, + cognito_domain: str, + cognito_client_id: str, + cognito_client_secret: str, + cognito_scope: str, + region: str = REGION, + gateway_id: str = "", + role_name: str = "", + user_pool_id: str = "", + ): + self.gateway_url = gateway_url + self.cognito_domain = cognito_domain + self.cognito_client_id = cognito_client_id + self.cognito_client_secret = cognito_client_secret + self.cognito_scope = cognito_scope + self.region = region + self.gateway_id = gateway_id + self.role_name = role_name + self.user_pool_id = user_pool_id + + def export_to_env(self): + """Set environment variables so downstream code can use them.""" + os.environ["AGENTCORE_GATEWAY_URL"] = self.gateway_url + os.environ["COGNITO_DOMAIN"] = self.cognito_domain + os.environ["COGNITO_CLIENT_ID"] = self.cognito_client_id + os.environ["COGNITO_CLIENT_SECRET"] = self.cognito_client_secret + os.environ["COGNITO_SCOPE"] = self.cognito_scope + os.environ["AWS_DEFAULT_REGION"] = self.region + + def print_env_vars(self): + """Print export commands for the user to save.""" + print("\n To reuse this gateway in future sessions, export these variables:\n") + print(f' export AGENTCORE_GATEWAY_URL="{self.gateway_url}"') + print(f' export COGNITO_DOMAIN="{self.cognito_domain}"') + print(f' export COGNITO_CLIENT_ID="{self.cognito_client_id}"') + print(f' export COGNITO_CLIENT_SECRET="{self.cognito_client_secret}"') + print(f' export COGNITO_SCOPE="{self.cognito_scope}"') + print(f' export AWS_DEFAULT_REGION="{self.region}"') + if self.gateway_id: + print(f"\n # Gateway ID (for cleanup): {self.gateway_id}") + if self.role_name: + print(f" # IAM Role: {self.role_name}") + if self.user_pool_id: + print(f" # Cognito Pool: {self.user_pool_id}") + + +# ── Detection: check env vars ───────────────────────────────────────────────── + + +def _config_from_env() -> GatewayConfig | None: + """Return a GatewayConfig from environment variables, or None if incomplete.""" + gateway_url = os.getenv("AGENTCORE_GATEWAY_URL", "") + cognito_domain = os.getenv("COGNITO_DOMAIN", "") + cognito_client_id = os.getenv("COGNITO_CLIENT_ID", "") + cognito_client_secret = os.getenv("COGNITO_CLIENT_SECRET", "") + cognito_scope = os.getenv("COGNITO_SCOPE", "") + + if all([gateway_url, cognito_domain, cognito_client_id, cognito_client_secret]): + return GatewayConfig( + gateway_url=gateway_url, + cognito_domain=cognito_domain, + cognito_client_id=cognito_client_id, + cognito_client_secret=cognito_client_secret, + cognito_scope=cognito_scope or "agentcore-websearch/invoke", + region=REGION, + ) + return None + + +# ── Detection: scan account for existing gateway ────────────────────────────── + + +def _find_existing_gateway() -> dict | None: + """Scan the account for an existing Gateway with a web-search connector target. + + Returns a dict with gateway_id and gateway_url if found, else None. + """ + try: + client = boto3.client("bedrock-agentcore-control", region_name=REGION) + + # List all gateways + paginator_kwargs = {} + while True: + response = client.list_gateways( + maxResults=50, **paginator_kwargs + ) + for gw in response.get("items", []): + gw_id = gw["gatewayId"] + # Check if this gateway has a web-search target + try: + targets = client.list_gateway_targets( + gatewayIdentifier=gw_id + ) + for target in targets.get("items", []): + target_name = target.get("name", "").lower() + if "web-search" in target_name or "websearch" in target_name: + # Get the full gateway details for the URL + gw_detail = client.get_gateway( + gatewayIdentifier=gw_id + ) + return { + "gateway_id": gw_id, + "gateway_url": gw_detail["gatewayUrl"], + "gateway_name": gw.get("name", ""), + } + except Exception: + continue + + next_token = response.get("nextToken") + if not next_token: + break + paginator_kwargs = {"nextToken": next_token} + + except Exception as e: + logger.debug("Could not scan for existing gateways: %s", e) + + return None + + +def _find_cognito_for_gateway(gateway_id: str) -> dict | None: + """Try to find Cognito credentials associated with a gateway. + + Looks for the standard Cognito pool/client created by our setup pattern. + Returns dict with domain, client_id, client_secret, scope or None. + """ + try: + cognito_client = boto3.client("cognito-idp", region_name=REGION) + pools = cognito_client.list_user_pools(MaxResults=60)["UserPools"] + + for pool in pools: + pool_name = pool["Name"] + # Look for pools matching our naming conventions + if "agentcore" in pool_name.lower() and "websearch" in pool_name.lower(): + pool_id = pool["Id"] + clients = cognito_client.list_user_pool_clients( + UserPoolId=pool_id, MaxResults=60 + )["UserPoolClients"] + + for client_info in clients: + if "websearch" in client_info["ClientName"].lower(): + desc = cognito_client.describe_user_pool_client( + UserPoolId=pool_id, + ClientId=client_info["ClientId"], + ) + client_detail = desc["UserPoolClient"] + domain = pool_id.replace("_", "").lower() + return { + "domain": domain, + "client_id": client_detail["ClientId"], + "client_secret": client_detail.get("ClientSecret", ""), + "scope": "agentcore-websearch/invoke", + "user_pool_id": pool_id, + } + except Exception as e: + logger.debug("Could not find Cognito credentials: %s", e) + + return None + + +# ── Provisioning ────────────────────────────────────────────────────────────── + + +def _wait_for_gateway_status(client, gateway_id, target_status="READY", max_wait=150): + """Poll gateway status until it reaches target_status.""" + for _ in range(max_wait // 5): + status = client.get_gateway(gatewayIdentifier=gateway_id)["status"] + if status == target_status: + return status + time.sleep(5) + return status + + +def _wait_for_targets_ready(client, gateway_id, max_wait=150): + """Poll until all gateway targets are READY.""" + for _ in range(max_wait // 5): + targets = client.list_gateway_targets(gatewayIdentifier=gateway_id) + if all(item["status"] == "READY" for item in targets["items"]): + return True + time.sleep(5) + return False + + +def _create_gateway_role(iam_client, role_name, account_id, region): + """Create the IAM service role for the Gateway.""" + assume_role_policy = { + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Principal": {"Service": "bedrock-agentcore.amazonaws.com"}, + "Action": "sts:AssumeRole", + "Condition": { + "StringEquals": {"aws:SourceAccount": account_id}, + "ArnLike": { + "aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*" + }, + }, + } + ], + } + + try: + role_response = iam_client.create_role( + RoleName=role_name, + AssumeRolePolicyDocument=json.dumps(assume_role_policy), + ) + print(f" Created role: {role_name}") + time.sleep(10) # Wait for IAM propagation + except iam_client.exceptions.EntityAlreadyExistsException: + role_response = iam_client.get_role(RoleName=role_name) + print(f" Role already exists: {role_name}") + + role_arn = role_response["Role"]["Arn"] + + iam_client.put_role_policy( + RoleName=role_name, + PolicyName="WebSearchGatewayPolicy", + PolicyDocument=json.dumps( + { + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "InvokeGateway", + "Effect": "Allow", + "Action": "bedrock-agentcore:InvokeGateway", + "Resource": f"arn:aws:bedrock-agentcore:{region}:{account_id}:gateway/*", + }, + { + "Sid": "InvokeWebSearch", + "Effect": "Allow", + "Action": "bedrock-agentcore:InvokeWebSearch", + "Resource": f"arn:aws:bedrock-agentcore:{region}:aws:tool/web-search.v1", + }, + ], + } + ), + ) + print(" Permissions attached ✓") + return role_arn + + +def _create_cognito_resources(cognito_client, region): + """Create Cognito User Pool, resource server, and M2M client.""" + pool_name = "agentcore-websearch-pool" + resource_server_id = "agentcore-websearch" + scopes = [{"ScopeName": "invoke", "ScopeDescription": "Invoke gateway"}] + scope_names = [f"{resource_server_id}/{s['ScopeName']}" for s in scopes] + + # Find or create user pool + user_pool_id = None + for pool in cognito_client.list_user_pools(MaxResults=60)["UserPools"]: + if pool["Name"] == pool_name: + user_pool_id = pool["Id"] + break + + if user_pool_id is None: + create_resp = cognito_client.create_user_pool(PoolName=pool_name) + user_pool_id = create_resp["UserPool"]["Id"] + domain = user_pool_id.replace("_", "").lower() + cognito_client.create_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + print(f" Created user pool: {user_pool_id}") + else: + print(f" User pool exists: {user_pool_id}") + + # Create resource server + try: + cognito_client.describe_resource_server( + UserPoolId=user_pool_id, Identifier=resource_server_id + ) + except cognito_client.exceptions.ResourceNotFoundException: + cognito_client.create_resource_server( + UserPoolId=user_pool_id, + Identifier=resource_server_id, + Name="WebSearch Gateway Resource Server", + Scopes=scopes, + ) + print(" Resource server ensured ✓") + + # Find or create M2M client + client_id, client_secret = None, None + for client in cognito_client.list_user_pool_clients( + UserPoolId=user_pool_id, MaxResults=60 + )["UserPoolClients"]: + if client["ClientName"] == "agentcore-websearch-client": + desc = cognito_client.describe_user_pool_client( + UserPoolId=user_pool_id, ClientId=client["ClientId"] + ) + client_id = client["ClientId"] + client_secret = desc["UserPoolClient"]["ClientSecret"] + break + + if client_id is None: + created = cognito_client.create_user_pool_client( + UserPoolId=user_pool_id, + ClientName="agentcore-websearch-client", + GenerateSecret=True, + AllowedOAuthFlows=["client_credentials"], + AllowedOAuthScopes=scope_names, + AllowedOAuthFlowsUserPoolClient=True, + SupportedIdentityProviders=["COGNITO"], + ExplicitAuthFlows=["ALLOW_REFRESH_TOKEN_AUTH"], + ) + client_id = created["UserPoolClient"]["ClientId"] + client_secret = created["UserPoolClient"]["ClientSecret"] + print(f" Created client: {client_id}") + else: + print(f" Client exists: {client_id}") + + domain = user_pool_id.replace("_", "").lower() + discovery_url = ( + f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}" + f"/.well-known/openid-configuration" + ) + scope_string = " ".join(scope_names) + + return { + "user_pool_id": user_pool_id, + "client_id": client_id, + "client_secret": client_secret, + "discovery_url": discovery_url, + "domain": domain, + "scope": scope_string, + } + + +def _create_gateway_and_target(gateway_client, name, role_arn, cognito_config): + """Create the AgentCore Gateway and Web Search target.""" + create_response = gateway_client.create_gateway( + name=name, + roleArn=role_arn, + protocolType="MCP", + protocolConfiguration={ + "mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"} + }, + authorizerType="CUSTOM_JWT", + authorizerConfiguration={ + "customJWTAuthorizer": { + "allowedClients": [cognito_config["client_id"]], + "discoveryUrl": cognito_config["discovery_url"], + } + }, + description="AgentCore Gateway with Web Search Tool (auto-provisioned by Deep Research Agent)", + ) + + gateway_id = create_response["gatewayId"] + gateway_url = create_response["gatewayUrl"] + print(f" Gateway ID: {gateway_id}") + print(f" Gateway URL: {gateway_url}") + + status = _wait_for_gateway_status(gateway_client, gateway_id) + print(f" Gateway status: {status}") + + # Create Web Search connector target + gateway_client.create_gateway_target( + name="web-search-tool", + gatewayIdentifier=gateway_id, + targetConfiguration={ + "mcp": { + "connector": { + "source": {"connectorId": "web-search"}, + "configurations": [ + {"name": "WebSearch", "parameterValues": {}} + ], + } + } + }, + credentialProviderConfigurations=[ + {"credentialProviderType": "GATEWAY_IAM_ROLE"} + ], + ) + + if _wait_for_targets_ready(gateway_client, gateway_id): + print(" Web Search target: READY ✓") + else: + print(" WARNING: Target did not reach READY state within timeout") + + return gateway_id, gateway_url + + +def provision_gateway(gateway_name: str | None = None) -> GatewayConfig: + """Create a new Gateway with Web Search from scratch. + + Args: + gateway_name: Optional custom name. Defaults to 'deep-research-web-search-gw'. + + Returns: + GatewayConfig with all connection details. + """ + name = gateway_name or "deep-research-web-search-gw" + region = REGION + + sts_client = boto3.client("sts", region_name=region) + account_id = sts_client.get_caller_identity()["Account"] + + print(f"\n Account: {account_id}") + print(f" Region: {region}") + + # Step 1: IAM Role + print("\n [1/4] Creating Gateway service role...") + iam_client = boto3.client("iam") + role_name = f"agentcore-{name}-role" + role_arn = _create_gateway_role(iam_client, role_name, account_id, region) + + # Step 2: Cognito + print("\n [2/4] Setting up Cognito authentication...") + cognito_client = boto3.client("cognito-idp", region_name=region) + cognito_config = _create_cognito_resources(cognito_client, region) + + # Step 3: Gateway + Target + print("\n [3/4] Creating AgentCore Gateway...") + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + gateway_id, gateway_url = _create_gateway_and_target( + gateway_client, name, role_arn, cognito_config + ) + + print("\n [4/4] Setup complete ✓") + + return GatewayConfig( + gateway_url=gateway_url, + cognito_domain=cognito_config["domain"], + cognito_client_id=cognito_config["client_id"], + cognito_client_secret=cognito_config["client_secret"], + cognito_scope=cognito_config["scope"], + region=region, + gateway_id=gateway_id, + role_name=role_name, + user_pool_id=cognito_config["user_pool_id"], + ) + + +# ── Main entry point: Detect → Prompt → Provision ───────────────────────────── + + +def ensure_gateway(interactive: bool = True) -> GatewayConfig: + """Ensure a working Gateway + Web Search configuration is available. + + Resolution order: + 1. Environment variables already set → use them + 2. Existing Gateway with web-search target in the account → reuse it + 3. Prompt user (if interactive) → provision a new one + + Args: + interactive: If True, prompt the user before creating resources. + If False (e.g. runtime mode), raise an error instead. + + Returns: + GatewayConfig ready to use. + + Raises: + SystemExit: If no gateway is available and user declines to create one. + ValueError: If non-interactive and no gateway is available. + """ + # ── Step 1: Check environment variables ──────────────────────────────── + config = _config_from_env() + if config: + logger.info("Using gateway configuration from environment variables.") + return config + + print("\n⚙️ No gateway configuration found in environment variables.") + print(" Scanning your account for an existing Gateway with Web Search...") + + # ── Step 2: Scan for existing gateway ────────────────────────────────── + existing = _find_existing_gateway() + if existing: + print(f"\n✅ Found existing Gateway: {existing['gateway_name']} ({existing['gateway_id']})") + print(" Checking for associated Cognito credentials...") + + cognito = _find_cognito_for_gateway(existing["gateway_id"]) + if cognito: + print(" ✅ Found Cognito credentials.") + config = GatewayConfig( + gateway_url=existing["gateway_url"], + cognito_domain=cognito["domain"], + cognito_client_id=cognito["client_id"], + cognito_client_secret=cognito["client_secret"], + cognito_scope=cognito["scope"], + region=REGION, + gateway_id=existing["gateway_id"], + user_pool_id=cognito["user_pool_id"], + ) + config.export_to_env() + config.print_env_vars() + return config + else: + print(" ⚠️ Could not find Cognito credentials for this Gateway.") + print(" Will need to provision a new setup.") + + # ── Step 3: Prompt and provision ─────────────────────────────────────── + if not interactive: + raise ValueError( + "No gateway configuration available. Set AGENTCORE_GATEWAY_URL and " + "Cognito environment variables, or run in interactive mode to auto-provision." + ) + + print("\n" + "-" * 60) + print(" No usable Gateway + Web Search setup found.") + print(" I can create one for you. This will provision:") + print(" • IAM service role (bedrock-agentcore trust)") + print(" • Cognito User Pool + M2M client") + print(" • AgentCore Gateway (MCP protocol)") + print(" • Web Search connector target") + print() + print(" Region: us-east-1 (only supported region for Web Search)") + print(" Estimated time: ~60 seconds") + print("-" * 60) + + response = input("\n Create Gateway + Web Search now? [y/N]: ").strip().lower() + if response not in ("y", "yes"): + print("\n Aborted. To set up manually, you can either:") + print() + print(" Option 1 — Use the bundled setup module:") + print(" python -c \"from gateway_setup import provision_gateway; cfg = provision_gateway(); cfg.print_env_vars()\"") + print() + print(" Option 2 — Use the standalone setup script:") + print(" python ../../01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py") + print() + print(" Then export the printed environment variables and re-run the agent.") + raise SystemExit(1) + + # Optional: let user name the gateway + custom_name = input(" Gateway name [deep-research-web-search-gw]: ").strip() + + print("\n🚀 Provisioning Gateway + Web Search Tool...\n") + config = provision_gateway(gateway_name=custom_name or None) + config.export_to_env() + config.print_env_vars() + print() + + return config diff --git a/02-use-cases/deep-research-agent/requirements.txt b/02-use-cases/deep-research-agent/requirements.txt new file mode 100644 index 000000000..449dc1750 --- /dev/null +++ b/02-use-cases/deep-research-agent/requirements.txt @@ -0,0 +1,6 @@ +bedrock-agentcore>=0.2.0 +strands-agents>=0.1.8 +boto3>=1.34.0 +botocore>=1.34.0 +mcp +requests From d07fea8ffa70d2bbccea2184a18b05fbfb164b4e Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 9 Jun 2026 09:33:45 -0400 Subject: [PATCH 04/21] Adding nagagaddamu to the contributors.md --- CONTRIBUTORS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 01ab0ecf5..1ae314e3a 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -22,6 +22,7 @@ - Grace Lang - Hari Tripathi - Joshua Samuel +- Naga Gaddamu(nagagaddamu) - khastation - manchandakp - madhurprash From 03663590db10022b33c09ffdc1e8698a33ad86ec Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 9 Jun 2026 11:52:26 -0400 Subject: [PATCH 05/21] security: remove hardcoded Cognito credentials from setup README --- .../03-web-search/01-setup-gateway/README.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md index 0c97c7185..9a940cdb1 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -82,16 +82,6 @@ export COGNITO_SCOPE="agentcore-websearch/invoke" export AWS_DEFAULT_REGION="us-east-1" ``` -```bash -#export AGENTCORE_GATEWAY_URL="https://naga-web-search-gw-jpeyqm6nni.gateway.bedrock-agentcore.us-east-1.amazonaws.com/mcp" -#export COGNITO_DOMAIN="us-east-16ay8pbcw2" -#export COGNITO_CLIENT_ID="n0gmrlojshfmc059dck71583f" -#export COGNITO_CLIENT_SECRET="s9bt641phqs1t7bie4lvg80sah9st50f6c3ooeksf0ri6d7f5po" -#export COGNITO_SCOPE="agentcore-websearch/invoke" -#export AWS_DEFAULT_REGION="us-east-1" -``` - - Export these before running the other demos. ## IAM Permissions From f51901a19065583ee94392639cd29e325150ac6b Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 9 Jun 2026 12:32:00 -0400 Subject: [PATCH 06/21] chore: remove 03-Agent-Core-web-search workshop (to be added in separate PR) --- .../01-web-search-gateway-setup.ipynb | 603 ------------------ .../01-gateway-setup-and-raw-mcp/README.md | 61 -- .../images/.gitkeep | 0 .../requirements.txt | 4 - .../02-web-search-strands-agent.ipynb | 305 --------- .../02-web-search-with-strands/README.md | 51 -- .../images/.gitkeep | 0 .../requirements.txt | 4 - .../03-web-search-langchain-agent.ipynb | 279 -------- .../03-web-search-with-langchain/README.md | 51 -- .../images/.gitkeep | 0 .../requirements.txt | 7 - .../01-cve-scanner/01-cve-scanner.ipynb | 303 --------- .../01-cve-scanner/README.md | 62 -- .../01-cve-scanner/images/.gitkeep | 0 .../01-cve-scanner/requirements.txt | 4 - .../02-earnings-brief/02-earnings-brief.ipynb | 318 --------- .../02-earnings-brief/README.md | 63 -- .../02-earnings-brief/images/.gitkeep | 0 .../02-earnings-brief/requirements.txt | 4 - 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"cells": [ - { - "cell_type": "markdown", - "id": "4015a0a7", - "metadata": {}, - "source": [ - "# AgentCore Web Search Tool — Gateway Setup and Raw MCP Calls\n", - "\n", - "## Overview\n", - "\n", - "The **Amazon Bedrock AgentCore Web Search Tool** eliminates a core limitation of AI agents: knowledge frozen at training time. This notebook creates all the infrastructure needed and verifies it with direct MCP protocol calls — no agent framework yet, just raw tool discovery and invocation.\n", - "\n", - "You will:\n", - "1. Create the IAM service role for the Gateway\n", - "2. Set up Amazon Cognito for inbound authentication\n", - "3. Create an AgentCore Gateway with MCP protocol\n", - "4. Add a Web Search Tool connector target\n", - "5. Verify with direct `tools/list` and `tools/call` MCP calls\n", - "\n", - "The environment variables printed at the end are used by notebooks 02 and 03.\n", - "\n", - "![Tutorial Architecture](../images/tutorial-architecture.png)\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Gateway target type | Connector (`web-search`) |\n", - "| Inbound Auth IdP | Amazon Cognito (`client_credentials` flow) |\n", - "| Outbound Auth | Automatic (Gateway IAM Role) |\n", - "| Tutorial vertical | Genral Purpose - applicable in any industry |\n", - "| Example complexity | Easy |\n", - "| SDK used | boto3 |\n", - "\n", - "### Authentication Architecture\n", - "\n", - "![Inbound and Outbound Auth](../images/inbound-and-outbound-auth.png)\n", - "\n", - "- **Inbound**: Cognito validates the OAuth token your agent passes to the Gateway\n", - "- **Outbound**: The Gateway IAM role authenticates to the Web Search backend automatically" - ] - }, - { - "cell_type": "markdown", - "id": "d5fd60fd", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "- Jupyter notebook (Python kernel)\n", - "- Python 3.10+ or later\n", - "- AWS credentials - choode one \n", - " - **Account ID + Role Name** — the notebook will call `sts:AssumeRole` to get temporary credentials (recommended for cross-account or Isengard access)\n", - " - **Pre-configured credentials** — via AWS CLI profile, environment variables, or instance role\n", - "- AWS account with access to Bedrock AgentCore Gateway, Cognito, and IAM\n", - "- Access to Bedrock inference models in us-east-1\n", - "\n", - "> **Region**: The Web Search Tool connector is available in **us-east-1** only.\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": [\n", - " \"iam:CreateRole\", \"iam:PutRolePolicy\", \"iam:GetRole\",\n", - " \"cognito-idp:CreateUserPool\", \"cognito-idp:CreateUserPoolDomain\",\n", - " \"cognito-idp:CreateResourceServer\", \"cognito-idp:CreateUserPoolClient\",\n", - " \"cognito-idp:ListUserPools\", \"cognito-idp:ListUserPoolClients\",\n", - " \"cognito-idp:DescribeUserPoolClient\", \"cognito-idp:DescribeResourceServer\",\n", - " \"bedrock-agentcore:CreateGateway\", \"bedrock-agentcore:GetGateway\",\n", - " \"bedrock-agentcore:CreateGatewayTarget\", \"bedrock-agentcore:ListGatewayTargets\"\n", - " ],\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "id": "ebf78fad", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c228fe71", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "id": "d420a08e", - "metadata": {}, - "source": [ - "## 2. Configure AWS Region and Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "822151f1", - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "import json\n", - "import os\n", - "import time\n", - "import requests\n", - "\n", - "# Web Search connector is only available in us-east-1\n", - "REGION = \"us-east-1\"\n", - "os.environ[\"AWS_DEFAULT_REGION\"] = REGION\n", - "\n", - "# Verify credentials\n", - "sts_client = boto3.client(\"sts\", region_name=REGION)\n", - "identity = sts_client.get_caller_identity()\n", - "ACCOUNT_ID = identity[\"Account\"]\n", - "print(f\"Account ID : {ACCOUNT_ID}\")\n", - "print(f\"Identity : {identity['Arn']}\")\n", - "print(f\"Region : {REGION}\")" - ] - }, - { - "cell_type": "markdown", - "id": "d53c4327", - "metadata": {}, - "source": [ - "## 3. Create the Gateway Service Role\n", - "\n", - "The Gateway needs an IAM role allowing the AgentCore service to perform actions on your behalf. Two permissions are required:\n", - "- `bedrock-agentcore:InvokeGateway` — invoke the Gateway itself\n", - "- `bedrock-agentcore:InvokeWebSearch` — authorize web search calls to `arn:aws:bedrock-agentcore::aws:tool/web-search.v1`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab8c7616", - "metadata": {}, - "outputs": [], - "source": [ - "GATEWAY_NAME = \"web-search-gateway\"\n", - "GATEWAY_ROLE_NAME = f\"agentcore-{GATEWAY_NAME}-role\"\n", - "\n", - "iam_client = boto3.client(\"iam\")\n", - "\n", - "assume_role_policy = {\n", - " \"Version\": \"2012-10-17\",\n", - " \"Statement\": [{\n", - " \"Effect\": \"Allow\",\n", - " \"Principal\": {\"Service\": \"bedrock-agentcore.amazonaws.com\"},\n", - " \"Action\": \"sts:AssumeRole\",\n", - " \"Condition\": {\n", - " \"StringEquals\": {\"aws:SourceAccount\": ACCOUNT_ID},\n", - " \"ArnLike\": {\"aws:SourceArn\": f\"arn:aws:bedrock-agentcore:{REGION}:{ACCOUNT_ID}:*\"}\n", - " }\n", - " }]\n", - "}\n", - "\n", - "try:\n", - " role_resp = iam_client.create_role(\n", - " RoleName=GATEWAY_ROLE_NAME,\n", - " AssumeRolePolicyDocument=json.dumps(assume_role_policy)\n", - " )\n", - " print(f\"Created role: {GATEWAY_ROLE_NAME}\")\n", - " time.sleep(10) # Allow IAM to propagate\n", - "except iam_client.exceptions.EntityAlreadyExistsException:\n", - " role_resp = iam_client.get_role(RoleName=GATEWAY_ROLE_NAME)\n", - " print(f\"Role already exists: {GATEWAY_ROLE_NAME}\")\n", - "\n", - "GATEWAY_ROLE_ARN = role_resp[\"Role\"][\"Arn\"]\n", - "\n", - "iam_client.put_role_policy(\n", - " RoleName=GATEWAY_ROLE_NAME,\n", - " PolicyName=\"WebSearchGatewayPolicy\",\n", - " PolicyDocument=json.dumps({\n", - " \"Version\": \"2012-10-17\",\n", - " \"Statement\": [\n", - " {\n", - " \"Sid\": \"InvokeGateway\",\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock-agentcore:InvokeGateway\",\n", - " \"Resource\": f\"arn:aws:bedrock-agentcore:{REGION}:{ACCOUNT_ID}:gateway/*\"\n", - " },\n", - " {\n", - " \"Sid\": \"InvokeWebSearch\",\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock-agentcore:InvokeWebSearch\",\n", - " \"Resource\": f\"arn:aws:bedrock-agentcore:{REGION}:aws:tool/web-search.v1\"\n", - " }\n", - " ]\n", - " })\n", - ")\n", - "print(f\"Gateway Role ARN: {GATEWAY_ROLE_ARN}\")\n", - "print(\"Permissions attached ✓\")" - ] - }, - { - "cell_type": "markdown", - "id": "3acd6c11", - "metadata": {}, - "source": [ - "## 4. Configure Inbound Authentication with Amazon Cognito\n", - "\n", - "We create a Cognito User Pool with a machine-to-machine (M2M) client using the `client_credentials` OAuth flow. The Gateway will validate JWT tokens issued by this pool." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c74df02d", - "metadata": {}, - "outputs": [], - "source": [ - "cognito = boto3.client(\"cognito-idp\", region_name=REGION)\n", - "\n", - "USER_POOL_NAME = \"agentcore-websearch-pool\"\n", - "RESOURCE_SERVER_ID = \"agentcore-websearch\"\n", - "SCOPES = [{\"ScopeName\": \"invoke\", \"ScopeDescription\": \"Invoke gateway\"}]\n", - "SCOPE_NAMES = [f\"{RESOURCE_SERVER_ID}/{s['ScopeName']}\" for s in SCOPES]\n", - "SCOPE_STRING = \" \".join(SCOPE_NAMES)\n", - "\n", - "# Find or create user pool\n", - "USER_POOL_ID = None\n", - "for pool in cognito.list_user_pools(MaxResults=60)[\"UserPools\"]:\n", - " if pool[\"Name\"] == USER_POOL_NAME:\n", - " USER_POOL_ID = pool[\"Id\"]\n", - " break\n", - "\n", - "if USER_POOL_ID is None:\n", - " resp = cognito.create_user_pool(PoolName=USER_POOL_NAME)\n", - " USER_POOL_ID = resp[\"UserPool\"][\"Id\"]\n", - " domain = USER_POOL_ID.replace(\"_\", \"\").lower()\n", - " cognito.create_user_pool_domain(Domain=domain, UserPoolId=USER_POOL_ID)\n", - " print(f\"Created user pool: {USER_POOL_ID}\")\n", - "else:\n", - " print(f\"User pool exists: {USER_POOL_ID}\")\n", - "\n", - "# Create resource server\n", - "try:\n", - " cognito.describe_resource_server(UserPoolId=USER_POOL_ID, Identifier=RESOURCE_SERVER_ID)\n", - "except cognito.exceptions.ResourceNotFoundException:\n", - " cognito.create_resource_server(\n", - " UserPoolId=USER_POOL_ID, Identifier=RESOURCE_SERVER_ID,\n", - " Name=\"WebSearch Gateway Resource Server\", Scopes=SCOPES\n", - " )\n", - "print(\"Resource server ensured ✓\")\n", - "\n", - "# Find or create M2M client\n", - "CLIENT_ID, CLIENT_SECRET = None, None\n", - "for c in cognito.list_user_pool_clients(UserPoolId=USER_POOL_ID, MaxResults=60)[\"UserPoolClients\"]:\n", - " if c[\"ClientName\"] == \"agentcore-websearch-client\":\n", - " desc = cognito.describe_user_pool_client(UserPoolId=USER_POOL_ID, ClientId=c[\"ClientId\"])\n", - " CLIENT_ID = c[\"ClientId\"]\n", - " CLIENT_SECRET = desc[\"UserPoolClient\"][\"ClientSecret\"]\n", - " break\n", - "\n", - "if CLIENT_ID is None:\n", - " created = cognito.create_user_pool_client(\n", - " UserPoolId=USER_POOL_ID, ClientName=\"agentcore-websearch-client\",\n", - " GenerateSecret=True, AllowedOAuthFlows=[\"client_credentials\"],\n", - " AllowedOAuthScopes=SCOPE_NAMES, AllowedOAuthFlowsUserPoolClient=True,\n", - " SupportedIdentityProviders=[\"COGNITO\"],\n", - " ExplicitAuthFlows=[\"ALLOW_REFRESH_TOKEN_AUTH\"]\n", - " )\n", - " CLIENT_ID = created[\"UserPoolClient\"][\"ClientId\"]\n", - " CLIENT_SECRET = created[\"UserPoolClient\"][\"ClientSecret\"]\n", - " print(f\"Created client: {CLIENT_ID}\")\n", - "else:\n", - " print(f\"Client exists: {CLIENT_ID}\")\n", - "\n", - "COGNITO_DOMAIN = USER_POOL_ID.replace(\"_\", \"\").lower()\n", - "DISCOVERY_URL = f\"https://cognito-idp.{REGION}.amazonaws.com/{USER_POOL_ID}/.well-known/openid-configuration\"\n", - "print(f\"Discovery URL: {DISCOVERY_URL}\")" - ] - }, - { - "cell_type": "markdown", - "id": "eed44e04", - "metadata": {}, - "source": [ - "## 5. Create the AgentCore Gateway" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "542cb13c", - "metadata": {}, - "outputs": [], - "source": [ - "gateway_client = boto3.client(\"bedrock-agentcore-control\", region_name=REGION)\n", - "\n", - "create_resp = gateway_client.create_gateway(\n", - " name=GATEWAY_NAME,\n", - " roleArn=GATEWAY_ROLE_ARN,\n", - " protocolType=\"MCP\",\n", - " protocolConfiguration={\n", - " \"mcp\": {\"supportedVersions\": [\"2025-03-26\"], \"searchType\": \"SEMANTIC\"}\n", - " },\n", - " authorizerType=\"CUSTOM_JWT\",\n", - " authorizerConfiguration={\n", - " \"customJWTAuthorizer\": {\n", - " \"allowedClients\": [CLIENT_ID],\n", - " \"discoveryUrl\": DISCOVERY_URL\n", - " }\n", - " },\n", - " description=\"AgentCore Gateway with Web Search Tool\"\n", - ")\n", - "\n", - "GATEWAY_ID = create_resp[\"gatewayId\"]\n", - "GATEWAY_URL = create_resp[\"gatewayUrl\"]\n", - "print(f\"Gateway ID : {GATEWAY_ID}\")\n", - "print(f\"Gateway URL : {GATEWAY_URL}\")\n", - "\n", - "# Wait for READY\n", - "for _ in range(30):\n", - " status = gateway_client.get_gateway(gatewayIdentifier=GATEWAY_ID)[\"status\"]\n", - " if status == \"READY\":\n", - " break\n", - " time.sleep(5)\n", - "print(f\"Gateway status: {status}\")" - ] - }, - { - "cell_type": "markdown", - "id": "64b4bad3", - "metadata": {}, - "source": [ - "## 6. Add the Web Search Connector Target\n", - "\n", - "The Web Search Tool uses the `connector` target type. Specify `connectorId: \"web-search\"` and the Gateway handles schema management, endpoint resolution, and outbound auth automatically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e2d8208", - "metadata": {}, - "outputs": [], - "source": [ - "target_resp = gateway_client.create_gateway_target(\n", - " name=\"web-search-tool\",\n", - " gatewayIdentifier=GATEWAY_ID,\n", - " targetConfiguration={\n", - " \"mcp\": {\n", - " \"connector\": {\n", - " \"source\": {\"connectorId\": \"web-search\"},\n", - " \"configurations\": [{\"name\": \"WebSearch\", \"parameterValues\": {}}]\n", - " }\n", - " }\n", - " },\n", - " credentialProviderConfigurations=[\n", - " {\"credentialProviderType\": \"GATEWAY_IAM_ROLE\"}\n", - " ]\n", - ")\n", - "\n", - "TARGET_ID = target_resp[\"targetId\"]\n", - "print(f\"Target ID: {TARGET_ID}\")\n", - "\n", - "# Wait for target READY\n", - "for _ in range(30):\n", - " targets = gateway_client.list_gateway_targets(gatewayIdentifier=GATEWAY_ID)\n", - " statuses = [t[\"status\"] for t in targets[\"items\"]]\n", - " print(f\" Target statuses: {statuses}\")\n", - " if all(s == \"READY\" for s in statuses):\n", - " print(\"\\n✅ All targets READY\")\n", - " break\n", - " time.sleep(5)" - ] - }, - { - "cell_type": "markdown", - "id": "6c104338", - "metadata": {}, - "source": [ - "## 7. Get an OAuth Token\n", - "\n", - "Retrieve a Cognito access token using the `client_credentials` flow. This token is passed as a Bearer header on every MCP call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dde14821", - "metadata": {}, - "outputs": [], - "source": [ - "def get_token():\n", - " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\n", - " \"grant_type\": \"client_credentials\",\n", - " \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET,\n", - " \"scope\": SCOPE_STRING\n", - " }\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "# Allow time for Cognito domain propagation\n", - "print(\"Waiting for Cognito domain propagation...\")\n", - "time.sleep(15)\n", - "\n", - "token = get_token()\n", - "print(f\"Token obtained ✓ (first 20 chars: {token[:20]}...)\")" - ] - }, - { - "cell_type": "markdown", - "id": "6ba40bc7", - "metadata": {}, - "source": [ - "## 8. Verify: Raw MCP Tool Discovery (`tools/list`)\n", - "\n", - "Call the Gateway directly using MCP — no agent framework. This confirms the WebSearch tool is available and shows its input schema." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b06ea7f3", - "metadata": {}, - "outputs": [], - "source": [ - "from mcp.client.streamable_http import streamablehttp_client\n", - "from strands.tools.mcp.mcp_client import MCPClient\n", - "\n", - "\n", - "def create_transport():\n", - " return streamablehttp_client(\n", - " GATEWAY_URL,\n", - " headers={\"Authorization\": f\"Bearer {token}\"}\n", - " )\n", - "\n", - "\n", - "mcp_client = MCPClient(create_transport)\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " print(f\"Discovered {len(tools)} tool(s):\\n\")\n", - " for tool in tools:\n", - " spec = tool.tool_spec\n", - " print(f\" Name : {spec['name']}\")\n", - " print(f\" Description : {spec.get('description', 'N/A')}\")\n", - " schema = json.dumps(spec.get('inputSchema', {}), indent=4)\n", - " print(f\" Input schema: {schema[:300]}\")\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "id": "6032a124", - "metadata": {}, - "source": [ - "## 9. Verify: Raw MCP Tool Invocation (`tools/call`)\n", - "\n", - "Invoke the WebSearch tool directly and inspect the structured response." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "817679e6", - "metadata": {}, - "outputs": [], - "source": [ - "TEST_QUERY = \"What is today's news around the world?\"\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " ws_tool_name = next(t.tool_name for t in tools if \"WebSearch\" in t.tool_name)\n", - "\n", - " print(f\"Invoking: {ws_tool_name}\")\n", - " print(f\"Query : {TEST_QUERY}\\n\")\n", - "\n", - " result = mcp_client.call_tool_sync(\"verify-call\", ws_tool_name, {\"query\": TEST_QUERY})\n", - "\n", - " for content in result.get(\"content\", [result] if not isinstance(result, list) else result):\n", - " if isinstance(content, dict) and \"text\" in content:\n", - " try:\n", - " parsed = json.loads(content[\"text\"])\n", - " print(json.dumps(parsed, indent=2))\n", - " except (ValueError, TypeError):\n", - " print(content[\"text\"][:2000])\n", - " else:\n", - " print(content)" - ] - }, - { - "cell_type": "markdown", - "id": "22a08536", - "metadata": {}, - "source": [ - "## 10. Save Environment Variables for Notebooks 02 and 03\n", - "\n", - "Export these before running the next notebooks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63754959", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"# Copy and export these environment variables:\\n\")\n", - "print(f'export AGENTCORE_GATEWAY_URL=\"{GATEWAY_URL}\"')\n", - "print(f'export COGNITO_DOMAIN=\"{COGNITO_DOMAIN}\"')\n", - "print(f'export COGNITO_CLIENT_ID=\"{CLIENT_ID}\"')\n", - "print(f'export COGNITO_CLIENT_SECRET=\"{CLIENT_SECRET}\"')\n", - "print(f'export COGNITO_SCOPE=\"{SCOPE_STRING}\"')\n", - "print(f'export AWS_DEFAULT_REGION=\"{REGION}\"')\n", - "print(f'\\n# For cleanup:')\n", - "print(f'# Gateway ID : {GATEWAY_ID}')\n", - "print(f'# User Pool ID : {USER_POOL_ID}')\n", - "print(f'# IAM Role : {GATEWAY_ROLE_NAME}')" - ] - }, - { - "cell_type": "markdown", - "id": "da25e94b", - "metadata": {}, - "source": [ - "## Cleanup (Optional)\n", - "\n", - "Run this cell only when you are done with **all** tutorials in this workshop. The Gateway and Cognito pool are reused by notebooks 02 and 03." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90d7f934", - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment to delete all resources\n", - "\n", - "# # Delete Gateway targets and Gateway\n", - "# for item in gateway_client.list_gateway_targets(gatewayIdentifier=GATEWAY_ID, maxResults=100)[\"items\"]:\n", - "# gateway_client.delete_gateway_target(gatewayIdentifier=GATEWAY_ID, targetId=item[\"targetId\"])\n", - "# print(f\"Deleted target: {item['name']}\")\n", - "# time.sleep(10)\n", - "# gateway_client.delete_gateway(gatewayIdentifier=GATEWAY_ID)\n", - "# print(f\"Deleted gateway: {GATEWAY_ID}\")\n", - "\n", - "# # Delete Cognito\n", - "# cognito.delete_user_pool_domain(Domain=COGNITO_DOMAIN, UserPoolId=USER_POOL_ID)\n", - "# cognito.delete_user_pool(UserPoolId=USER_POOL_ID)\n", - "# print(f\"Deleted user pool: {USER_POOL_ID}\")\n", - "\n", - "# # Delete IAM role\n", - "# iam_client.delete_role_policy(RoleName=GATEWAY_ROLE_NAME, PolicyName=\"WebSearchGatewayPolicy\")\n", - "# iam_client.delete_role(RoleName=GATEWAY_ROLE_NAME)\n", - "# print(f\"Deleted role: {GATEWAY_ROLE_NAME}\")\n", - "\n", - "print(\"Uncomment the lines above to delete resources.\")" - ] - }, - { - "cell_type": "markdown", - "id": "a912b8ec", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you:\n", - "- Created an AgentCore Gateway with MCP protocol and Cognito JWT authentication\n", - "- Added a Web Search connector target — no schema or endpoint configuration needed\n", - "- Verified the setup with raw `tools/list` and `tools/call` MCP invocations\n", - "\n", - "**Next**: Run notebook `02-web-search-strands-agent.ipynb` to use this Gateway with a Strands AI agent." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md deleted file mode 100644 index 98f8bb52b..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/README.md +++ /dev/null @@ -1,61 +0,0 @@ -# Web Search Tool — Gateway Setup and Raw MCP Calls - -## Overview - -In this tutorial you will learn how to: - -1. Create the IAM service role for the Gateway -2. Configure Amazon Cognito for inbound authentication -3. Create an AgentCore Gateway with MCP protocol -4. Add a Web Search Tool connector target -5. Verify the setup with direct MCP `tools/list` and `tools/call` invocations - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Gateway target type | Connector (`web-search`) | -| Tutorial vertical | Cross-vertical | -| Example complexity | Easy | -| SDK used | boto3 | - -### Tutorial Architecture - -
- -
- -### Inbound and Outbound Authentication - -
- -
- -The Gateway uses two authentication layers: -- **Inbound**: Amazon Cognito validates the OAuth token passed by your agent -- **Outbound**: The Gateway's IAM role authenticates automatically to the Web Search backend — no additional credentials needed - -## Notebook - -→ [`01-web-search-gateway-setup.ipynb`](01-web-search-gateway-setup.ipynb) - -## Prerequisites - -- AWS account allowlisted for the Web Search Tool private beta -- Python 3.10+ and Jupyter -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions - -```bash -pip install -r requirements.txt -``` - -> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. - -## What You'll Build - -After completing this tutorial you'll have: -- An AgentCore Gateway with MCP protocol -- A Web Search connector target in READY state -- Environment variables ready for tutorials 02 and 03 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt deleted file mode 100644 index 1d8cae582..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/01-gateway-setup-and-raw-mcp/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -boto3 -mcp -requests -strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb deleted file mode 100644 index 13093a9c0..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/02-web-search-strands-agent.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Web Search Tool with Strands Agent\n", - "\n", - "## Overview\n", - "\n", - "This notebook connects a Strands AI agent to the AgentCore Gateway created in notebook 01. The agent automatically discovers the Web Search tool via MCP `tools/list`, then invokes it when answering questions that require current information.\n", - "\n", - "You will:\n", - "1. Connect to the Gateway using MCP Streamable HTTP transport\n", - "2. Discover the WebSearch tool at runtime\n", - "3. Create a Strands agent and ask real-time questions\n", - "4. Inspect the agent's tool calls and grounded responses\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Agentic framework | Strands Agents |\n", - "| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) |\n", - "| Tutorial vertical | Cross-vertical |\n", - "| Example complexity | Easy |\n", - "| SDK used | boto3, strands-agents |\n", - "\n", - "### How It Works\n", - "\n", - "```\n", - "User question\n", - " │\n", - " ▼\n", - "Strands Agent (Claude Sonnet 4)\n", - " │ decides to call WebSearch\n", - " ▼\n", - "MCP tools/call → AgentCore Gateway → Web Search Backend\n", - " │\n", - " ▼\n", - "Structured results { text, url, title, publishedDate }\n", - " │\n", - " ▼\n", - "Final answer with cited sources\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Complete notebook `01-web-search-gateway-setup.ipynb` first and set the following environment variables:\n", - "\n", - "```bash\n", - "export AGENTCORE_GATEWAY_URL=\"...\"\n", - "export COGNITO_DOMAIN=\"...\"\n", - "export COGNITO_CLIENT_ID=\"...\"\n", - "export COGNITO_CLIENT_SECRET=\"...\"\n", - "export COGNITO_SCOPE=\"agentcore-websearch/invoke\"\n", - "export AWS_DEFAULT_REGION=\"us-east-1\"\n", - "```\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock:InvokeModel\",\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "from mcp.client.streamable_http import streamablehttp_client\n", - "from strands import Agent\n", - "from strands.models import BedrockModel\n", - "from strands.tools.mcp.mcp_client import MCPClient\n", - "\n", - "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", - "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", - "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", - "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", - "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", - "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", - "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", - "\n", - "print(f\"Gateway URL : {GATEWAY_URL}\")\n", - "print(f\"Region : {REGION}\")\n", - "print(f\"Model : {MODEL_ID}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Create OAuth Token and MCP Transport" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_token():\n", - " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\n", - " \"grant_type\": \"client_credentials\",\n", - " \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET,\n", - " \"scope\": SCOPE_STRING\n", - " }\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "\n", - "def create_transport():\n", - " \"\"\"Create a fresh MCP Streamable HTTP transport with a valid token.\"\"\"\n", - " token = get_token()\n", - " return streamablehttp_client(\n", - " GATEWAY_URL,\n", - " headers={\"Authorization\": f\"Bearer {token}\"}\n", - " )\n", - "\n", - "\n", - "mcp_client = MCPClient(create_transport)\n", - "print(\"MCP client created ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Discover Tools and Create the Agent\n", - "\n", - "The agent discovers tools dynamically at runtime via `tools/list`. Strands registers them automatically — no manual tool definitions needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SYSTEM_PROMPT = \"\"\"You are a helpful research assistant with access to real-time web search.\n", - "\n", - "PRINCIPLES:\n", - "- Use WebSearch to find current information before answering\n", - "- Always cite your sources with URLs\n", - "- Keep queries concise (under 200 characters)\n", - "- Synthesize information from multiple results when possible\n", - "\"\"\"\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " print(f\"Discovered {len(tools)} tool(s): {[t.tool_name for t in tools]}\")\n", - "\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", - " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", - " print(f\"Agent ready with tools: {agent.tool_names}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Test the Agent on a Current Events Question\n", - "\n", - "The agent will automatically invoke WebSearch and return a grounded answer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "QUERY = \"What is today's news around the world?\"\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", - " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", - "\n", - " print(f\"Query: {QUERY}\")\n", - " print(\"-\" * 60)\n", - " response = agent(QUERY)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"AGENT RESPONSE\")\n", - "print(\"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Try More Queries\n", - "\n", - "The WebSearch tool handles queries up to 200 characters. Here are a few more examples showing different query types." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "demo_queries = [\n", - " \"What are the latest developments in quantum computing in 2026?\",\n", - " \"What is the current price of gold?\",\n", - " \"What new AWS services were announced recently?\"\n", - "]\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.7, max_tokens=1024)\n", - " agent = Agent(model=model, tools=tools, system_prompt=SYSTEM_PROMPT)\n", - "\n", - " for query in demo_queries:\n", - " print(f\"\\n{'=' * 60}\")\n", - " print(f\"Query: {query}\")\n", - " print(\"=\" * 60)\n", - " response = agent(query)\n", - " if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you:\n", - "- Connected a Strands agent to the AgentCore Gateway via MCP Streamable HTTP\n", - "- Discovered the WebSearch tool dynamically at runtime\n", - "- Got grounded, cited answers to real-time questions\n", - "\n", - "**Next**: Run notebook `03-web-search-langchain-agent.ipynb` for the same integration using LangChain and LangGraph." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md deleted file mode 100644 index d51a409ba..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/README.md +++ /dev/null @@ -1,51 +0,0 @@ -# Web Search Tool with Strands Agent - -## Overview - -In this tutorial you will learn how to: - -1. Connect a Strands agent to an AgentCore Gateway via MCP Streamable HTTP -2. Discover the Web Search tool dynamically at runtime -3. Ask real-time questions — the agent invokes WebSearch automatically -4. Receive grounded responses with cited source URLs - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Agentic framework | Strands Agents | -| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) | -| Tutorial vertical | Cross-vertical | -| Example complexity | Easy | -| SDK used | boto3, strands-agents | - -### Tutorial Architecture - -``` -┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ -│ User │ ─────────────────────────────▶ │ Strands Agent │ -│ │◀───────────────────────────── │ Claude Sonnet 4 │ -│ │ Grounded answer + citations │ │ -└────────────┘ │ tools: [WebSearch] │ - │ │ MCP tools/call │ - └───────┼──────────────────┘ - ▼ - ┌──────────────────────────┐ - │ AgentCore Gateway │ - │ Web Search Connector │ - └──────────────────────────┘ -``` - -## Notebook - -→ [`02-web-search-strands-agent.ipynb`](02-web-search-strands-agent.ipynb) - -## Prerequisites - -Complete tutorial `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. - -```bash -pip install -r requirements.txt -``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt deleted file mode 100644 index 1d8cae582..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/02-web-search-with-strands/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -boto3 -mcp -requests -strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb deleted file mode 100644 index 704725da1..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/03-web-search-langchain-agent.ipynb +++ /dev/null @@ -1,279 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Web Search Tool with LangChain Agent\n", - "\n", - "## Overview\n", - "\n", - "This notebook demonstrates the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the ReAct agent loop.\n", - "\n", - "You will:\n", - "1. Connect to the Gateway using `MultiServerMCPClient` from `langchain-mcp-adapters`\n", - "2. Discover Web Search tools via MCP\n", - "3. Build a ReAct agent with `ChatBedrockConverse`\n", - "4. Run web-grounded Q&A\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Agentic framework | LangChain + LangGraph |\n", - "| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) |\n", - "| Tutorial vertical | Cross-vertical |\n", - "| Example complexity | Easy |\n", - "| SDK used | boto3, langchain-aws, langchain-mcp-adapters |\n", - "\n", - "### How It Works\n", - "\n", - "```\n", - "User question\n", - " │\n", - " ▼\n", - "LangGraph ReAct Agent (ChatBedrockConverse)\n", - " │ calls WebSearch tool\n", - " ▼\n", - "MultiServerMCPClient → AgentCore Gateway → Web Search Backend\n", - " │\n", - " ▼\n", - "Structured results → Final answer with citations\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Complete notebook `01-web-search-gateway-setup.ipynb` first and set the environment variables it prints.\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock:InvokeModel\",\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import asyncio\n", - "import os\n", - "import requests\n", - "\n", - "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", - "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", - "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", - "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", - "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", - "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", - "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", - "\n", - "print(f\"Gateway URL : {GATEWAY_URL}\")\n", - "print(f\"Region : {REGION}\")\n", - "print(f\"Model : {MODEL_ID}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Token Helper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_token():\n", - " \"\"\"Retrieve a fresh OAuth access token from Cognito.\"\"\"\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\n", - " \"grant_type\": \"client_credentials\",\n", - " \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET,\n", - " \"scope\": SCOPE_STRING\n", - " }\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "token = get_token()\n", - "print(\"Token obtained ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Create LangChain Agent with MCP Tools\n", - "\n", - "`MultiServerMCPClient` connects to one or more MCP servers and converts their tools into LangChain-compatible tool objects. `create_react_agent` builds a LangGraph ReAct agent with those tools." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_aws import ChatBedrockConverse\n", - "from langchain_mcp_adapters.client import MultiServerMCPClient\n", - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "\n", - "async def run_agent(query: str) -> str:\n", - " \"\"\"Run the LangChain agent with a web search query.\"\"\"\n", - " model = ChatBedrockConverse(\n", - " model=MODEL_ID,\n", - " region_name=REGION,\n", - " temperature=0.7,\n", - " max_tokens=1024\n", - " )\n", - "\n", - " async with MultiServerMCPClient({\n", - " \"web-search\": {\n", - " \"transport\": \"streamable_http\",\n", - " \"url\": GATEWAY_URL,\n", - " \"headers\": {\"Authorization\": f\"Bearer {get_token()}\"}\n", - " }\n", - " }) as client:\n", - " tools = client.get_tools()\n", - " print(f\"Discovered {len(tools)} tool(s): {[t.name for t in tools]}\")\n", - "\n", - " agent = create_react_agent(model, tools=tools)\n", - " result = await agent.ainvoke({\n", - " \"messages\": [{\"role\": \"user\", \"content\": query}]\n", - " })\n", - "\n", - " final_message = result[\"messages\"][-1]\n", - " return final_message.content if hasattr(final_message, \"content\") else str(final_message)\n", - "\n", - "\n", - "print(\"Agent function defined ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Ask a Real-Time Question" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "QUERY = \"What is today's news around the world?\"\n", - "\n", - "print(f\"Query: {QUERY}\")\n", - "print(\"-\" * 60)\n", - "\n", - "answer = await run_agent(QUERY)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"LANGCHAIN AGENT RESPONSE\")\n", - "print(\"=\" * 60)\n", - "print(answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Try More Queries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "demo_queries = [\n", - " \"What are the latest AI model releases in 2026?\",\n", - " \"What is the current EUR/USD exchange rate?\",\n", - " \"What new features were announced in Python 3.14?\"\n", - "]\n", - "\n", - "for query in demo_queries:\n", - " print(f\"\\n{'=' * 60}\")\n", - " print(f\"Query: {query}\")\n", - " print(\"=\" * 60)\n", - " answer = await run_agent(query)\n", - " print(answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you:\n", - "- Used `langchain-mcp-adapters` to connect to the AgentCore Gateway\n", - "- Built a LangGraph ReAct agent with `ChatBedrockConverse`\n", - "- Got grounded, cited answers using the same Gateway from notebook 01\n", - "\n", - "The Gateway is framework-agnostic — Strands, LangChain, or any MCP client works the same way.\n", - "\n", - "**Next**: Explore the advanced examples in `04-advanced-examples/` for multi-turn search patterns." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md deleted file mode 100644 index 3ce70c848..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/README.md +++ /dev/null @@ -1,51 +0,0 @@ -# Web Search Tool with LangChain Agent - -## Overview - -In this tutorial you will learn how to: - -1. Connect a LangChain agent to an AgentCore Gateway using `langchain-mcp-adapters` -2. Discover Web Search tools via the MCP protocol -3. Build a ReAct agent with `ChatBedrockConverse` and LangGraph -4. Run web-grounded Q&A with the same Gateway set up in tutorial 01 - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Agentic framework | LangChain + LangGraph | -| LLM model | Anthropic Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) | -| Tutorial vertical | Cross-vertical | -| Example complexity | Easy | -| SDK used | boto3, langchain-aws, langchain-mcp-adapters | - -### Tutorial Architecture - -``` -┌────────────┐ question ┌─────────────────────────────────────────┐ -│ User │ ───────────▶ │ LangGraph ReAct Agent │ -│ │◀─────────── │ ChatBedrockConverse (Claude Sonnet 4) │ -│ │ answer │ │ -└────────────┘ │ MultiServerMCPClient │ - │ │ streamable_http │ - └───────┼─────────────────────────────────┘ - ▼ - ┌──────────────────────┐ - │ AgentCore Gateway │ - │ Web Search │ - └──────────────────────┘ -``` - -## Notebook - -→ [`03-web-search-langchain-agent.ipynb`](03-web-search-langchain-agent.ipynb) - -## Prerequisites - -Complete tutorial `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. - -```bash -pip install -r requirements.txt -``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt deleted file mode 100644 index 9b6ebd50e..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/03-web-search-with-langchain/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -boto3 -mcp -requests -langchain -langchain-mcp-adapters -langchain-aws -langgraph diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb deleted file mode 100644 index 8c043f202..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb +++ /dev/null @@ -1,303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CVE Scanner — Multi-turn Security Vulnerability Search\n", - "\n", - "## Overview\n", - "\n", - "This notebook builds a security agent that scans a dependency list for recent CVEs using live web search. The agent issues one focused search per package, then synthesizes severity across all results into a prioritized remediation report.\n", - "\n", - "**Why live search is critical here**: CVE databases update daily. A newly published exploit or patch changes the risk profile of a dependency within hours — training data is always stale for this.\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Agentic framework | Strands Agents |\n", - "| LLM model | Anthropic Claude Sonnet 4 |\n", - "| Tutorial vertical | Security / DevOps |\n", - "| Example complexity | Intermediate |\n", - "| SDK used | boto3, strands-agents |\n", - "\n", - "### Multi-turn Search Pattern\n", - "\n", - "```\n", - "Input: requirements.txt\n", - " │\n", - " ▼\n", - "Parse packages + versions\n", - " │\n", - " ▼\n", - "For each package:\n", - " Search 1: \"CVE vulnerability\"\n", - " Search 2: \" latest version security patch\"\n", - " │\n", - " ▼\n", - "Aggregate + score by CVSS severity\n", - " │\n", - " ▼\n", - "Output: Prioritized remediation report\n", - " CRITICAL requests 2.28.0 → CVE-XXXX, upgrade to 2.32.3\n", - " HIGH urllib3 1.26.5 → CVE-YYYY, upgrade to 2.2.2\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock:InvokeModel\",\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration and Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "from mcp.client.streamable_http import streamablehttp_client\n", - "from strands import Agent\n", - "from strands.models import BedrockModel\n", - "from strands.tools.mcp.mcp_client import MCPClient\n", - "\n", - "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", - "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", - "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", - "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", - "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", - "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", - "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", - "\n", - "\n", - "def get_token():\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "\n", - "def create_transport():\n", - " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", - "\n", - "\n", - "print(\"Configuration loaded ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Define the Dependency List\n", - "\n", - "This is the list of packages to scan. Update it with your actual dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sample dependency list — replace with your actual requirements\n", - "DEPENDENCIES = [\n", - " \"requests==2.28.0\",\n", - " \"urllib3==1.26.5\",\n", - " \"cryptography==3.4.8\",\n", - " \"pillow==9.0.0\",\n", - " \"django==3.2.0\"\n", - "]\n", - "\n", - "print(f\"Packages to scan ({len(DEPENDENCIES)}):\")\n", - "for dep in DEPENDENCIES:\n", - " print(f\" {dep}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. CVE Scanner Agent\n", - "\n", - "The agent uses the CVE Scanner system prompt, which instructs it to search for each dependency separately and return a structured severity report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "CVE_SYSTEM_PROMPT = \"\"\"You are a security analyst agent with access to real-time web search.\n", - "\n", - "When given a list of Python dependencies, you will:\n", - "1. For each package and version, search for known CVEs and security advisories\n", - "2. Search for the latest patched version of each package\n", - "3. Assign a severity level: CRITICAL, HIGH, MEDIUM, LOW, or NONE\n", - "4. Produce a remediation report sorted by severity\n", - "\n", - "SEARCH STRATEGY:\n", - "- Use queries like \"CVE \" and \" security vulnerability 2025 2026\"\n", - "- Keep each query under 200 characters\n", - "- Search each package independently before synthesizing\n", - "\n", - "OUTPUT FORMAT:\n", - "For each package, output a row:\n", - " | == | | Upgrade to: | \n", - "\n", - "End with a summary of total findings by severity level.\n", - "\"\"\"\n", - "\n", - "mcp_client = MCPClient(create_transport)\n", - "print(\"CVE scanner agent ready ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Run the CVE Scan\n", - "\n", - "The agent will issue multiple searches — one per package — and synthesize a prioritized report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dep_list = \"\\n\".join(f\" - {d}\" for d in DEPENDENCIES)\n", - "scan_prompt = f\"\"\"Scan the following Python dependencies for security vulnerabilities and CVEs.\n", - "Search for each package individually, then produce a prioritized remediation report.\n", - "\n", - "Dependencies:\n", - "{dep_list}\n", - "\"\"\"\n", - "\n", - "print(\"Starting CVE scan...\")\n", - "print(\"-\" * 60)\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.3, max_tokens=2048)\n", - " agent = Agent(model=model, tools=tools, system_prompt=CVE_SYSTEM_PROMPT)\n", - " response = agent(scan_prompt)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"CVE SCAN REPORT\")\n", - "print(\"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Scan a Custom requirements.txt\n", - "\n", - "Pass the content of your actual `requirements.txt` file to scan your real dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# To scan your own requirements.txt:\n", - "# with open(\"path/to/your/requirements.txt\") as f:\n", - "# requirements_content = f.read()\n", - "\n", - "# custom_prompt = f\"\"\"Scan these Python dependencies for CVEs:\\n\\n{requirements_content}\"\"\"\n", - "# with mcp_client:\n", - "# tools = mcp_client.list_tools_sync()\n", - "# model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.3, max_tokens=2048)\n", - "# agent = Agent(model=model, tools=tools, system_prompt=CVE_SYSTEM_PROMPT)\n", - "# response = agent(custom_prompt)\n", - "\n", - "print(\"Uncomment the cells above to scan your own requirements.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you built a security scanning agent that:\n", - "- Issues one targeted search per dependency (multi-turn pattern)\n", - "- Finds CVEs that postdate any model training cutoff\n", - "- Produces a prioritized remediation report\n", - "\n", - "The key insight: **each search is informed by the dependency from the parsed list** — this is the \"search for each item\" multi-turn pattern.\n", - "\n", - "**Next**: See `02-earnings-brief.ipynb` for the chained search pattern where each result informs the next query." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md deleted file mode 100644 index 72c4f815d..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# CVE Scanner — Multi-turn Security Vulnerability Search - -## Overview - -In this example you will build a security agent that scans a list of Python dependencies for known vulnerabilities using live web search. The agent issues one search per package, synthesizes severity across all results, and produces a prioritized remediation report. - -**Why live search is critical here**: CVE databases update daily. A newly published exploit or patch changes the risk profile of a dependency within hours. Training data is always stale for this. - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Agentic framework | Strands Agents | -| LLM model | Anthropic Claude Sonnet 4 | -| Tutorial vertical | Security / DevOps | -| Example complexity | Intermediate | -| SDK used | boto3, strands-agents | - -### What the Agent Does - -``` -Input: requirements.txt contents - │ - ▼ -┌────────────────────────────────┐ -│ For each dependency: │ -│ 1. Search "CVE " │ -│ 2. Search " latest │ -│ version security patch" │ -│ 3. Extract severity + fix │ -└────────────────────────────────┘ - │ - ▼ -Output: Prioritized table - CRITICAL requests 2.28.0 → CVE-2024-XXXX, upgrade to 2.32.3 - HIGH urllib3 1.26.5 → CVE-2024-YYYY, upgrade to 2.2.2 - ... -``` - -### Multi-turn Search Pattern - -The agent runs a search loop driven by the dependency list: -1. Parse the dependency list into packages and versions -2. For each package: search for CVEs, then search for the current patched version -3. Aggregate results and score by CVSS severity -4. Generate a remediation report sorted by risk - -Each search query is informed by the package name and version from the previous parse step — this is the core multi-turn pattern. - -## Notebook - -→ [`01-cve-scanner.ipynb`](01-cve-scanner.ipynb) - -## Prerequisites - -Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. - -```bash -pip install -r requirements.txt -``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt deleted file mode 100644 index 1d8cae582..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/01-cve-scanner/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -boto3 -mcp -requests -strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb deleted file mode 100644 index 40a08e675..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Earnings Brief Agent — Chained Multi-turn Research\n", - "\n", - "## Overview\n", - "\n", - "This notebook builds a financial research agent that, given a stock ticker, automatically searches for analyst estimates, recent competitor results, and sector news — then generates a structured pre-earnings call brief.\n", - "\n", - "**Why live search is critical here**: Analyst consensus, guidance revisions, and competitor earnings are filed days before the call. This information does not exist in any model's training data.\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Agentic framework | Strands Agents |\n", - "| LLM model | Anthropic Claude Sonnet 4 |\n", - "| Tutorial vertical | Finance |\n", - "| Example complexity | Intermediate |\n", - "| SDK used | boto3, strands-agents |\n", - "\n", - "### Chained Search Pattern\n", - "\n", - "```\n", - "Input: Ticker (e.g., \"AMZN\")\n", - " │\n", - " ▼\n", - "Search 1: Analyst EPS + revenue estimates\n", - " │ results inform → what consensus expects\n", - " ▼\n", - "Search 2: Guidance revisions since last quarter\n", - " │ results inform → any surprise risk\n", - " ▼\n", - "Search 3: Top competitors' recent earnings\n", - " │ results inform → sector context\n", - " ▼\n", - "Search 4: Macro / sector news this quarter\n", - " │\n", - " ▼\n", - "Output: Structured pre-earnings brief\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock:InvokeModel\",\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration and Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "from mcp.client.streamable_http import streamablehttp_client\n", - "from strands import Agent\n", - "from strands.models import BedrockModel\n", - "from strands.tools.mcp.mcp_client import MCPClient\n", - "\n", - "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", - "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", - "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", - "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", - "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", - "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", - "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", - "\n", - "\n", - "def get_token():\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "\n", - "def create_transport():\n", - " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", - "\n", - "\n", - "print(\"Configuration loaded ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Choose a Ticker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Change this to any US-listed company ticker\n", - "TICKER = \"AMZN\"\n", - "COMPANY_NAME = \"Amazon\" # Used in search queries for better results\n", - "\n", - "print(f\"Generating pre-earnings brief for: {TICKER} ({COMPANY_NAME})\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Define the Earnings Brief Agent\n", - "\n", - "The system prompt instructs the agent to follow the four-search research sequence and produce a structured output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "EARNINGS_SYSTEM_PROMPT = \"\"\"You are a financial research analyst with access to real-time web search.\n", - "\n", - "When given a stock ticker and company name, produce a pre-earnings call brief by:\n", - "\n", - "RESEARCH SEQUENCE (do these searches in order):\n", - "1. Search: \" earnings estimate analyst consensus Q 2026\"\n", - "2. Search: \" guidance revision outlook 2026\"\n", - "3. Search: \" competitors earnings results recent\"\n", - "4. Search: \" sector macro headwinds tailwinds 2026\"\n", - "\n", - "CRITICAL RULES:\n", - "- Keep each search query under 200 characters\n", - "- Each search informs the next — note what you learned before issuing the next query\n", - "- Cite URLs for every claim\n", - "\n", - "OUTPUT FORMAT (use markdown):\n", - "\n", - "## Pre-Earnings Brief: — \n", - "\n", - "### Analyst Consensus\n", - "\n", - "\n", - "### Guidance and Surprise Risk\n", - "\n", - "\n", - "### Competitor Context\n", - "\n", - "\n", - "### Macro and Sector Factors\n", - "\n", - "\n", - "### What to Watch\n", - "<3 key metrics or statements to monitor during the call>\n", - "\n", - "### Sources\n", - "\n", - "\"\"\"\n", - "\n", - "mcp_client = MCPClient(create_transport)\n", - "print(\"Earnings brief agent ready ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Generate the Pre-Earnings Brief\n", - "\n", - "The agent will run 4 chained searches — each one informed by the previous result — then synthesize the brief." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "brief_prompt = f\"\"\"Generate a pre-earnings call brief for {TICKER} ({COMPANY_NAME}).\n", - "\n", - "Follow the research sequence: analyst estimates → guidance revisions → competitor context → macro factors.\n", - "Produce the full structured brief with cited sources.\n", - "\"\"\"\n", - "\n", - "print(f\"Researching {TICKER}...\")\n", - "print(\"-\" * 60)\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", - " agent = Agent(model=model, tools=tools, system_prompt=EARNINGS_SYSTEM_PROMPT)\n", - " response = agent(brief_prompt)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(f\"PRE-EARNINGS BRIEF: {TICKER}\")\n", - "print(\"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Try Another Ticker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Change these to generate a brief for a different company\n", - "TICKER_2 = \"TSLA\"\n", - "COMPANY_2 = \"Tesla\"\n", - "\n", - "brief_prompt_2 = f\"\"\"Generate a pre-earnings call brief for {TICKER_2} ({COMPANY_2}).\n", - "Follow the research sequence and produce the full structured brief with cited sources.\n", - "\"\"\"\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", - " agent = Agent(model=model, tools=tools, system_prompt=EARNINGS_SYSTEM_PROMPT)\n", - " response = agent(brief_prompt_2)\n", - "\n", - "print(f\"\\n{'=' * 60}\")\n", - "print(f\"PRE-EARNINGS BRIEF: {TICKER_2}\")\n", - "print(\"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you built a financial research agent that:\n", - "- Runs 4 chained searches where each result shapes the next query\n", - "- Synthesizes analyst data, competitive context, and macro factors\n", - "- Produces a structured brief with cited sources\n", - "\n", - "The key insight: **the output of each search is used to form a better next query** — this is the chained multi-turn pattern.\n", - "\n", - "**Next**: See `03-iterative-research.ipynb` for the generalized search → reflect → search loop that underpins both this example and the CVE scanner." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md deleted file mode 100644 index c7ae55b5f..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/README.md +++ /dev/null @@ -1,63 +0,0 @@ -# Earnings Brief Agent — Chained Multi-turn Research - -## Overview - -In this example you will build a financial research agent that, given a company ticker, automatically searches for analyst estimates, recent competitor results, and sector news — then generates a structured pre-earnings call brief. - -**Why live search is critical here**: Analyst consensus, guidance revisions, and competitor earnings are filed days before the call. This information does not exist in any model's training data. - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Agentic framework | Strands Agents | -| LLM model | Anthropic Claude Sonnet 4 | -| Tutorial vertical | Finance | -| Example complexity | Intermediate | -| SDK used | boto3, strands-agents | - -### What the Agent Does - -``` -Input: Ticker symbol (e.g., "AMZN") + earnings date - │ - ▼ -┌─────────────────────────────────────────────┐ -│ Search 1: Analyst EPS and revenue estimates │ -│ Search 2: Guidance revisions since last Q │ -│ Search 3: Top 2 competitors' recent results │ -│ Search 4: Macro / sector news this quarter │ -└─────────────────────────────────────────────┘ - │ - ▼ -Output: Structured brief - ## Pre-Earnings Brief: AMZN — Q2 2026 - ### Analyst Consensus - ### Key Risks and Tailwinds - ### Competitor Context - ### What to Watch -``` - -### Chained Search Pattern - -Each search is informed by the results of the previous one: -1. Analyst consensus → identifies what the market expects -2. Guidance revision search → narrows to surprises vs. prior guidance -3. Competitor search → adds comparative context -4. Macro/sector search → explains external factors the model might miss - -The agent reflects after each search and decides what to look for next — this is the chained multi-turn pattern. - -## Notebook - -→ [`02-earnings-brief.ipynb`](02-earnings-brief.ipynb) - -## Prerequisites - -Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. - -```bash -pip install -r requirements.txt -``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt deleted file mode 100644 index 1d8cae582..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/02-earnings-brief/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -boto3 -mcp -requests -strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb deleted file mode 100644 index 34cf5092a..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/03-iterative-research.ipynb +++ /dev/null @@ -1,324 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Iterative Research Agent — Search → Reflect → Search Loop\n", - "\n", - "## Overview\n", - "\n", - "This notebook builds a research agent that answers complex questions through an explicit multi-turn loop: plan, search, reflect on gaps, search again — repeating until confident. This is the foundational pattern behind the CVE scanner and earnings brief examples.\n", - "\n", - "By making the loop explicit with a configurable max-iterations and confidence threshold, you can tune it for depth vs. cost.\n", - "\n", - "### Tutorial Details\n", - "\n", - "| Information | Details |\n", - "|:------------|:--------|\n", - "| Tutorial type | Interactive (Jupyter Notebook) |\n", - "| AgentCore components | AgentCore Gateway |\n", - "| Agentic framework | Strands Agents |\n", - "| LLM model | Anthropic Claude Sonnet 4 |\n", - "| Tutorial vertical | Cross-vertical |\n", - "| Example complexity | Advanced |\n", - "| SDK used | boto3, strands-agents |\n", - "\n", - "### The Research Loop\n", - "\n", - "```\n", - "Question\n", - " │\n", - " ▼\n", - "PLAN: Break into sub-questions\n", - " │\n", - " ▼\n", - "SEARCH: Execute highest-priority query ◀──────────────┐\n", - " │ │\n", - " ▼ │\n", - "REFLECT: What gaps remain? │\n", - " ├── Gaps found → refine query, iterate (max N) ────┘\n", - " └── Confident → synthesize final answer\n", - "```\n", - "\n", - "### When Single-Shot Search Isn't Enough\n", - "\n", - "Use this pattern when:\n", - "- The question requires comparing multiple sources\n", - "- Initial results reveal follow-up questions\n", - "- You need to reconcile conflicting information\n", - "- The topic has multiple dimensions that all matter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it prints.\n", - "\n", - "### Required IAM permissions\n", - "\n", - "~~~json\n", - "{\n", - " \"Effect\": \"Allow\",\n", - " \"Action\": \"bedrock:InvokeModel\",\n", - " \"Resource\": \"*\"\n", - "}\n", - "~~~" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Install Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade -r requirements.txt --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configuration and Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import requests\n", - "from mcp.client.streamable_http import streamablehttp_client\n", - "from strands import Agent\n", - "from strands.models import BedrockModel\n", - "from strands.tools.mcp.mcp_client import MCPClient\n", - "\n", - "REGION = os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n", - "GATEWAY_URL = os.environ[\"AGENTCORE_GATEWAY_URL\"]\n", - "COGNITO_DOMAIN = os.environ[\"COGNITO_DOMAIN\"]\n", - "CLIENT_ID = os.environ[\"COGNITO_CLIENT_ID\"]\n", - "CLIENT_SECRET = os.environ[\"COGNITO_CLIENT_SECRET\"]\n", - "SCOPE_STRING = os.environ[\"COGNITO_SCOPE\"]\n", - "MODEL_ID = \"us.anthropic.claude-sonnet-4-20250514-v1:0\"\n", - "\n", - "\n", - "def get_token():\n", - " url = f\"https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token\"\n", - " resp = requests.post(\n", - " url,\n", - " headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n", - " data={\"grant_type\": \"client_credentials\", \"client_id\": CLIENT_ID,\n", - " \"client_secret\": CLIENT_SECRET, \"scope\": SCOPE_STRING}\n", - " )\n", - " resp.raise_for_status()\n", - " return resp.json()[\"access_token\"]\n", - "\n", - "\n", - "def create_transport():\n", - " return streamablehttp_client(GATEWAY_URL, headers={\"Authorization\": f\"Bearer {get_token()}\"})\n", - "\n", - "\n", - "# Tunable parameters\n", - "MAX_SEARCH_ITERATIONS = 4 # Maximum number of search rounds before forcing synthesis\n", - "CONFIDENCE_THRESHOLD = 0.8 # The agent uses this conceptually — expressed in the prompt\n", - "\n", - "print(f\"Max iterations : {MAX_SEARCH_ITERATIONS}\")\n", - "print(f\"Configuration : ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Define the Iterative Research Agent\n", - "\n", - "The system prompt explicitly encodes the plan → search → reflect → search loop with a stopping condition." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ITERATIVE_RESEARCH_PROMPT = f\"\"\"You are a thorough research analyst with access to real-time web search.\n", - "\n", - "RESEARCH LOOP (repeat up to {MAX_SEARCH_ITERATIONS} iterations):\n", - "\n", - "STEP 1 — PLAN:\n", - " Break the question into sub-questions. List them in priority order.\n", - " Identify what you can answer confidently vs. what requires search.\n", - "\n", - "STEP 2 — SEARCH:\n", - " Execute the highest-priority unanswered sub-question as a web search.\n", - " Keep queries under 200 characters.\n", - " After each search, note: \"Learned: . Remaining gaps: .\"\n", - "\n", - "STEP 3 — REFLECT:\n", - " Ask yourself: \"Do I have enough to answer the original question confidently?\"\n", - " - If NO and iterations remain: formulate the next search based on gaps, go to STEP 2\n", - " - If YES or iterations exhausted: go to STEP 4\n", - "\n", - "STEP 4 — SYNTHESIZE:\n", - " Write a comprehensive answer that:\n", - " - Directly addresses the original question\n", - " - Integrates findings from all search rounds\n", - " - Cites URLs for all factual claims\n", - " - Notes any remaining uncertainties\n", - "\n", - "SHOW YOUR WORK: Make the plan, search queries, and reflections visible in your response.\n", - "This helps users understand the research process and verify the results.\n", - "\"\"\"\n", - "\n", - "mcp_client = MCPClient(create_transport)\n", - "print(\"Iterative research agent ready ✓\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Research a Complex Question\n", - "\n", - "This question requires multiple search angles — a single query can't answer it reliably." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RESEARCH_QUESTION = \"\"\"\n", - "What are the key differences between Claude Sonnet 4 and GPT-4.5 for enterprise code generation use cases,\n", - "and which one has better community adoption among developers as of 2026?\n", - "\"\"\"\n", - "\n", - "print(f\"Research question: {RESEARCH_QUESTION.strip()}\")\n", - "print(\"-\" * 60)\n", - "print(\"Running iterative research loop...\")\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", - " agent = Agent(model=model, tools=tools, system_prompt=ITERATIVE_RESEARCH_PROMPT)\n", - " response = agent(RESEARCH_QUESTION.strip())\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"RESEARCH REPORT\")\n", - "print(\"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Try Questions That Require Multiple Search Angles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "multi_angle_questions = [\n", - " \"What regulatory changes in the EU in 2026 affect AI startup compliance, and what are the key deadlines?\",\n", - " \"How has the adoption of Model Context Protocol changed agent development practices in 2025-2026?\",\n", - " \"What are the current best practices for deploying LLM agents in production, based on recent industry reports?\"\n", - "]\n", - "\n", - "# Run one example (uncomment others as needed)\n", - "selected_question = multi_angle_questions[0]\n", - "\n", - "print(f\"Question: {selected_question}\")\n", - "print(\"-\" * 60)\n", - "\n", - "with mcp_client:\n", - " tools = mcp_client.list_tools_sync()\n", - " model = BedrockModel(model_id=MODEL_ID, region_name=REGION, temperature=0.5, max_tokens=3000)\n", - " agent = Agent(model=model, tools=tools, system_prompt=ITERATIVE_RESEARCH_PROMPT)\n", - " response = agent(selected_question)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "if hasattr(response, \"message\"):\n", - " for block in response.message.get(\"content\", []):\n", - " if block.get(\"text\"):\n", - " print(block[\"text\"])\n", - "else:\n", - " print(str(response))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Tuning the Loop\n", - "\n", - "Adjust `MAX_SEARCH_ITERATIONS` at the top of this notebook to control depth vs. latency/cost:\n", - "\n", - "| Setting | Effect | Use when |\n", - "|:--------|:-------|:---------|\n", - "| 2 | Fast, shallow | Simple factual questions |\n", - "| 4 (default) | Balanced | Most research tasks |\n", - "| 6+ | Deep, thorough | Complex comparative analysis |\n", - "\n", - "Each additional iteration costs one WebSearch call and one LLM call." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this notebook you built a research agent that:\n", - "- Plans its research before searching\n", - "- Reflects after each search to identify gaps\n", - "- Issues targeted follow-up queries until confident\n", - "- Synthesizes findings with cited sources\n", - "\n", - "This is the foundational multi-turn pattern. Both the CVE scanner (search per item) and earnings brief (chained search) are specializations of this loop, tuned for their specific data structures.\n", - "\n", - "### Summary: Three Multi-turn Patterns\n", - "\n", - "| Pattern | Trigger | Example |\n", - "|:--------|:--------|:--------|\n", - "| Search per item | Fixed list of inputs | CVE scanner |\n", - "| Chained search | Each result shapes next query | Earnings brief |\n", - "| Iterative (this) | Reflect on gaps, search until confident | General deep research |" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md deleted file mode 100644 index e187e650f..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# Iterative Research Agent — Search → Reflect → Search Loop - -## Overview - -In this example you will build a research agent that answers complex questions by planning its research, executing searches, reflecting on what it still doesn't know, and issuing follow-up queries — repeating until it can answer with confidence. - -This is a technique demonstration. The CVE Scanner and Earnings Brief examples both use this underlying pattern; this notebook makes it explicit and reusable. - -### Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebook) | -| AgentCore components | AgentCore Gateway | -| Agentic framework | Strands Agents | -| LLM model | Anthropic Claude Sonnet 4 | -| Tutorial vertical | Cross-vertical | -| Example complexity | Advanced | -| SDK used | boto3, strands-agents | - -### The Research Loop - -``` -Question - │ - ▼ -┌──────────────────────────────────────────┐ -│ Plan: What do I need to know? │ -│ → Break question into sub-questions │ -└──────────────┬───────────────────────────┘ - │ - ▼ -┌──────────────────────────────────────────┐ -│ Search: Execute highest-priority query │◀──┐ -└──────────────┬───────────────────────────┘ │ - │ │ - ▼ │ -┌──────────────────────────────────────────┐ │ -│ Reflect: What gaps remain? │ │ -│ → If gaps exist: refine query, repeat ──┼───┘ -│ → If confident: synthesize │ -└──────────────┬───────────────────────────┘ - │ - ▼ - Final answer with citations -``` - -### Why This Pattern Matters - -Single-shot search works for simple factual queries. For questions that require: -- Comparing multiple sources -- Reconciling conflicting information -- Drilling into details from a high-level result - -...you need the reflect-and-refine loop. This notebook teaches that loop explicitly with configurable max iterations and a confidence threshold. - -### Example Questions - -- "What are the key differences between Claude 4 and GPT-4.5 for code generation?" -- "What regulatory changes in 2026 affect EU-based AI startups?" -- "What happened to Silicon Valley Bank and what changed in US banking regulation after?" - -## Notebook - -→ [`03-iterative-research.ipynb`](03-iterative-research.ipynb) - -## Prerequisites - -Complete `01-gateway-setup-and-raw-mcp` and export the environment variables. - -```bash -pip install -r requirements.txt -``` diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/images/.gitkeep b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/images/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt deleted file mode 100644 index 1d8cae582..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -boto3 -mcp -requests -strands-agents>=0.1.8 diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md deleted file mode 100644 index f902c4fef..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Advanced Web Search Examples - -## Overview - -These examples go beyond single-shot search to demonstrate patterns where live web data is genuinely critical — not optional. Each example shows a realistic use case where stale training data would break the outcome. - -## Examples - -| Folder | Use Case | Key Pattern | -|:-------|:---------|:------------| -| [`01-cve-scanner/`](01-cve-scanner/) | Security vulnerability scan across a dependency list | Multi-turn: one search per package, chained reasoning | -| [`02-earnings-brief/`](02-earnings-brief/) | Pre-earnings call research brief | Multi-turn: analyst estimates → competitors → sector news → synthesis | -| [`03-iterative-research/`](03-iterative-research/) | Deep Q&A via search → reflect → search loop | Technique demo: explicit multi-turn research pattern | - -## When to Use Each Pattern - -### CVE Scanner — "search for each item in a list" -The agent receives a list of dependencies, searches for CVEs for each one, and produces a prioritized remediation report. The loop is driven by the input list, and each search result is independent. - -### Earnings Brief — "chained research where each search informs the next" -The agent starts with analyst consensus, finds gaps, searches competitors, then searches for macro context. The queries are generated dynamically — each result shapes what to search for next. - -### Iterative Research — "search until confident" -The agent explicitly plans its research, executes searches, reflects on gaps, and issues follow-up queries until it can answer with confidence. This is the foundational pattern the other two use cases build on. - -## Prerequisites - -All examples require a running AgentCore Gateway with Web Search connector target. -Complete `01-gateway-setup-and-raw-mcp` first and export the environment variables it produces. diff --git a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md b/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md deleted file mode 100644 index 00f6d3dca..000000000 --- a/06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/README.md +++ /dev/null @@ -1,127 +0,0 @@ -# Amazon Bedrock AgentCore Web Search Tool - -## Overview - -Amazon Bedrock AgentCore Web Search Tool exposes live web search as a fully managed, MCP-compliant tool through AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no search APIs to provision, no infrastructure to manage. - -This tutorial series covers setup, basic agent integration, and advanced multi-turn patterns where real-time web data is genuinely critical to the outcome. - -
- -
- -## How It Works - -The Web Search Tool uses the **connector** target type in AgentCore Gateway. You specify `connectorId: "web-search"` and the Gateway handles schema management, endpoint resolution, and outbound authentication automatically. - -``` -Your Agent (Strands / LangChain / any MCP client) - │ - │ OAuth Bearer token (inbound auth) - ▼ -AgentCore Gateway (MCP endpoint) - │ - │ IAM role (outbound auth — automatic) - ▼ -Web Search Backend - │ - ▼ -Structured results: { text, url, title, publishedDate } -``` - -**Inbound auth**: Amazon Cognito with `client_credentials` flow — your agent gets a token and passes it to the Gateway. - -**Outbound auth**: Fully automatic — the Gateway's IAM role authenticates to the Web Search backend. Nothing to configure. - -
- -
- -## Key Capabilities - -- **Real-time information** — live web results with titles, URLs, snippets, and publication dates -- **Zero infrastructure** — no search APIs, no hosting, no scaling configuration -- **Framework agnostic** — Strands Agents, LangChain, LangGraph, or any MCP client -- **MCP-native** — tool discovery and invocation via standard `tools/list` and `tools/call` - -## Tutorial Details - -| Information | Details | -|:------------|:--------| -| Tutorial type | Interactive (Jupyter Notebooks) | -| AgentCore components | AgentCore Gateway | -| Gateway target type | Connector (`web-search`) | -| Agentic frameworks | Strands Agents, LangChain + LangGraph | -| Inbound auth | Amazon Cognito (`client_credentials` flow) | -| Outbound auth | Automatic (Gateway IAM role) | -| LLM model | Anthropic Claude Sonnet 4 | -| Region | us-east-1 (Web Search connector availability) | -| SDK used | boto3 | - -## Tutorials Overview - -### Foundation Tutorials - -Run these in order — each tutorial builds on the previous one. - -| Tutorial | What You'll Learn | -|:---------|:-----------------| -| [01 — Gateway Setup and Raw MCP](01-gateway-setup-and-raw-mcp/) | Create IAM role, Cognito, Gateway, and Web Search target. Verify with direct MCP calls. | -| [02 — Web Search with Strands](02-web-search-with-strands/) | Full agent loop using Strands Agents and Claude Sonnet 4. | -| [03 — Web Search with LangChain](03-web-search-with-langchain/) | Same integration using LangChain + LangGraph. | - -### Advanced Examples - -These examples require the Gateway from tutorial 01. They demonstrate patterns where live web search is genuinely critical — not optional. - -| Example | Use Case | Pattern | -|:--------|:---------|:--------| -| [CVE Scanner](04-advanced-examples/01-cve-scanner/) | Scan dependencies for recent security vulnerabilities | Multi-turn: one search per package | -| [Earnings Brief](04-advanced-examples/02-earnings-brief/) | Pre-earnings call research brief for any ticker | Chained: each search informs the next | -| [Iterative Research](04-advanced-examples/03-iterative-research/) | Deep Q&A via search → reflect → search loop | Technique: explicit multi-turn pattern | - -## Prerequisites - -- AWS account allowlisted for the Web Search Tool private beta -- Python 3.10+ and Jupyter Notebook (or JupyterLab) -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions -- Access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-20250514-v1:0`) in us-east-1 - -```bash -pip install -r requirements.txt -``` - -> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. - -## Quick Start - -```bash -# Step 1: Set up Gateway (run once — reused by all other tutorials) -jupyter notebook 01-gateway-setup-and-raw-mcp/01-web-search-gateway-setup.ipynb - -# Step 2: Try with Strands -jupyter notebook 02-web-search-with-strands/02-web-search-strands-agent.ipynb - -# Step 3: Try with LangChain -jupyter notebook 03-web-search-with-langchain/03-web-search-langchain-agent.ipynb - -# Advanced: CVE Scanner -jupyter notebook 04-advanced-examples/01-cve-scanner/01-cve-scanner.ipynb - -# Advanced: Earnings Brief -jupyter notebook 04-advanced-examples/02-earnings-brief/02-earnings-brief.ipynb - -# Advanced: Iterative Research Pattern -jupyter notebook 04-advanced-examples/03-iterative-research/03-iterative-research.ipynb -``` - -## Files - -| Path | Description | -|:-----|:------------| -| `requirements.txt` | Shared Python dependencies | -| `images/` | Architecture diagrams | -| 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z3%+?B^VoAuRf3ct{uGK>eHWY^R(*Q+Z@eW%BJh&*qE6gL91k`QTSZ3cbiwWO|14** zXl;17_Q~EI8^1<}?m|Ei3}GFK13QWPNX0_$fMi}zB6#?pHXutE1YW zIV_}h3$M@W4h%HCFhlb{f_JH>{gnS-0x3E)iyvBpB~G?CX5EH*txJ#*)W-VH>8MU!28Tper`sWpN>>K32dHit_hUL{_iS zbkzK6{14;y|2gPaJ&=BC4(U@vS*U*`rx@waP(s3vEyz@~DS5_W<6v5T+G=F&XV1PBoEkB8WqKH&Itmi**+7R%aJp7X@PA6t=0.1.8 -mcp -requests -langchain -langchain-mcp-adapters -langchain-aws -langgraph From 2cb91d38a781ec73d27c7996b19837bbbdcf356e Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 9 Jun 2026 12:40:39 -0400 Subject: [PATCH 07/21] workshop read me clean up --- 06-workshops/05-AgentCore-tools/README.md | 28 +---------------------- 1 file changed, 1 insertion(+), 27 deletions(-) diff --git a/06-workshops/05-AgentCore-tools/README.md b/06-workshops/05-AgentCore-tools/README.md index cd0ee00fc..e7f322724 100644 --- a/06-workshops/05-AgentCore-tools/README.md +++ b/06-workshops/05-AgentCore-tools/README.md @@ -2,11 +2,10 @@ ## Overview Amazon Bedrock AgentCore Tools provide enterprise-grade capabilities that enhance AI agents' ability to perform complex -tasks securely and efficiently. This suite includes three tools: +tasks securely and efficiently. This suite includes tools such as: - Amazon Bedrock AgentCore Code Interpreter - Amazon Bedrock AgentCore Browser Tool -- Amazon Bedrock AgentCore Web Search Tool ## Amazon Bedrock AgentCore Code Interpreter @@ -49,32 +48,7 @@ tasks securely and efficiently. This suite includes three tools: - Large-scale data processing and monitoring - Secure code execution for AI agents in enterprise settings -## Amazon Bedrock AgentCore Web Search Tool - -### Key Features - -1. **Real-time Information Access**: Retrieve current web results with titles, URLs, snippets, and publication dates — no frozen training data. - -2. **Zero Infrastructure Management**: No search APIs to provision, no scaling to configure. Expose web search through AgentCore Gateway as a fully managed MCP connector. - -3. **Framework Agnostic**: Works with Strands Agents, LangChain, LangGraph, CrewAI, or any MCP-compatible client. - -### Benefits - -- **Grounded Responses**: Agents cite live sources rather than hallucinating outdated facts. -- **Critical for Time-Sensitive Use Cases**: CVE scanning, earnings research, regulatory monitoring — all require data that doesn't exist in training sets. -- **MCP-Native Discovery**: Agents discover and invoke the tool via standard `tools/list` and `tools/call` — no custom integration code. - -## Use Cases - -- Complex data analysis and visualization in secure environments -- Automated web interactions for form filling, data extraction, and multi-step processes -- Large-scale data processing and monitoring -- Secure code execution for AI agents in enterprise settings -- Real-time web grounding for agents that need current information - ## Tutorials Overview 1. [Amazon Bedrock AgentCore Code Interpreter](01-Agent-Core-code-interpreter) 2. [Amazon Bedrock AgentCore Browser Tool](02-Agent-Core-browser-tool) -3. [Amazon Bedrock AgentCore Web Search Tool](03-Agent-Core-web-search) From 32c52ecee46cf9d073a762cabb4568ab486542bb Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 9 Jun 2026 13:00:41 -0400 Subject: [PATCH 08/21] docs: remove web-search workshop references and add deep-research-agent to use-cases README --- 02-use-cases/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/02-use-cases/README.md b/02-use-cases/README.md index f97b0ea25..d98224370 100644 --- a/02-use-cases/README.md +++ b/02-use-cases/README.md @@ -9,6 +9,7 @@ This folder contains end-to-end applications that demonstrate how to apply Amazo * **[AWS Operations Agent](./AWS-operations-agent/)**: Intelligent AWS operations assistant with Okta authentication and comprehensive monitoring capabilities * **[Customer Support Assistant](./customer-support-assistant/)**: Production-ready customer service agent with memory, knowledge base integration, and Google OAuth * **[DB Performance Analyzer](./DB-performance-analyzer/)**: Database performance monitoring and analysis agent with PostgreSQL integration +* **[Deep Research Agent](./deep-research-agent/)**: Iterative web research agent using AgentCore Web Search Tool with auto-provisioning Gateway setup and configurable search depth * **[Device Management Agent](./device-management-agent/)**: IoT device management system with Cognito authentication and real-time monitoring * **[Enterprise Web Intelligence Agent](./enterprise-web-intelligence-agent/)**: Web research and analysis agent using browser tools for competitive intelligence * **[Farm Management Advisor](./farm-management-advisor/)**: Agricultural advisory system with plant detection, weather forecasting, and care recommendations From 7ea8c5b96467d6633fd338394cd55909e111a116 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Fri, 12 Jun 2026 15:23:22 -0400 Subject: [PATCH 09/21] security: write credentials to .env file instead of logging to stdout Resolves CodeQL finding: clear-text logging of sensitive information. Setup scripts now write credentials to .env.web-search (gitignored) instead of printing secrets to stdout. All READMEs updated to reference 'source .env.web-search' instead of manual export. --- .gitignore | 1 + .../03-web-search/01-setup-gateway/README.md | 24 +++++++++----- .../01-setup-gateway/setup_gateway.py | 31 ++++++++++++------- .../03-web-search/02-raw-mcp/README.md | 2 +- .../03-web-search/03-strands-agent/README.md | 2 +- .../04-langchain-agent/README.md | 2 +- .../03-web-search/README.md | 9 ++---- .../README.md | 3 +- 02-use-cases/deep-research-agent/README.md | 10 ++++-- .../deep-research-agent/gateway_setup.py | 28 ++++++++++------- 10 files changed, 69 insertions(+), 43 deletions(-) diff --git a/.gitignore b/.gitignore index 07f1ca5d0..18cf6029f 100644 --- a/.gitignore +++ b/.gitignore @@ -163,6 +163,7 @@ celerybeat.pid # Environments .env +.env.* .venv env/ venv/ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md index 9a940cdb1..61774ba7f 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -71,18 +71,26 @@ python setup_gateway.py --gateway-name my-web-search-gw python setup_gateway.py --region us-east-1 ``` -After completion, the script prints environment variables to export: +After completion, the script writes credentials to a local `.env.web-search` file: ```bash -export AGENTCORE_GATEWAY_URL="https://..." -export COGNITO_DOMAIN="..." -export COGNITO_CLIENT_ID="..." -export COGNITO_CLIENT_SECRET="..." -export COGNITO_SCOPE="agentcore-websearch/invoke" -export AWS_DEFAULT_REGION="us-east-1" +source .env.web-search ``` -Export these before running the other demos. +This loads the following variables into your shell: + +```bash +AGENTCORE_GATEWAY_URL="https://..." +COGNITO_DOMAIN="..." +COGNITO_CLIENT_ID="..." +COGNITO_CLIENT_SECRET="..." +COGNITO_SCOPE="agentcore-websearch/invoke" +AWS_DEFAULT_REGION="us-east-1" +``` + +Source this file before running the other demos. + +> **⚠️ Security**: The `.env.web-search` file contains your client secret. Keep it secure and do not commit it to version control (it's already in `.gitignore`). ## IAM Permissions diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py index 3ca062cd2..dc287a505 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py @@ -7,7 +7,7 @@ 3. AgentCore Gateway with MCP protocol and JWT authorization 4. Web Search Tool connector target -After running this script, export the printed environment variables to use +After running this script, load credentials with `source .env.web-search` to use with the other demos in this folder. Prerequisites: @@ -335,16 +335,25 @@ def main(): print("\n" + "=" * 60) print("SETUP COMPLETE") print("=" * 60) - print("\nExport these environment variables to use with other demos:\n") - print(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"") - print(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"") - print(f"export COGNITO_CLIENT_ID=\"{cognito_config['client_id']}\"") - print(f"export COGNITO_CLIENT_SECRET=\"{cognito_config['client_secret']}\"") - print(f"export COGNITO_SCOPE=\"{cognito_config['scope']}\"") - print(f"export AWS_DEFAULT_REGION=\"{region}\"") - print(f"\n# Gateway ID (for cleanup): {gateway_id}") - print(f"# IAM Role: {role_name}") - print(f"# Cognito Pool: {cognito_config['user_pool_id']}") + + # Write credentials to a local .env file (avoids logging secrets to stdout) + env_file = ".env.web-search" + with open(env_file, "w") as f: + f.write(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"\n") + f.write(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"\n") + f.write(f"export COGNITO_CLIENT_ID=\"{cognito_config['client_id']}\"\n") + f.write(f"export COGNITO_CLIENT_SECRET=\"{cognito_config['client_secret']}\"\n") # noqa: E501 + f.write(f"export COGNITO_SCOPE=\"{cognito_config['scope']}\"\n") + f.write(f"export AWS_DEFAULT_REGION=\"{region}\"\n") + + print(f"\n✅ Credentials written to: {env_file}") + print(" Load them with: source .env.web-search\n") + print(f" Gateway URL: {gateway_url}") + print(f" Gateway ID: {gateway_id} (for cleanup)") + print(f" IAM Role: {role_name}") + print(f" Cognito Pool: {cognito_config['user_pool_id']}") + print(f"\n⚠️ Keep {env_file} secure — it contains your client secret.") + print(f" Add it to .gitignore to avoid committing it.") if __name__ == "__main__": diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md index 73182422b..49e8f4527 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -68,7 +68,7 @@ Calling the tool with a query returns structured results: pip install -r ../requirements.txt ``` -Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. +Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` ## Usage diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md index 28060b839..86c8ba773 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md @@ -63,7 +63,7 @@ The `create_mcp_client()` function handles OAuth token retrieval and transport c pip install -r ../requirements.txt ``` -Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. +Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md index cb69c8ef6..908e7cf0f 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md @@ -67,7 +67,7 @@ LangChain's MCP adapter uses async I/O. The demo wraps the agent call in `asynci pip install -r ../requirements.txt ``` -Run `01-setup-gateway/setup_gateway.py` first and export the environment variables it prints. +Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index ff094209a..ac1e7551b 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -108,13 +108,8 @@ pip install -r requirements.txt # Step 1: Create Gateway and Web Search target python 01-setup-gateway/setup_gateway.py -# Step 2: Export the environment variables printed by setup -export AGENTCORE_GATEWAY_URL="..." -export COGNITO_DOMAIN="..." -export COGNITO_CLIENT_ID="..." -export COGNITO_CLIENT_SECRET="..." -export COGNITO_SCOPE="agentcore-websearch/invoke" -export AWS_DEFAULT_REGION="us-east-1" +# Step 2: Load the credentials written by setup +source .env.web-search # Optional: override the default Bedrock model export BEDROCK_MODEL_ID="us.anthropic.claude-3-5-sonnet-20241022-v2:0" diff --git a/01-features/03-connect-your-agent-to-anything/README.md b/01-features/03-connect-your-agent-to-anything/README.md index 2a5db4e85..6fa5e51d9 100644 --- a/01-features/03-connect-your-agent-to-anything/README.md +++ b/01-features/03-connect-your-agent-to-anything/README.md @@ -80,7 +80,8 @@ python 02-browser/01-nova-act/getting_started.py \ # Web Search Tool pip install -r 03-web-search/requirements.txt python 03-web-search/01-setup-gateway/setup_gateway.py -# Export the printed env vars, then: +# Load the credentials written by setup: +source .env.web-search python 03-web-search/03-strands-agent/web_search_strands.py ``` diff --git a/02-use-cases/deep-research-agent/README.md b/02-use-cases/deep-research-agent/README.md index 2839b0c05..e48dd106a 100644 --- a/02-use-cases/deep-research-agent/README.md +++ b/02-use-cases/deep-research-agent/README.md @@ -147,11 +147,17 @@ On first run without environment variables, the agent will: 1. Scan your account for an existing Gateway with a Web Search target 2. If found, reuse it automatically 3. If not found, prompt you to create one (~60 seconds) -4. Print `export` commands to skip this step on future runs +4. Write credentials to `.env.web-search` for future runs ### 3. (Optional) Pre-configure environment variables -If you already have a Gateway or want to skip auto-detection: +If you already have a Gateway from a previous setup, source the credentials file: + +```bash +source .env.web-search +``` + +Or set them manually: ```bash export AGENTCORE_GATEWAY_URL="https://..." diff --git a/02-use-cases/deep-research-agent/gateway_setup.py b/02-use-cases/deep-research-agent/gateway_setup.py index 7ad94b587..1de63f7b7 100644 --- a/02-use-cases/deep-research-agent/gateway_setup.py +++ b/02-use-cases/deep-research-agent/gateway_setup.py @@ -80,20 +80,26 @@ def export_to_env(self): os.environ["AWS_DEFAULT_REGION"] = self.region def print_env_vars(self): - """Print export commands for the user to save.""" - print("\n To reuse this gateway in future sessions, export these variables:\n") - print(f' export AGENTCORE_GATEWAY_URL="{self.gateway_url}"') - print(f' export COGNITO_DOMAIN="{self.cognito_domain}"') - print(f' export COGNITO_CLIENT_ID="{self.cognito_client_id}"') - print(f' export COGNITO_CLIENT_SECRET="{self.cognito_client_secret}"') - print(f' export COGNITO_SCOPE="{self.cognito_scope}"') - print(f' export AWS_DEFAULT_REGION="{self.region}"') + """Write credentials to a local .env file and print non-sensitive info.""" + env_file = ".env.web-search" + with open(env_file, "w") as f: + f.write(f'export AGENTCORE_GATEWAY_URL="{self.gateway_url}"\n') + f.write(f'export COGNITO_DOMAIN="{self.cognito_domain}"\n') + f.write(f'export COGNITO_CLIENT_ID="{self.cognito_client_id}"\n') + f.write(f'export COGNITO_CLIENT_SECRET="{self.cognito_client_secret}"\n') + f.write(f'export COGNITO_SCOPE="{self.cognito_scope}"\n') + f.write(f'export AWS_DEFAULT_REGION="{self.region}"\n') + + print(f"\n ✅ Credentials written to: {env_file}") + print(f" Load them with: source {env_file}\n") + print(f" Gateway URL: {self.gateway_url}") if self.gateway_id: - print(f"\n # Gateway ID (for cleanup): {self.gateway_id}") + print(f" Gateway ID: {self.gateway_id} (for cleanup)") if self.role_name: - print(f" # IAM Role: {self.role_name}") + print(f" IAM Role: {self.role_name}") if self.user_pool_id: - print(f" # Cognito Pool: {self.user_pool_id}") + print(f" Cognito Pool: {self.user_pool_id}") + print(f"\n ⚠️ Keep {env_file} secure — it contains your client secret.") # ── Detection: check env vars ───────────────────────────────────────────────── From 7a5431ff8d1a3e084cc4a636d2c7ef2752502f8f Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Fri, 12 Jun 2026 16:47:45 -0400 Subject: [PATCH 10/21] fix: add request timeout and update search privacy wording - Add timeout=10 to requests.post calls (resolves CodeQL finding) - Simplify search privacy notice (remove 'licensed content' reference) --- .../03-web-search/01-setup-gateway/README.md | 2 +- .../03-web-search/02-raw-mcp/README.md | 2 +- .../03-web-search/03-strands-agent/README.md | 2 +- .../03-web-search/04-langchain-agent/README.md | 2 +- .../03-connect-your-agent-to-anything/03-web-search/README.md | 2 +- .../03-web-search/utils/gateway_auth.py | 1 + 02-use-cases/deep-research-agent/deep_research_agent.py | 1 + 7 files changed, 7 insertions(+), 5 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md index 61774ba7f..f4a035e1f 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md @@ -4,7 +4,7 @@ This script creates all the infrastructure needed to expose the Web Search Tool through an AgentCore Gateway. After running it, you'll have a fully functional MCP endpoint that any agent framework can connect to. -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. ``` ┌──────────────────┐ ┌─────────────────────────────────────┐ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md index 49e8f4527..964472d14 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -4,7 +4,7 @@ This demo calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target infrastructure are working correctly. -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. ``` ┌──────────────────┐ tools/list ┌─────────────────────────────────┐ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md index 86c8ba773..34c8cd169 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md @@ -4,7 +4,7 @@ This demo shows the complete agent integration: a Strands agent automatically discovers and invokes the Web Search Tool to answer real-time questions with cited sources. -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. ``` ┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md index 908e7cf0f..a839beb26 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md @@ -4,7 +4,7 @@ This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. ``` ┌────────────┐ "Latest AWS announcements?" ┌──────────────────────────┐ │ User │ ─────────────────────────────▶ │ LangChain Agent │ diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index ac1e7551b..fc4923a54 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -4,7 +4,7 @@ [Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained index built from open-source and licensed content providers. No queries are routed to third-party search engines or external providers. +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. ![Web Search Tool Architecture](images/tutorial-architecture.png) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py index 214b2ef0f..00e082f7c 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py @@ -69,6 +69,7 @@ def get_oauth_token( "client_secret": client_secret, "scope": scope, }, + timeout=10, ) response.raise_for_status() return response.json()["access_token"] diff --git a/02-use-cases/deep-research-agent/deep_research_agent.py b/02-use-cases/deep-research-agent/deep_research_agent.py index a8cd0a1a5..3c48102ac 100644 --- a/02-use-cases/deep-research-agent/deep_research_agent.py +++ b/02-use-cases/deep-research-agent/deep_research_agent.py @@ -103,6 +103,7 @@ def get_oauth_token() -> str: "client_secret": cognito_client_secret, "scope": cognito_scope, }, + timeout=10, ) if resp.status_code != 200: error_detail = resp.text From 9475ce923a3723fc32e4874b210f005f48240ed4 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Fri, 12 Jun 2026 17:38:48 -0400 Subject: [PATCH 11/21] Fixed recommendations from vedashreevinay --- .../03-web-search/03-strands-agent/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md index 34c8cd169..c7a90957f 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md @@ -34,7 +34,7 @@ The agent connects to the Gateway via MCP Streamable HTTP and calls `tools/list` When you ask a question: 1. Strands sends the query + tool schema to Claude Sonnet 4 -2. Claude decides to call `WebSearch` with a concise search query +2. Claude(the Strands Agent model) decides to call `WebSearch` with a concise search query 3. Strands invokes the tool via MCP `tools/call` on the Gateway 4. The Gateway routes to the Web Search connector and returns results 5. Results are fed back to Claude as a tool result From 27da7370c1608eef40a0036be2a11269d31fc2f3 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Sun, 14 Jun 2026 21:22:53 -0400 Subject: [PATCH 12/21] deep research agent use cases restructure + updated ascii workflow to architecture png --- .../03-web-search/02-raw-mcp/README.md | 10 +--- .../images/raw-mcp-call-architecture.png | Bin 0 -> 115417 bytes .../03-web-search/03-strands-agent/README.md | 18 +------ .../strands-web-search-architecture.png | Bin 0 -> 141999 bytes .../04-langchain-agent/README.md | 17 +----- .../langchain-web-search-architecture.png | Bin 0 -> 146371 bytes .../03-web-search/README.md | 13 +---- .../agentcore-web-search-architecture.png | Bin 0 -> 202864 bytes .../01-conversational-agents/README.md | 1 + .../deep-research-agent/README.md | 49 ++---------------- .../deep_research_agent.py | 0 .../deep-research-agent/gateway_setup.py | 0 .../deep-research-agent-architecture.png | Bin 0 -> 211659 bytes .../images/deep-research-loop-diagram.png | Bin 0 -> 122187 bytes .../deep-research-agent/requirements.txt | 0 02-use-cases/README.md | 1 + 16 files changed, 13 insertions(+), 96 deletions(-) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/images/raw-mcp-call-architecture.png create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/images/strands-web-search-architecture.png create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/images/langchain-web-search-architecture.png create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/images/agentcore-web-search-architecture.png rename 02-use-cases/{ => 01-conversational-agents}/deep-research-agent/README.md (70%) rename 02-use-cases/{ => 01-conversational-agents}/deep-research-agent/deep_research_agent.py (100%) rename 02-use-cases/{ => 01-conversational-agents}/deep-research-agent/gateway_setup.py (100%) create mode 100644 02-use-cases/01-conversational-agents/deep-research-agent/images/deep-research-agent-architecture.png create mode 100644 02-use-cases/01-conversational-agents/deep-research-agent/images/deep-research-loop-diagram.png rename 02-use-cases/{ => 01-conversational-agents}/deep-research-agent/requirements.txt (100%) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md index 964472d14..a75306d3e 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md @@ -6,15 +6,7 @@ This demo calls the AgentCore Gateway directly over the MCP protocol — no agen > 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. -``` -┌──────────────────┐ tools/list ┌─────────────────────────────────┐ -│ raw_mcp_call.py │ ──────────────▶ │ AgentCore Gateway │ -│ │ │ │ -│ MCPClient │ tools/call │ Target: web-search (connector) │ -│ (Streamable │ ──────────────▶ │ │ -│ HTTP) │◀────────────── │ → WebSearch results │ -└──────────────────┘ results └─────────────────────────────────┘ -``` +![Raw MCP Client web search Architecture](images/raw-mcp-call-architecture.png) ## How It Works diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/images/raw-mcp-call-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/images/raw-mcp-call-architecture.png new file mode 100644 index 0000000000000000000000000000000000000000..0868daf2369936303372931095222252bedfd2fd GIT binary patch literal 115417 zcmeFZ1wd5W+CDCzfHZ>AB`w{cgrtOkfQm>-3?Typ!yrSK64Ho-AV`~}baxC5(%p!3 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Queries do not route to any third-party search engines or external providers. -``` -┌────────────┐ "What's the latest AI news?" ┌──────────────────────────┐ -│ User │ ─────────────────────────────▶ │ Strands Agent │ -│ │◀───────────────────────────── │ (Claude Sonnet 4) │ -│ │ "Here's what I found: [...]" │ │ -└────────────┘ │ tools: [WebSearch] │ - │ │ │ - └───────┼──────────────────┘ - │ MCP tools/call - ▼ - ┌──────────────────────────┐ - │ AgentCore Gateway │ - │ → Web Search Connector │ - │ → Structured results │ - └──────────────────────────┘ -``` + +![Strands Agent web search Architecture](images/strands-web-search-architecture.png) ## How It Works diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/images/strands-web-search-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/images/strands-web-search-architecture.png new file mode 100644 index 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a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md index a839beb26..024e2d75a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md @@ -5,21 +5,8 @@ This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. > 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. -``` -┌────────────┐ "Latest AWS announcements?" ┌──────────────────────────┐ -│ User │ ─────────────────────────────▶ │ LangChain Agent │ -│ │◀───────────────────────────── │ (ChatBedrockConverse) │ -│ │ "Here's what I found: [...]" │ │ -└────────────┘ │ tools from MCP client │ - │ │ │ - └───────┼──────────────────┘ - │ MultiServerMCPClient - ▼ - ┌──────────────────────────┐ - │ AgentCore Gateway │ - │ → Web Search Connector │ - └──────────────────────────┘ -``` + +![Langchain web search Architecture](images/langchain-web-search-architecture.png) ## How It Works diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/images/langchain-web-search-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/images/langchain-web-search-architecture.png new file mode 100644 index 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a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -14,18 +14,7 @@ The Web Search Tool uses the **connector** target type — a fully AWS-managed integration that requires no schema, no endpoint configuration, and no outbound credential setup. You specify `connectorId: "web-search"` and the Gateway handles everything else. -``` -┌─────────────┐ OAuth Token ┌──────────────────────────────────┐ -│ Your Agent │ ───────────────▶ │ AgentCore Gateway (MCP) │ -│ (Strands, │ │ │ -│ LangChain, │ tools/list │ ┌────────────────────────────┐ │ -│ or any MCP │ ───────────────▶ │ │ Web Search Connector │ │ -│ client) │ │ │ connectorId: "web-search" │ │ -│ │ tools/call │ │ │ │ -│ │ ───────────────▶ │ │ → Real-time web results │ │ -│ │◀─────────────── │ └────────────────────────────┘ │ -└─────────────┘ results └──────────────────────────────────┘ -``` +![Web search with Amazon Bedrock AgentCore Gateway](images/agentcore-web-search-architecture.png) ### Key Capabilities diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/images/agentcore-web-search-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/images/agentcore-web-search-architecture.png new file mode 100644 index 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Advanced | Runtime, Gateway, Memory, A2A using Strands + OpenAI Agents + Google ADK | | [AWS-operations-agent](./AWS-operations-agent/) | Cloud Operations | Advanced | Runtime, Gateway, Memory, Policy, Observability; built with Strands, ADK, and OpenAI Agents SDK | | [customer-support-assistant-vpc](./customer-support-assistant-vpc/) | Retail / E-commerce | Intermediate | Runtime, Gateway deployed inside a VPC with private endpoints | +| [deep-research-agent](./deep-research-agent/) | Research / Q&A | Intermediate | Runtime, Gateway (Web Search); iterative Plan → Search → Reflect → Synthesize loop with auto-provisioning | | [device-management-agent](./device-management-agent/) | IoT / Smart Home | Intermediate | Runtime, Gateway, Policy, Identity (Cognito); React frontend | | [finance-personal-assistant](./finance-personal-assistant/) | Personal Finance | Beginner | Gateway, Policy; notebook-based | | [healthcare-appointment-agent](./healthcare-appointment-agent/) | Healthcare | Intermediate | Runtime, Gateway, Policy, Observability; FHIR R4 via HealthLake | diff --git a/02-use-cases/deep-research-agent/README.md b/02-use-cases/01-conversational-agents/deep-research-agent/README.md similarity index 70% rename from 02-use-cases/deep-research-agent/README.md rename to 02-use-cases/01-conversational-agents/deep-research-agent/README.md index e48dd106a..5521c6df7 100644 --- a/02-use-cases/deep-research-agent/README.md +++ b/02-use-cases/01-conversational-agents/deep-research-agent/README.md @@ -6,30 +6,7 @@ An intelligent research agent powered by Amazon Bedrock AgentCore and Claude Son Single-shot web search works for simple factual queries. For questions that require comparing multiple sources, reconciling conflicting information, or drilling into details revealed by earlier results, you need a reflect-and-refine loop. The Deep Research Agent makes that loop explicit and configurable. -``` -Question - │ - ▼ -┌──────────────────────────────────────────┐ -│ PLAN: Break into prioritised │ -│ sub-questions │ -└──────────────┬───────────────────────────┘ - │ - ▼ -┌──────────────────────────────────────────┐ -│ SEARCH: Execute highest-priority query │◀──┐ -└──────────────┬───────────────────────────┘ │ - │ │ - ▼ │ -┌──────────────────────────────────────────┐ │ -│ REFLECT: What gaps remain? │ │ -│ → Gaps found: refine query, repeat ─────┼───┘ -│ → Confident: synthesize │ -└──────────────┬───────────────────────────┘ - │ - ▼ - Comprehensive answer with citations -``` +![Deep Research Agent-Iterative Research Loop](images/deep-research-loop-diagram.png) | Information | Details | |:------------|:--------| @@ -62,22 +39,7 @@ Question ## Architecture -``` -┌─────────────┐ Research question ┌──────────────────────────────────┐ -│ User / │ ───────────────────▶ │ Deep Research Agent │ -│ Client │ │ (Claude Sonnet 4 via Strands) │ -│ │◀─────────────────── │ │ -│ │ Cited research │ Plan → Search → Reflect loop │ -└─────────────┘ report │ │ │ - └───────┼──────────────────────────┘ - │ MCP tools/call - ▼ - ┌──────────────────────────────────┐ - │ AgentCore Gateway │ - │ → Web Search Connector │ - │ → Real-time web results │ - └──────────────────────────────────┘ -``` +![Deep Research Agent with Amazon Bedrock AgentCore](images/deep-research-agent-architecture.png) ## Features @@ -126,7 +88,7 @@ On subsequent runs, if you export the printed variables, only `bedrock:InvokeMod ### 1. Install dependencies ```bash -cd 02-use-cases/deep-research-agent +cd 02-use-cases/01-conversational-agents/deep-research-agent pip install -r requirements.txt ``` @@ -192,7 +154,7 @@ When deployed, invoke via the AgentCore Runtime API: When you're done, remove all provisioned resources using the shared cleanup script: ```bash -python ../../01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py \ +python ../../../01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py \ --gateway-id \ --user-pool-id \ --role-name @@ -259,6 +221,5 @@ deep_research_agent.py ## Related Resources -- [`01-features/03-connect-your-agent-to-anything/03-web-search/`](../../01-features/03-connect-your-agent-to-anything/03-web-search/) — Gateway setup, raw MCP, and basic agent demos -- [`06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/`](../../06-workshops/05-AgentCore-tools/03-Agent-Core-web-search/04-advanced-examples/03-iterative-research/) — Jupyter Notebook version of this pattern (workshop format) +- [`01-features/03-connect-your-agent-to-anything/03-web-search/`](../../../01-features/03-connect-your-agent-to-anything/03-web-search/) — Gateway setup, raw MCP, and basic agent demos - [AgentCore Gateway documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) diff --git a/02-use-cases/deep-research-agent/deep_research_agent.py b/02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py similarity index 100% rename from 02-use-cases/deep-research-agent/deep_research_agent.py rename to 02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py diff --git a/02-use-cases/deep-research-agent/gateway_setup.py 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Users authenticate through an iden | [A2A-multi-agent-incident-response](./01-conversational-agents/A2A-multi-agent-incident-response/) | IT / DevOps | Runtime, Gateway, Memory, A2A (3 frameworks) | | [AWS-operations-agent](./01-conversational-agents/AWS-operations-agent/) | Cloud Operations | Runtime, Gateway, Memory, Policy, Observability | | [customer-support-assistant-vpc](./01-conversational-agents/customer-support-assistant-vpc/) | Retail / E-commerce | Runtime, Gateway (VPC) | +| [deep-research-agent](./01-conversational-agents/deep-research-agent/) | Research / Q&A | Gateway (Web Search), Runtime | | [device-management-agent](./01-conversational-agents/device-management-agent/) | IoT / Smart Home | Runtime, Gateway, Policy, Identity (Cognito) | | [finance-personal-assistant](./01-conversational-agents/finance-personal-assistant/) | Personal Finance | Gateway, Policy | | [healthcare-appointment-agent](./01-conversational-agents/healthcare-appointment-agent/) | Healthcare | Runtime, Gateway, Policy, Observability (FHIR R4) | From a6fb30f02a8e0d6dc28f648caa90464fba544f12 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 15 Jun 2026 03:22:58 -0400 Subject: [PATCH 13/21] refactor: restructure web-search by key learning and add self-contained cleanup - Reorganize 03-web-search/ into standalone samples: 01-raw-mcp, 02-strands-agent, 03-langchain-agent - Each sample includes setup_gateway.py, demo script, cleanup.py, images, and README - Remove separate 01-setup-gateway/ and 05-cleanup/ folders - Move shared setup/cleanup logic to utils/ - Add cleanup.py to deep-research-agent use case - Update all READMEs to template format (Core Concepts, Tutorials, Quick Start, How It Works) - Add request timeout to gateway_auth.py - Pin boto3>=1.38.0 in requirements.txt --- .../03-web-search/01-raw-mcp/README.md | 124 ++++++++++++ .../{05-cleanup => 01-raw-mcp}/cleanup.py | 15 +- .../images/raw-mcp-call-architecture.png | Bin .../raw_mcp_call.py | 0 .../03-web-search/01-raw-mcp/setup_gateway.py | 17 ++ .../03-web-search/01-setup-gateway/README.md | 143 -------------- .../03-web-search/02-raw-mcp/README.md | 84 -------- .../03-web-search/02-strands-agent/README.md | 117 ++++++++++++ .../03-web-search/02-strands-agent/cleanup.py | 129 +++++++++++++ .../strands-web-search-architecture.png | Bin .../02-strands-agent/setup_gateway.py | 17 ++ .../web_search_strands.py | 0 .../03-langchain-agent/README.md | 122 ++++++++++++ .../03-langchain-agent/cleanup.py | 129 +++++++++++++ .../langchain-web-search-architecture.png | Bin .../03-langchain-agent/setup_gateway.py | 15 ++ .../web_search_langchain.py | 0 .../03-web-search/03-strands-agent/README.md | 85 --------- .../04-langchain-agent/README.md | 87 --------- .../03-web-search/05-cleanup/README.md | 22 --- .../03-web-search/README.md | 179 +++++------------- .../03-web-search/requirements.txt | 3 +- .../gateway_setup.py} | 6 +- .../deep-research-agent/README.md | 24 ++- .../deep-research-agent/cleanup.py | 135 +++++++++++++ .../deep-research-agent/requirements.txt | 6 +- 26 files changed, 884 insertions(+), 575 deletions(-) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md rename 01-features/03-connect-your-agent-to-anything/03-web-search/{05-cleanup => 01-raw-mcp}/cleanup.py (85%) rename 01-features/03-connect-your-agent-to-anything/03-web-search/{02-raw-mcp => 01-raw-mcp}/images/raw-mcp-call-architecture.png (100%) rename 01-features/03-connect-your-agent-to-anything/03-web-search/{02-raw-mcp => 01-raw-mcp}/raw_mcp_call.py (100%) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py delete mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md delete mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py rename 01-features/03-connect-your-agent-to-anything/03-web-search/{03-strands-agent => 02-strands-agent}/images/strands-web-search-architecture.png (100%) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py rename 01-features/03-connect-your-agent-to-anything/03-web-search/{03-strands-agent => 02-strands-agent}/web_search_strands.py (100%) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py rename 01-features/03-connect-your-agent-to-anything/03-web-search/{04-langchain-agent => 03-langchain-agent}/images/langchain-web-search-architecture.png (100%) create mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py rename 01-features/03-connect-your-agent-to-anything/03-web-search/{04-langchain-agent => 03-langchain-agent}/web_search_langchain.py (100%) delete mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md delete mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md delete mode 100644 01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md rename 01-features/03-connect-your-agent-to-anything/03-web-search/{01-setup-gateway/setup_gateway.py => utils/gateway_setup.py} (98%) create mode 100644 02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md new file mode 100644 index 000000000..ac591c89b --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md @@ -0,0 +1,124 @@ +# Raw MCP Tool Discovery and Invocation + +## Overview + +This example calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target are working correctly. + +> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. + +![Raw MCP Client web search Architecture](images/raw-mcp-call-architecture.png) + +## Prerequisites + +- Python 3.10+ +- AWS account with Amazon Bedrock enabled in **us-east-1** +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions + +## How It Works + +The script performs three operations against the Gateway: + +### Step 1: Authenticate + +An OAuth token is obtained from Cognito using the `client_credentials` flow. The token is attached as a `Bearer` header on the MCP Streamable HTTP transport. This is handled internally by `create_streamable_http_transport()`. + +```python +from utils.gateway_auth import create_streamable_http_transport + +transport = create_streamable_http_transport() +``` + +### Step 2: Discover Tools (`tools/list`) + +The MCP `tools/list` call returns all tools available on the Gateway. For a Gateway with the Web Search connector target, you'll see: + +```json +{ + "name": "WebSearch", + "description": "Search the web for current information", + "inputSchema": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query (max 200 characters)" + } + }, + "required": ["query"] + } +} +``` + +### Step 3: Invoke the Tool (`tools/call`) + +Calling the tool with a query returns structured results: + +```json +{ + "results": [ + { + "text": "Snippet from the web page...", + "url": "https://example.com/article", + "title": "Article Title", + "publishedDate": "2026-05-28" + } + ] +} +``` + +## Files + +| File | Description | +|:-----|:------------| +| `raw_mcp_call.py` | Main example script — tool discovery and invocation | + +## Quick Start + +```bash +# Install dependencies +pip install -r ../requirements.txt + +# Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) +python setup_gateway.py +#python setup_gateway.py --gateway-name my-web-search-gw + +# Load credentials into your shell +source .env.web-search + +# Run the example — discovers WebSearch tool and invokes it +python raw_mcp_call.py + +# Try a custom query +python raw_mcp_call.py --query "Latest Python release" +python raw_mcp_call.py --query "AWS re:Invent 2026 announcements" +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--query` | No | Search query (default: built-in example query) | + +## Cleanup (Optional) + +When you're done remove all provisioned AWS resources: + +**1. Retrieve resource IDs** from the setup output (printed when you ran `setup_gateway.py`): + +``` +Gateway ID: +IAM Role: agentcore-web-search-gateway-role +Cognito Pool: +``` + +> **Tip:** If you no longer have the terminal output, the gateway ID is the subdomain prefix in your `AGENTCORE_GATEWAY_URL` (e.g., `gw-abc123` from `https://gw-abc123.gateway.bedrock-agentcore...`). The IAM role follows the pattern `agentcore--role`. + +**2. Run cleanup:** + +```bash +python cleanup.py --gateway-id --user-pool-id --role-name +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--gateway-id` | Yes | Gateway ID | +| `--user-pool-id` | Yes | Cognito User Pool ID | +| `--role-name` | Yes | IAM role name | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py similarity index 85% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py rename to 01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py index 82119344a..8d42e1b8b 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py @@ -21,14 +21,9 @@ import boto3 -# ── Configuration ───────────────────────────────────────────────────────────── - REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") -# ── Cleanup Steps ───────────────────────────────────────────────────────────── - - def delete_gateway(gateway_client, gateway_id): """Delete all targets and the gateway itself.""" print("\n[1/3] Deleting Gateway resources...") @@ -67,7 +62,6 @@ def delete_iam_role(iam_client, role_name): """Delete the IAM role and its inline policies.""" print("\n[3/3] Deleting IAM resources...") try: - # Delete inline policies policies = iam_client.list_role_policies(RoleName=role_name) for policy_name in policies["PolicyNames"]: iam_client.delete_role_policy( @@ -81,9 +75,6 @@ def delete_iam_role(iam_client, role_name): print(f" Error deleting IAM role: {e}") -# ── Main ────────────────────────────────────────────────────────────────────── - - def parse_args(): parser = argparse.ArgumentParser( description="Clean up Web Search Tool Gateway resources" @@ -123,6 +114,12 @@ def main(): delete_cognito(cognito_client, args.user_pool_id) delete_iam_role(iam_client, args.role_name) + # Remove local credentials file + env_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env.web-search") + if os.path.exists(env_file): + os.remove(env_file) + print(f"\n Deleted local credentials file: {env_file}") + print("\n" + "=" * 60) print("✅ All resources cleaned up successfully!") print("=" * 60) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/images/raw-mcp-call-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/images/raw-mcp-call-architecture.png similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/images/raw-mcp-call-architecture.png rename to 01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/images/raw-mcp-call-architecture.png diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/raw_mcp_call.py rename to 01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py new file mode 100644 index 000000000..b10080a6c --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py @@ -0,0 +1,17 @@ +"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. +After running this script, load credentials with `source .env.web-search` to use +with the other web search examples. + +Usage: + python setup_gateway.py + python setup_gateway.py --gateway-name my-gateway + python setup_gateway.py --region us-east-1 +""" +import sys +import os + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.gateway_setup import main + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md deleted file mode 100644 index f4a035e1f..000000000 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/README.md +++ /dev/null @@ -1,143 +0,0 @@ -# Set Up AgentCore Gateway with Web Search Tool - -## Overview - -This script creates all the infrastructure needed to expose the Web Search Tool through an AgentCore Gateway. After running it, you'll have a fully functional MCP endpoint that any agent framework can connect to. - -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. - -``` -┌──────────────────┐ ┌─────────────────────────────────────┐ -│ setup_gateway │────────▶│ AWS Resources Created: │ -│ │ │ │ -│ 1. IAM Role │ │ • IAM Role (InvokeWebSearch) │ -│ 2. Cognito │ │ • Cognito User Pool + M2M Client │ -│ 3. Gateway │ │ • AgentCore Gateway (MCP) │ -│ 4. Target │ │ • Web Search Connector Target │ -└──────────────────┘ └─────────────────────────────────────┘ -``` - -## How It Works - -### Gateway Service Role - -The Gateway needs an IAM role that allows the AgentCore service to perform actions on your behalf. For the Web Search Tool, the role needs two permissions: - -- `bedrock-agentcore:InvokeGateway` — to invoke the Gateway itself -- `bedrock-agentcore:InvokeWebSearch` — to authorize web search invocations against `arn:aws:bedrock-agentcore::aws:tool/web-search.v1` - -### Cognito Authentication - -The script creates a Cognito User Pool with: -- A resource server defining an `invoke` scope -- A machine-to-machine (M2M) client using the `client_credentials` OAuth flow - -This provides JWT-based authentication for incoming requests to the Gateway. - -### Gateway Creation - -The Gateway is created with: -- **Protocol**: MCP (Model Context Protocol) -- **MCP version**: 2025-03-26 -- **Search type**: SEMANTIC (enables tool discovery across multiple targets) -- **Authorizer**: Custom JWT backed by the Cognito User Pool - -### Web Search Connector Target - -The Web Search Tool uses the `connector` target type — a fully AWS-managed integration. You specify `connectorId: "web-search"` and the Gateway handles schema management, endpoint resolution, and service authentication automatically. - -Outbound authentication uses `GATEWAY_IAM_ROLE` — the Gateway's own service role authenticates to the Web Search backend. No additional credentials needed. - -## Prerequisites - -```bash -pip install -r ../requirements.txt -``` - -Your AWS credentials must have permissions to create IAM roles, Cognito pools, and AgentCore Gateways. Your account must be allowlisted for the Web Search Tool private beta. - -> **Region**: The Web Search Tool connector is currently available in **us-east-1** only. - -## Usage - -```bash -# Default setup -python setup_gateway.py - -# Custom gateway name -python setup_gateway.py --gateway-name my-web-search-gw - -# Explicit region -python setup_gateway.py --region us-east-1 -``` - -After completion, the script writes credentials to a local `.env.web-search` file: - -```bash -source .env.web-search -``` - -This loads the following variables into your shell: - -```bash -AGENTCORE_GATEWAY_URL="https://..." -COGNITO_DOMAIN="..." -COGNITO_CLIENT_ID="..." -COGNITO_CLIENT_SECRET="..." -COGNITO_SCOPE="agentcore-websearch/invoke" -AWS_DEFAULT_REGION="us-east-1" -``` - -Source this file before running the other demos. - -> **⚠️ Security**: The `.env.web-search` file contains your client secret. Keep it secure and do not commit it to version control (it's already in `.gitignore`). - -## IAM Permissions - -### Caller (you) - -```json -{ - "Effect": "Allow", - "Action": [ - "iam:CreateRole", - "iam:PutRolePolicy", - "iam:GetRole", - "cognito-idp:CreateUserPool", - "cognito-idp:CreateUserPoolDomain", - "cognito-idp:CreateResourceServer", - "cognito-idp:CreateUserPoolClient", - "cognito-idp:ListUserPools", - "cognito-idp:ListUserPoolClients", - "cognito-idp:DescribeUserPoolClient", - "cognito-idp:DescribeResourceServer", - "bedrock-agentcore:CreateGateway", - "bedrock-agentcore:GetGateway", - "bedrock-agentcore:CreateGatewayTarget", - "bedrock-agentcore:ListGatewayTargets" - ], - "Resource": "*" -} -``` - -### Gateway Service Role (created by this script) - -```json -{ - "Effect": "Allow", - "Action": [ - "bedrock-agentcore:InvokeGateway", - "bedrock-agentcore:InvokeWebSearch" - ], - "Resource": [ - "arn:aws:bedrock-agentcore:us-east-1::gateway/*", - "arn:aws:bedrock-agentcore:us-east-1:aws:tool/web-search.v1" - ] -} -``` - -## Files - -| File | Description | -|:-----|:------------| -| `setup_gateway.py` | Main setup script — creates all resources | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md deleted file mode 100644 index a75306d3e..000000000 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-raw-mcp/README.md +++ /dev/null @@ -1,84 +0,0 @@ -# Raw MCP Tool Discovery and Invocation - -## Overview - -This demo calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target infrastructure are working correctly. - -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. - -![Raw MCP Client web search Architecture](images/raw-mcp-call-architecture.png) - -## How It Works - -### Tool Discovery (`tools/list`) - -The MCP `tools/list` call returns all tools available on the Gateway. For a Gateway with only the Web Search connector target, you'll see one tool: - -```json -{ - "name": "WebSearch", - "description": "Search the web for current information", - "inputSchema": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "Search query (max 200 characters)" - } - }, - "required": ["query"] - } -} -``` - -### Tool Invocation (`tools/call`) - -Calling the tool with a query returns structured results: - -```json -{ - "results": [ - { - "text": "Snippet from the web page...", - "url": "https://example.com/article", - "title": "Article Title", - "publishedDate": "2026-05-28" - } - ] -} -``` - -### Authentication Flow - -1. The script obtains an OAuth token from Cognito (`client_credentials` flow) -2. The token is passed as a `Bearer` header on the MCP Streamable HTTP transport -3. The Gateway validates the JWT and authorizes the request - -## Prerequisites - -```bash -pip install -r ../requirements.txt -``` - -Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` - -## Usage - -```bash -# Default query -python raw_mcp_call.py - -# Custom query -python raw_mcp_call.py --query "Latest Python release" -python raw_mcp_call.py --query "AWS re:Invent 2026 announcements" -``` - -## IAM Permissions - -No direct IAM permissions needed — authentication is handled via the Cognito OAuth token. The Gateway's service role provides the `bedrock-agentcore:InvokeWebSearch` permission. - -## Files - -| File | Description | -|:-----|:------------| -| `raw_mcp_call.py` | Main demo script — tool discovery and invocation | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md new file mode 100644 index 000000000..f58b45dea --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md @@ -0,0 +1,117 @@ +# Web Search with a Strands AI Agent + +## Overview + +This example shows the complete agent integration: a Strands agent automatically discovers and invokes the Web Search Tool to answer real-time questions with cited sources. + +![Strands Agent web search Architecture](images/strands-web-search-architecture.png) + +## Prerequisites + +- Python 3.10+ +- AWS account with Amazon Bedrock enabled in **us-east-1** +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- Claude Sonnet 4 model access enabled in Bedrock + +```bash +pip install -r ../requirements.txt +``` + +## Quick Start + +```bash +# 1. Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) +python setup_gateway.py + +# 2. Load credentials into your shell +source .env.web-search + +# 3. (Optional) Override the default model +export BEDROCK_MODEL_ID="us.anthropic.claude-sonnet-4-20250514-v1:0" + +# 4. Run the agent +python web_search_strands.py + +# Try a custom query +python web_search_strands.py --query "What are the latest AI announcements?" +python web_search_strands.py --query "Current price of Bitcoin" +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--query` | No | Research question (default: built-in demo queries) | + +## How It Works + +The script connects to the Gateway, creates an agent, and runs a research query: + +### Step 1: Connect to the Gateway + +The agent connects via MCP Streamable HTTP. Authentication is handled internally — an OAuth token is obtained from Cognito and attached to the transport. The Gateway returns the `WebSearch` tool with its input schema, and Strands registers it as an available tool for the LLM. + +```python +from utils.web_search_agent import create_agent, create_mcp_client + +mcp_client = create_mcp_client() +``` + +### Step 2: Create the Agent + +The `create_agent()` function discovers tools via `tools/list` and configures a Strands agent with a system prompt that instructs it to search and cite sources: + +```python +with mcp_client: + agent = create_agent(mcp_client) +``` + +### Step 3: Run the Agent Loop + +When you ask a question, the agent loop executes: + +1. Strands sends the query + tool schema to Claude Sonnet 4 +2. Claude decides to call `WebSearch` with a concise search query +3. Strands invokes the tool via MCP `tools/call` on the Gateway +4. The Gateway routes to the Web Search connector and returns results +5. Results are fed back to Claude as a tool result +6. Claude synthesizes a final answer with cited sources + +```python +result = agent("What is the latest version of Python?") +print(result.message) +``` + +## Files + +| File | Description | +|:-----|:------------| +| `setup_gateway.py` | Creates Gateway + Web Search target infrastructure | +| `web_search_strands.py` | Main demo script — Strands agent with web search | +| `cleanup.py` | Deletes all provisioned AWS resources | +| `../utils/web_search_agent.py` | Shared agent factory with MCP client setup | +| `../utils/gateway_auth.py` | OAuth token retrieval and transport creation | + +## Cleanup (Optional) + +When you're done remove all provisioned AWS resources: + +**1. Retrieve resource IDs** from the setup output (printed when you ran `setup_gateway.py`): + +``` +Gateway ID: +IAM Role: agentcore-web-search-gateway-role +Cognito Pool: +``` + +> **Tip:** If you no longer have the terminal output, the gateway ID is the subdomain prefix in your `AGENTCORE_GATEWAY_URL` (e.g., `gw-abc123` from `https://gw-abc123.gateway.bedrock-agentcore...`). The IAM role follows the pattern `agentcore--role`. + +**2. Run cleanup:** + +```bash +python cleanup.py --gateway-id --user-pool-id --role-name +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--gateway-id` | Yes | Gateway ID | +| `--user-pool-id` | Yes | Cognito User Pool ID | +| `--role-name` | Yes | IAM role name | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py new file mode 100644 index 000000000..8d42e1b8b --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py @@ -0,0 +1,129 @@ +""" +Clean up all resources created by the Web Search Tool setup. + +Deletes in order: + 1. Gateway targets and Gateway + 2. Cognito User Pool (domain, clients, pool) + 3. IAM Role and inline policies + +Prerequisites: + pip install -r ../requirements.txt + AWS credentials with permissions to delete the resources. + +Usage: + python cleanup.py --gateway-id --user-pool-id --role-name + python cleanup.py --gateway-id gw-abc123 --user-pool-id us-east-1_AbCdEf --role-name agentcore-web-search-gateway-role +""" + +import argparse +import os +import time + +import boto3 + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") + + +def delete_gateway(gateway_client, gateway_id): + """Delete all targets and the gateway itself.""" + print("\n[1/3] Deleting Gateway resources...") + try: + targets = gateway_client.list_gateway_targets( + gatewayIdentifier=gateway_id, maxResults=100 + ) + for item in targets["items"]: + gateway_client.delete_gateway_target( + gatewayIdentifier=gateway_id, targetId=item["targetId"] + ) + print(f" Deleted target: {item['name']}") + + time.sleep(10) + gateway_client.delete_gateway(gatewayIdentifier=gateway_id) + print(f" Deleted gateway: {gateway_id}") + except Exception as e: + print(f" Error deleting gateway: {e}") + + +def delete_cognito(cognito_client, user_pool_id): + """Delete the Cognito User Pool and its domain.""" + print("\n[2/3] Deleting Cognito resources...") + try: + domain = user_pool_id.replace("_", "").lower() + cognito_client.delete_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + cognito_client.delete_user_pool(UserPoolId=user_pool_id) + print(f" Deleted user pool: {user_pool_id}") + except Exception as e: + print(f" Error deleting Cognito: {e}") + + +def delete_iam_role(iam_client, role_name): + """Delete the IAM role and its inline policies.""" + print("\n[3/3] Deleting IAM resources...") + try: + policies = iam_client.list_role_policies(RoleName=role_name) + for policy_name in policies["PolicyNames"]: + iam_client.delete_role_policy( + RoleName=role_name, PolicyName=policy_name + ) + print(f" Deleted policy: {policy_name}") + + iam_client.delete_role(RoleName=role_name) + print(f" Deleted role: {role_name}") + except Exception as e: + print(f" Error deleting IAM role: {e}") + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Clean up Web Search Tool Gateway resources" + ) + parser.add_argument( + "--gateway-id", required=True, help="Gateway ID to delete" + ) + parser.add_argument( + "--user-pool-id", required=True, help="Cognito User Pool ID to delete" + ) + parser.add_argument( + "--role-name", required=True, help="IAM role name to delete" + ) + parser.add_argument( + "--region", default=REGION, help="AWS region (default: us-east-1)" + ) + return parser.parse_args() + + +def main(): + args = parse_args() + region = args.region + + print("=" * 60) + print("AgentCore Web Search Tool — Resource Cleanup") + print("=" * 60) + print(f"\nRegion: {region}") + print(f"Gateway ID: {args.gateway_id}") + print(f"User Pool ID: {args.user_pool_id}") + print(f"Role Name: {args.role_name}") + + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + cognito_client = boto3.client("cognito-idp", region_name=region) + iam_client = boto3.client("iam") + + delete_gateway(gateway_client, args.gateway_id) + delete_cognito(cognito_client, args.user_pool_id) + delete_iam_role(iam_client, args.role_name) + + # Remove local credentials file + env_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env.web-search") + if os.path.exists(env_file): + os.remove(env_file) + print(f"\n Deleted local credentials file: {env_file}") + + print("\n" + "=" * 60) + print("✅ All resources cleaned up successfully!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/images/strands-web-search-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/images/strands-web-search-architecture.png similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/images/strands-web-search-architecture.png rename to 01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/images/strands-web-search-architecture.png diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py new file mode 100644 index 000000000..b10080a6c --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py @@ -0,0 +1,17 @@ +"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. +After running this script, load credentials with `source .env.web-search` to use +with the other web search examples. + +Usage: + python setup_gateway.py + python setup_gateway.py --gateway-name my-gateway + python setup_gateway.py --region us-east-1 +""" +import sys +import os + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.gateway_setup import main + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/web_search_strands.py rename to 01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md new file mode 100644 index 000000000..5e0b89113 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md @@ -0,0 +1,122 @@ +# Web Search with a LangChain Agent + +## Overview + +This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. + +![Langchain web search Architecture](images/langchain-web-search-architecture.png) + +## Prerequisites + +- Python 3.10+ +- AWS account with Amazon Bedrock enabled in **us-east-1** +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- Claude Sonnet 4 model access enabled in Bedrock + +```bash +pip install -r ../requirements.txt +``` + +## Quick Start + +```bash +# 1. Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) +python setup_gateway.py +#python setup_gateway.py --gateway-name my-web-search-gw + +# 2. Load credentials into your shell +source .env.web-search + +# 3. (Optional) Override the default model +export BEDROCK_MODEL_ID="us.anthropic.claude-sonnet-4-20250514-v1:0" + +# 4. Run the agent +python web_search_langchain.py + +# Try a custom query +python web_search_langchain.py --query "Latest AWS announcements" +python web_search_langchain.py --query "Python 3.13 new features" +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--query` | No | Search query (default: built-in demo query) | + +## How It Works + +The script authenticates, discovers tools from the Gateway, and runs an agent loop: + +### Step 1: Connect to the Gateway via MCP + +An OAuth token is obtained from Cognito and passed to `MultiServerMCPClient`, which connects to the Gateway and converts MCP tools into LangChain-compatible objects: + +```python +from langchain_mcp_adapters.client import MultiServerMCPClient + +async with MultiServerMCPClient({ + "web-search": { + "transport": "streamable_http", + "url": gateway_url, + "headers": {"Authorization": f"Bearer {token}"}, + } +}) as client: + tools = client.get_tools() +``` + +### Step 2: Create the Agent + +The agent uses LangGraph's `create_react_agent` with `ChatBedrockConverse` as the LLM: + +```python +from langchain_aws import ChatBedrockConverse +from langgraph.prebuilt import create_react_agent + +model = ChatBedrockConverse( + model="us.anthropic.claude-sonnet-4-6", + region_name="us-east-1", +) +agent = create_react_agent(model, tools=tools) +``` + +### Step 3: Run the Agent Loop + +LangChain's MCP adapter uses async I/O. The agent invokes `WebSearch` automatically when it determines a search is needed, then synthesizes a cited response: + +```python +result = await agent.ainvoke({"messages": [{"role": "user", "content": query}]}) +``` + +## Files + +| File | Description | +|:-----|:------------| +| `setup_gateway.py` | Creates Gateway + Web Search target infrastructure | +| `web_search_langchain.py` | Main demo script — LangChain agent with web search | +| `cleanup.py` | Deletes all provisioned AWS resources | +| `../utils/gateway_auth.py` | OAuth token retrieval (shared with other demos) | + +## Cleanup (Optional) + +When you're done remove all provisioned AWS resources: + +**1. Retrieve resource IDs** from the setup output (printed when you ran `setup_gateway.py`): + +``` +Gateway ID: +IAM Role: agentcore-web-search-gateway-role +Cognito Pool: +``` + +> **Tip:** If you no longer have the terminal output, the gateway ID is the subdomain prefix in your `AGENTCORE_GATEWAY_URL` (e.g., `gw-abc123` from `https://gw-abc123.gateway.bedrock-agentcore...`). The IAM role follows the pattern `agentcore--role`. + +**2. Run cleanup:** + +```bash +python cleanup.py --gateway-id --user-pool-id --role-name +``` + +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--gateway-id` | Yes | Gateway ID | +| `--user-pool-id` | Yes | Cognito User Pool ID | +| `--role-name` | Yes | IAM role name | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py new file mode 100644 index 000000000..8d42e1b8b --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py @@ -0,0 +1,129 @@ +""" +Clean up all resources created by the Web Search Tool setup. + +Deletes in order: + 1. Gateway targets and Gateway + 2. Cognito User Pool (domain, clients, pool) + 3. IAM Role and inline policies + +Prerequisites: + pip install -r ../requirements.txt + AWS credentials with permissions to delete the resources. + +Usage: + python cleanup.py --gateway-id --user-pool-id --role-name + python cleanup.py --gateway-id gw-abc123 --user-pool-id us-east-1_AbCdEf --role-name agentcore-web-search-gateway-role +""" + +import argparse +import os +import time + +import boto3 + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") + + +def delete_gateway(gateway_client, gateway_id): + """Delete all targets and the gateway itself.""" + print("\n[1/3] Deleting Gateway resources...") + try: + targets = gateway_client.list_gateway_targets( + gatewayIdentifier=gateway_id, maxResults=100 + ) + for item in targets["items"]: + gateway_client.delete_gateway_target( + gatewayIdentifier=gateway_id, targetId=item["targetId"] + ) + print(f" Deleted target: {item['name']}") + + time.sleep(10) + gateway_client.delete_gateway(gatewayIdentifier=gateway_id) + print(f" Deleted gateway: {gateway_id}") + except Exception as e: + print(f" Error deleting gateway: {e}") + + +def delete_cognito(cognito_client, user_pool_id): + """Delete the Cognito User Pool and its domain.""" + print("\n[2/3] Deleting Cognito resources...") + try: + domain = user_pool_id.replace("_", "").lower() + cognito_client.delete_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + cognito_client.delete_user_pool(UserPoolId=user_pool_id) + print(f" Deleted user pool: {user_pool_id}") + except Exception as e: + print(f" Error deleting Cognito: {e}") + + +def delete_iam_role(iam_client, role_name): + """Delete the IAM role and its inline policies.""" + print("\n[3/3] Deleting IAM resources...") + try: + policies = iam_client.list_role_policies(RoleName=role_name) + for policy_name in policies["PolicyNames"]: + iam_client.delete_role_policy( + RoleName=role_name, PolicyName=policy_name + ) + print(f" Deleted policy: {policy_name}") + + iam_client.delete_role(RoleName=role_name) + print(f" Deleted role: {role_name}") + except Exception as e: + print(f" Error deleting IAM role: {e}") + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Clean up Web Search Tool Gateway resources" + ) + parser.add_argument( + "--gateway-id", required=True, help="Gateway ID to delete" + ) + parser.add_argument( + "--user-pool-id", required=True, help="Cognito User Pool ID to delete" + ) + parser.add_argument( + "--role-name", required=True, help="IAM role name to delete" + ) + parser.add_argument( + "--region", default=REGION, help="AWS region (default: us-east-1)" + ) + return parser.parse_args() + + +def main(): + args = parse_args() + region = args.region + + print("=" * 60) + print("AgentCore Web Search Tool — Resource Cleanup") + print("=" * 60) + print(f"\nRegion: {region}") + print(f"Gateway ID: {args.gateway_id}") + print(f"User Pool ID: {args.user_pool_id}") + print(f"Role Name: {args.role_name}") + + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + cognito_client = boto3.client("cognito-idp", region_name=region) + iam_client = boto3.client("iam") + + delete_gateway(gateway_client, args.gateway_id) + delete_cognito(cognito_client, args.user_pool_id) + delete_iam_role(iam_client, args.role_name) + + # Remove local credentials file + env_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env.web-search") + if os.path.exists(env_file): + os.remove(env_file) + print(f"\n Deleted local credentials file: {env_file}") + + print("\n" + "=" * 60) + print("✅ All resources cleaned up successfully!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/images/langchain-web-search-architecture.png b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/images/langchain-web-search-architecture.png similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/images/langchain-web-search-architecture.png rename to 01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/images/langchain-web-search-architecture.png diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py new file mode 100644 index 000000000..e1fe26293 --- /dev/null +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py @@ -0,0 +1,15 @@ +"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. + + Usage: + python setup_gateway.py + python setup_gateway.py --gateway-name my-gateway + python setup_gateway.py --region us-east-1 +""" +import sys +import os + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from utils.gateway_setup import main + +if __name__ == "__main__": + main() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py similarity index 100% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/web_search_langchain.py rename to 01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md deleted file mode 100644 index 822b8f491..000000000 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-strands-agent/README.md +++ /dev/null @@ -1,85 +0,0 @@ -# Web Search with a Strands AI Agent - -## Overview - -This demo shows the complete agent integration: a Strands agent automatically discovers and invokes the Web Search Tool to answer real-time questions with cited sources. - -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. - - -![Strands Agent web search Architecture](images/strands-web-search-architecture.png) - -## How It Works - -### Tool Discovery - -The agent connects to the Gateway via MCP Streamable HTTP and calls `tools/list`. The Gateway returns the `WebSearch` tool with its input schema. Strands automatically registers it as an available tool for the LLM. - -### Agent Loop - -When you ask a question: - -1. Strands sends the query + tool schema to Claude Sonnet 4 -2. Claude(the Strands Agent model) decides to call `WebSearch` with a concise search query -3. Strands invokes the tool via MCP `tools/call` on the Gateway -4. The Gateway routes to the Web Search connector and returns results -5. Results are fed back to Claude as a tool result -6. Claude synthesizes a final answer with cited sources - -### Shared Agent (`utils/web_search_agent.py`) - -The agent factory handles MCP client setup and tool discovery: - -```python -from utils.web_search_agent import create_agent, create_mcp_client - -mcp_client = create_mcp_client() - -with mcp_client: - agent = create_agent(mcp_client) - result = agent("What is the latest version of Python?") - print(result.message) -``` - -The `create_mcp_client()` function handles OAuth token retrieval and transport creation. The `create_agent()` function discovers tools and configures the Strands agent with an appropriate system prompt. - -## Prerequisites - -```bash -pip install -r ../requirements.txt -``` - -Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` - -Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. - -## Usage - -```bash -# Run default demo queries -python web_search_strands.py - -# Custom query -python web_search_strands.py --query "What are the latest AI announcements?" -python web_search_strands.py --query "Current price of Bitcoin" -``` - -## IAM Permissions - -```json -{ - "Effect": "Allow", - "Action": "bedrock:InvokeModel", - "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-6" -} -``` - -Gateway invocation is authorized via the Cognito OAuth token — no additional IAM permissions needed for the caller. - -## Files - -| File | Description | -|:-----|:------------| -| `web_search_strands.py` | Main demo script — Strands agent with web search | -| `../utils/web_search_agent.py` | Shared agent factory with MCP client setup | -| `../utils/gateway_auth.py` | OAuth token retrieval and transport creation | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md deleted file mode 100644 index 024e2d75a..000000000 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/04-langchain-agent/README.md +++ /dev/null @@ -1,87 +0,0 @@ -# Web Search with a LangChain Agent - -## Overview - -This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. - -> 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. - -![Langchain web search Architecture](images/langchain-web-search-architecture.png) - -## How It Works - -### MCP Client Setup - -`langchain-mcp-adapters` provides `MultiServerMCPClient` which connects to one or more MCP servers and converts their tools into LangChain-compatible tool objects: - -```python -from langchain_mcp_adapters.client import MultiServerMCPClient - -async with MultiServerMCPClient({ - "web-search": { - "transport": "streamable_http", - "url": gateway_url, - "headers": {"Authorization": f"Bearer {token}"}, - } -}) as client: - tools = client.get_tools() - # tools is a list of LangChain Tool objects -``` - -### Agent Creation - -The agent uses LangGraph's `create_react_agent` with `ChatBedrockConverse` as the LLM: - -```python -from langchain_aws import ChatBedrockConverse -from langgraph.prebuilt import create_react_agent - -model = ChatBedrockConverse( - model="us.anthropic.claude-sonnet-4-6", - region_name="us-east-1", -) -agent = create_react_agent(model, tools=tools) -result = await agent.ainvoke({"messages": [{"role": "user", "content": query}]}) -``` - -### Async Execution - -LangChain's MCP adapter uses async I/O. The demo wraps the agent call in `asyncio.run()` for CLI usage. - -## Prerequisites - -```bash -pip install -r ../requirements.txt -``` - -Run `01-setup-gateway/setup_gateway.py` first, then load credentials: `source .env.web-search` - -Requires access to Claude Sonnet 4 (`us.anthropic.claude-sonnet-4-6`) in us-east-1. - -## Usage - -```bash -# Default query -python web_search_langchain.py - -# Custom query -python web_search_langchain.py --query "Latest AWS announcements" -python web_search_langchain.py --query "Python 3.13 new features" -``` - -## IAM Permissions - -```json -{ - "Effect": "Allow", - "Action": "bedrock:InvokeModel", - "Resource": "arn:aws:bedrock:us-east-1::foundation-model/us.anthropic.claude-sonnet-4-6" -} -``` - -## Files - -| File | Description | -|:-----|:------------| -| `web_search_langchain.py` | Main demo script — LangChain agent with web search | -| `../utils/gateway_auth.py` | OAuth token retrieval (shared with other demos) | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md deleted file mode 100644 index 5d3ad7ae8..000000000 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Resource Cleanup - -## Overview - -Deletes all AWS resources created by the setup script: Gateway targets, Gateway, Cognito User Pool, and IAM role. - -## Usage - -Use the resource IDs printed by `01-setup-gateway/setup_gateway.py`: - -```bash -python cleanup.py \ - --gateway-id gw-abc123def456 \ - --user-pool-id us-east-1_AbCdEfGh \ - --role-name agentcore-web-search-gateway-role -``` - -## Files - -| File | Description | -|:-----|:------------| -| `cleanup.py` | Deletes Gateway, Cognito, and IAM resources | diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index 0063b311b..82bcb6707 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -1,14 +1,41 @@ # AgentCore Web Search Tool -## Overview +![Web Search Tool Architecture](images/tutorial-architecture.png) -[Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. +[Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-connector-web-search-tool.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. > 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. -![Web Search Tool Architecture](images/tutorial-architecture.png) +## Key Capabilities + +- **Real-time information access** — Retrieve current web results with titles, URLs, snippets, and publication dates +- **Zero infrastructure management** — No search APIs to provision or scaling to configure +- **Framework agnostic** — Works with Strands Agents, LangChain, LangGraph, CrewAI, or any MCP-compatible client +- **Structured results** — Results returned in both MCP `content` (text) and `structuredContent` (typed JSON) formats +- **MCP-native** — Standard `tools/list` and `tools/call` protocol; no custom SDK required + +## Tutorials + +| Section | Description | +|:--------|:------------| +| [01-raw-mcp/](01-raw-mcp/) | Direct MCP tool discovery and invocation without an agent framework | +| [02-strands-agent/](02-strands-agent/) | Full agent loop with Strands Agents — automatic tool selection and cited responses | +| [03-langchain-agent/](03-langchain-agent/) | Full agent loop using LangChain + LangGraph with MCP adapter | + +## Prerequisites + +- AWS account with Amazon Bedrock enabled in **us-east-1** +- Python 3.10+ +- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- Model access enabled for Claude Sonnet 4 (cross-region inference profile) + +```bash +pip install -r requirements.txt +``` -## How the Web Search Tool Works +> **Note:** The Web Search Tool connector is currently available in **us-east-1** only. + +## Core Concepts ### Gateway + Connector Architecture @@ -16,13 +43,6 @@ The Web Search Tool uses the **connector** target type — a fully AWS-managed i ![Web search with Amazon Bedrock AgentCore Gateway](images/agentcore-web-search-architecture.png) -### Key Capabilities - -- **Real-time information access** — Retrieve current web results with titles, URLs, snippets, and publication dates -- **Zero infrastructure management** — No search APIs to provision or scaling to configure -- **Framework agnostic** — Works with Strands Agents, LangChain, LangGraph, CrewAI, or any MCP-compatible client -- **Structured results** — Results returned in both MCP `content` (text) and `structuredContent` (typed JSON) formats - ### How It Works 1. **Gateway setup** — Create an AgentCore Gateway and add a Web Search Tool target using `connectorId: "web-search"` @@ -60,137 +80,32 @@ The Web Search Tool uses the **connector** target type — a fully AWS-managed i - **Inbound**: Amazon Cognito with `client_credentials` OAuth flow (can use other OAuth providers) - **Outbound**: Automatic — the Gateway uses its own IAM role to authenticate to the Web Search backend -## Demos - -| Folder | Framework | What You'll Learn | -|:-------|:----------|:------------------| -| [01-setup-gateway/](01-setup-gateway/) | boto3 (SDK) | Create IAM role, Cognito, Gateway, and Web Search target | -| [02-raw-mcp/](02-raw-mcp/) | MCP protocol | Direct tool discovery and invocation without an agent | -| [03-strands-agent/](03-strands-agent/) | Strands Agents | Full agent loop with automatic tool selection and cited responses | -| [04-langchain-agent/](04-langchain-agent/) | LangChain + LangGraph | Full agent loop integration using LangChain's MCP adapter | -| [05-cleanup/](05-cleanup/) | boto3 (SDK) | Delete all resources created by the setup | - -## Prerequisites - -Before running the agent demos (steps 3–4 in Quick Start), your AWS account must have model access enabled for the Bedrock model you intend to use. - -**Default model**: `us.anthropic.claude-sonnet-4-5-20250514-v1:0` (cross-region inference profile) - -To use a different model, export `BEDROCK_MODEL_ID` before running any agent demo: - -```bash -export BEDROCK_MODEL_ID="us.anthropic.claude-3-5-sonnet-20241022-v2:0" -``` - -> **Note:** On-demand model IDs (e.g. `anthropic.claude-sonnet-4-5-20250929-v1:0`) are not supported directly — you must use a cross-region inference profile ID (prefixed with `us.`, `eu.`, or `ap.`) or an inference profile ARN. See [Supported models and Regions for cross-Region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) for the full list. - -To enable model access in your account: -1. Open the [Amazon Bedrock console](https://console.aws.amazon.com/bedrock/) -2. Go to **Model access** in the left navigation -3. Request access for the model you want to use - -## Quick Start +## End-to-End Example ```bash pip install -r requirements.txt -# Step 1: Create Gateway and Web Search target -python 01-setup-gateway/setup_gateway.py +# Create Gateway and Web Search target (run from any sample folder) +python 01-raw-mcp/setup_gateway.py -# Step 2: Load the credentials written by setup +# Load credentials source .env.web-search -# Optional: override the default Bedrock model -export BEDROCK_MODEL_ID="us.anthropic.claude-3-5-sonnet-20241022-v2:0" - -# Step 3: Verify with raw MCP calls -python 02-raw-mcp/raw_mcp_call.py - -# Step 4: Run with Strands agent -python 03-strands-agent/web_search_strands.py - -# Step 5: Run with LangChain agent -python 04-langchain-agent/web_search_langchain.py - -# Cleanup when done -python 05-cleanup/cleanup.py --gateway-id --user-pool-id --role-name -``` - -## Shared Utilities - -Demos 02–04 share utilities in `utils/`: - -```python -from utils.gateway_auth import get_oauth_token, create_streamable_http_transport -from utils.web_search_agent import create_agent, create_mcp_client -``` - -- `gateway_auth.py` — OAuth token retrieval from Cognito and MCP transport factory -- `web_search_agent.py` — Strands agent factory with Web Search tools +# Verify with raw MCP calls +python 01-raw-mcp/raw_mcp_call.py -## IAM Permissions +# Run with Strands agent +python 02-strands-agent/web_search_strands.py -### Caller (setup script) +# Run with LangChain agent +python 03-langchain-agent/web_search_langchain.py -```json -{ - "Effect": "Allow", - "Action": [ - "iam:CreateRole", - "iam:PutRolePolicy", - "iam:GetRole", - "cognito-idp:CreateUserPool", - "cognito-idp:CreateUserPoolDomain", - "cognito-idp:CreateResourceServer", - "cognito-idp:CreateUserPoolClient", - "bedrock-agentcore:CreateGateway", - "bedrock-agentcore:GetGateway", - "bedrock-agentcore:CreateGatewayTarget", - "bedrock-agentcore:ListGatewayTargets" - ], - "Resource": "*" -} +# Cleanup when done (run from any sample folder) +python 01-raw-mcp/cleanup.py --gateway-id --user-pool-id --role-name ``` -### Caller (agent demos) - -```json -{ - "Effect": "Allow", - "Action": "bedrock:InvokeModel", - "Resource": "*" -} -``` - -### Gateway Service Role (created by setup) - -```json -{ - "Effect": "Allow", - "Action": [ - "bedrock-agentcore:InvokeGateway", - "bedrock-agentcore:InvokeWebSearch" - ], - "Resource": [ - "arn:aws:bedrock-agentcore:us-east-1::gateway/*", - "arn:aws:bedrock-agentcore:us-east-1:aws:tool/web-search.v1" - ] -} -``` - -## Region Availability - -The Web Search Tool connector is currently available in **us-east-1** only. - -## Files +## Documentation -| File | Description | -|:-----|:------------| -| `requirements.txt` | Python dependencies for all sub-demos | -| `utils/gateway_auth.py` | Shared OAuth and MCP transport utilities | -| `utils/web_search_agent.py` | Shared Strands agent factory | -| `01-setup-gateway/` | Gateway and target creation script | -| `02-raw-mcp/` | Direct MCP protocol demo | -| `03-strands-agent/` | Strands agent demo | -| `04-langchain-agent/` | LangChain agent demo | -| `05-cleanup/` | Resource deletion script | +- [Web Search Tool connector](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-connector-web-search-tool.html) +- [AgentCore Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) +- [Supported models for cross-region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt index f7769b56a..ee73c7891 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt @@ -1,4 +1,5 @@ -boto3 +boto3>=1.38.0 +botocore>=1.38.0 strands-agents>=0.1.8 mcp requests diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py similarity index 98% rename from 01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py rename to 01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py index dc287a505..7b4798978 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py @@ -336,8 +336,10 @@ def main(): print("SETUP COMPLETE") print("=" * 60) - # Write credentials to a local .env file (avoids logging secrets to stdout) - env_file = ".env.web-search" + # Write credentials to a local .env file next to the script that invoked setup + caller_dir = os.path.dirname(os.path.abspath(sys.argv[0])) if sys.argv[0] else os.getcwd() + env_file = os.path.join(caller_dir, ".env.web-search") + with open(env_file, "w") as f: f.write(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"\n") f.write(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"\n") diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/README.md b/02-use-cases/01-conversational-agents/deep-research-agent/README.md index 5521c6df7..cd26843e0 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/README.md +++ b/02-use-cases/01-conversational-agents/deep-research-agent/README.md @@ -37,7 +37,7 @@ Single-shot web search works for simple factual queries. For questions that requ - "What are the current best practices for deploying LLM agents in production, based on recent industry reports?" - "What are the trade-offs between RAG and fine-tuning for enterprise LLM applications?" -## Architecture +## Use Case Architecture ![Deep Research Agent with Amazon Bedrock AgentCore](images/deep-research-agent-architecture.png) @@ -151,16 +151,25 @@ When deployed, invoke via the AgentCore Runtime API: ## Cleanup -When you're done, remove all provisioned resources using the shared cleanup script: +When you're done, remove all provisioned resources: ```bash -python ../../../01-features/03-connect-your-agent-to-anything/03-web-search/05-cleanup/cleanup.py \ - --gateway-id \ - --user-pool-id \ - --role-name +python cleanup.py --gateway-id --user-pool-id --role-name ``` -The gateway ID, role name, and user pool ID are printed during provisioning. After cleanup, unset environment variables: +| Parameter | Required | Description | +|:----------|:---------|:------------| +| `--gateway-id` | Yes | Gateway ID (printed during provisioning) | +| `--user-pool-id` | Yes | Cognito User Pool ID (printed during provisioning) | +| `--role-name` | Yes | IAM role name (printed during provisioning) | + +The cleanup script will: +- Delete the Gateway and all its targets +- Delete the Cognito User Pool and domain +- Delete the IAM service role and inline policies +- Remove the local `.env.web-search` credentials file + +After cleanup, unset environment variables: ```bash unset AGENTCORE_GATEWAY_URL COGNITO_DOMAIN COGNITO_CLIENT_ID COGNITO_CLIENT_SECRET COGNITO_SCOPE @@ -192,6 +201,7 @@ See the Prerequisites section above for the full list of permissions needed to c |:-----|:------------| | `deep_research_agent.py` | Main agent — Plan/Search/Reflect loop, AgentCore Runtime entrypoint, and CLI | | `gateway_setup.py` | Auto-detection and provisioning of Gateway + Web Search infrastructure | +| `cleanup.py` | Deletes all provisioned AWS resources and local credentials | | `requirements.txt` | Python dependencies | | `README.md` | This file | diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py b/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py new file mode 100644 index 000000000..1f99865ed --- /dev/null +++ b/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py @@ -0,0 +1,135 @@ +""" +Clean up all resources provisioned by the Deep Research Agent. + +Deletes in order: + 1. Gateway targets and Gateway + 2. Cognito User Pool (domain, clients, pool) + 3. IAM Role and inline policies + 4. Local .env.web-search credentials file + +Prerequisites: + pip install -r requirements.txt + AWS credentials with permissions to delete the resources. + +Usage: + python cleanup.py --gateway-id --user-pool-id --role-name + python cleanup.py --gateway-id gw-abc123 --user-pool-id us-east-1_AbCdEf --role-name agentcore-web-search-gateway-role +""" + +import argparse +import os +import time + +import boto3 + +REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") + + +def delete_gateway(gateway_client, gateway_id): + """Delete all targets and the gateway itself.""" + print("\n[1/3] Deleting Gateway resources...") + try: + targets = gateway_client.list_gateway_targets( + gatewayIdentifier=gateway_id, maxResults=100 + ) + for item in targets["items"]: + gateway_client.delete_gateway_target( + gatewayIdentifier=gateway_id, targetId=item["targetId"] + ) + print(f" Deleted target: {item['name']}") + + time.sleep(10) + gateway_client.delete_gateway(gatewayIdentifier=gateway_id) + print(f" Deleted gateway: {gateway_id}") + except Exception as e: + print(f" Error deleting gateway: {e}") + + +def delete_cognito(cognito_client, user_pool_id): + """Delete the Cognito User Pool and its domain.""" + print("\n[2/3] Deleting Cognito resources...") + try: + domain = user_pool_id.replace("_", "").lower() + cognito_client.delete_user_pool_domain( + Domain=domain, UserPoolId=user_pool_id + ) + cognito_client.delete_user_pool(UserPoolId=user_pool_id) + print(f" Deleted user pool: {user_pool_id}") + except Exception as e: + print(f" Error deleting Cognito: {e}") + + +def delete_iam_role(iam_client, role_name): + """Delete the IAM role and its inline policies.""" + print("\n[3/3] Deleting IAM resources...") + try: + policies = iam_client.list_role_policies(RoleName=role_name) + for policy_name in policies["PolicyNames"]: + iam_client.delete_role_policy( + RoleName=role_name, PolicyName=policy_name + ) + print(f" Deleted policy: {policy_name}") + + iam_client.delete_role(RoleName=role_name) + print(f" Deleted role: {role_name}") + except Exception as e: + print(f" Error deleting IAM role: {e}") + + +def delete_env_file(): + """Remove local .env.web-search credentials file.""" + env_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env.web-search") + if os.path.exists(env_file): + os.remove(env_file) + print(f"\n Deleted local credentials file: {env_file}") + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Clean up Deep Research Agent Gateway resources" + ) + parser.add_argument( + "--gateway-id", required=True, help="Gateway ID to delete" + ) + parser.add_argument( + "--user-pool-id", required=True, help="Cognito User Pool ID to delete" + ) + parser.add_argument( + "--role-name", required=True, help="IAM role name to delete" + ) + parser.add_argument( + "--region", default=REGION, help="AWS region (default: us-east-1)" + ) + return parser.parse_args() + + +def main(): + args = parse_args() + region = args.region + + print("=" * 60) + print("Deep Research Agent — Resource Cleanup") + print("=" * 60) + print(f"\nRegion: {region}") + print(f"Gateway ID: {args.gateway_id}") + print(f"User Pool ID: {args.user_pool_id}") + print(f"Role Name: {args.role_name}") + + gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) + cognito_client = boto3.client("cognito-idp", region_name=region) + iam_client = boto3.client("iam") + + delete_gateway(gateway_client, args.gateway_id) + delete_cognito(cognito_client, args.user_pool_id) + delete_iam_role(iam_client, args.role_name) + delete_env_file() + + print("\n" + "=" * 60) + print("✅ All resources cleaned up successfully!") + print("=" * 60) + print("\n💡 Remember to unset environment variables:") + print(" unset AGENTCORE_GATEWAY_URL COGNITO_DOMAIN COGNITO_CLIENT_ID COGNITO_CLIENT_SECRET COGNITO_SCOPE") + + +if __name__ == "__main__": + main() diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt b/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt index 449dc1750..46b6c671a 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt +++ b/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt @@ -1,6 +1,6 @@ bedrock-agentcore>=0.2.0 strands-agents>=0.1.8 -boto3>=1.34.0 -botocore>=1.34.0 +boto3>=1.38.0 +botocore>=1.38.0 mcp -requests +requests \ No newline at end of file From 23cba1243ac23277f2f291aafa74c8e4f6daa3e1 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Thu, 18 Jun 2026 12:41:42 -0400 Subject: [PATCH 14/21] Testing post NY Summit boto3 sdk release --- .../03-web-search/01-raw-mcp/README.md | 1 + .../03-web-search/01-raw-mcp/raw_mcp_call.py | 2 +- .../03-web-search/02-strands-agent/README.md | 3 ++- .../03-web-search/03-langchain-agent/README.md | 4 ++-- .../03-langchain-agent/web_search_langchain.py | 5 +++-- .../03-web-search/requirements.txt | 4 ++-- .../03-web-search/utils/gateway_setup.py | 6 ++++++ .../01-conversational-agents/deep-research-agent/README.md | 2 +- .../deep-research-agent/requirements.txt | 4 ++-- 9 files changed, 20 insertions(+), 11 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md index ac591c89b..ac240e675 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md @@ -77,6 +77,7 @@ Calling the tool with a query returns structured results: ```bash # Install dependencies pip install -r ../requirements.txt +#python3 -m pip install --upgrade --force-reinstall boto3 # Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) python setup_gateway.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py index 3d66b2479..8defc824e 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py @@ -41,7 +41,7 @@ # ── Configuration ───────────────────────────────────────────────────────────── -DEFAULT_QUERY = "what is tesla stock price right now?" +DEFAULT_QUERY = "Tesla stock price right now?" # ── Main ────────────────────────────────────────────────────────────────────── diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md index f58b45dea..23547339a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md @@ -21,7 +21,8 @@ pip install -r ../requirements.txt ```bash # 1. Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) -python setup_gateway.py +#python setup_gateway.py +python setup_gateway.py --gateway-name strands-web-search-gw # 2. Load credentials into your shell source .env.web-search diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md index 5e0b89113..b7c6ad2a1 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md @@ -21,8 +21,8 @@ pip install -r ../requirements.txt ```bash # 1. Set up the Gateway (creates IAM role, Cognito, Gateway, Web Search target) -python setup_gateway.py -#python setup_gateway.py --gateway-name my-web-search-gw +#python setup_gateway.py +python setup_gateway.py --gateway-name langchain-web-search-gw # 2. Load credentials into your shell source .env.web-search diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py index 096d84619..491e6ae97 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py @@ -41,7 +41,7 @@ from langchain_aws import ChatBedrockConverse from langchain_mcp_adapters.client import MultiServerMCPClient -from langgraph.prebuilt import create_react_agent # noqa: E402 — stable in langgraph>=1.0 +from langchain.agents import create_agent # ── Configuration ───────────────────────────────────────────────────────────── @@ -84,7 +84,8 @@ async def run_agent(query: str): print(f" Discovered {len(tools)} tool(s)") # Create and run the agent - agent = create_react_agent(model, tools=tools) + #agent = create_react_agent(model, tools=tools) + agent = create_agent(model, tools=tools) result = await agent.ainvoke( {"messages": [{"role": "user", "content": query}]} ) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt index ee73c7891..d8c552bb4 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/requirements.txt @@ -1,5 +1,5 @@ -boto3>=1.38.0 -botocore>=1.38.0 +boto3 +botocore strands-agents>=0.1.8 mcp requests diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py index 7b4798978..a25667e4f 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py @@ -33,8 +33,10 @@ import argparse import json import os +import sys import time + import boto3 # ── Configuration ───────────────────────────────────────────────────────────── @@ -347,6 +349,10 @@ def main(): f.write(f"export COGNITO_CLIENT_SECRET=\"{cognito_config['client_secret']}\"\n") # noqa: E501 f.write(f"export COGNITO_SCOPE=\"{cognito_config['scope']}\"\n") f.write(f"export AWS_DEFAULT_REGION=\"{region}\"\n") + f.write(f'export GATEWAY_ID="{gateway_id}"\n') + f.write(f'export USER_POOL_ID="{cognito_config["user_pool_id"]}"\n') + f.write(f'export ROLE_NAME="{role_name}"\n') + f.write(f'# Cleanup resource IDs\n') print(f"\n✅ Credentials written to: {env_file}") print(" Load them with: source .env.web-search\n") diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/README.md b/02-use-cases/01-conversational-agents/deep-research-agent/README.md index cd26843e0..d016196dd 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/README.md +++ b/02-use-cases/01-conversational-agents/deep-research-agent/README.md @@ -130,7 +130,7 @@ export COGNITO_SCOPE="agentcore-websearch/invoke" export AWS_DEFAULT_REGION="us-east-1" ``` -### 4. Deploy to AgentCore Runtime +### 4. (Optional) Deploy to AgentCore Runtime For runtime deployments, environment variables **must** be pre-configured in the container environment (no interactive provisioning in runtime mode): diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt b/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt index 46b6c671a..bf6ee953e 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt +++ b/02-use-cases/01-conversational-agents/deep-research-agent/requirements.txt @@ -1,6 +1,6 @@ bedrock-agentcore>=0.2.0 strands-agents>=0.1.8 -boto3>=1.38.0 -botocore>=1.38.0 +boto3 +botocore mcp requests \ No newline at end of file From f7bb89982f5db9004a2ed11d7450ee1649c140e8 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 17:48:34 -0400 Subject: [PATCH 15/21] Changes to follow naming convention 'AgentCore gateway ' AgentCore web search + Updated websearch tutorialarchitecture + web search supported regions and note --- .../03-web-search/01-raw-mcp/README.md | 4 ++-- .../03-web-search/01-raw-mcp/cleanup.py | 2 +- .../03-web-search/01-raw-mcp/raw_mcp_call.py | 6 +++--- .../03-web-search/01-raw-mcp/setup_gateway.py | 2 +- .../03-web-search/02-strands-agent/README.md | 2 +- .../03-web-search/02-strands-agent/cleanup.py | 2 +- .../02-strands-agent/setup_gateway.py | 2 +- .../02-strands-agent/web_search_strands.py | 4 ++-- .../03-langchain-agent/README.md | 4 ++-- .../03-langchain-agent/cleanup.py | 2 +- .../03-langchain-agent/setup_gateway.py | 2 +- .../web_search_langchain.py | 4 ++-- .../03-web-search/README.md | 14 +++++++------- .../images/tutorial-architecture.png | Bin 31128 -> 50098 bytes .../03-web-search/utils/gateway_auth.py | 2 +- .../03-web-search/utils/gateway_setup.py | 16 ++++++++-------- .../03-web-search/utils/web_search_agent.py | 6 +++--- .../README.md | 10 +++++----- 01-features/README.md | 3 ++- .../deep-research-agent/README.md | 16 ++++++++-------- .../deep-research-agent/gateway_setup.py | 2 +- README.md | 4 ++-- 22 files changed, 55 insertions(+), 54 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md index ac240e675..c67b60124 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/README.md @@ -2,7 +2,7 @@ ## Overview -This example calls the AgentCore Gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target are working correctly. +This example calls the AgentCore gateway directly over the MCP protocol — no agent framework involved. It's the simplest way to verify your Gateway and Web Search Tool target are working correctly. > 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. @@ -12,7 +12,7 @@ This example calls the AgentCore Gateway directly over the MCP protocol — no a - Python 3.10+ - AWS account with Amazon Bedrock enabled in **us-east-1** -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- AWS credentials with IAM, Cognito, and AgentCore gateway permissions ## How It Works diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py index 8d42e1b8b..98157b22e 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py @@ -99,7 +99,7 @@ def main(): region = args.region print("=" * 60) - print("AgentCore Web Search Tool — Resource Cleanup") + print("AgentCore web search tool — Resource Cleanup") print("=" * 60) print(f"\nRegion: {region}") print(f"Gateway ID: {args.gateway_id}") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py index 8defc824e..4d86c18a5 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py @@ -1,5 +1,5 @@ """ -Raw MCP Tool Discovery and Invocation against AgentCore Gateway. +Raw MCP Tool Discovery and Invocation against AgentCore gateway. Demonstrates direct MCP protocol calls without an agent framework: 1. Connect to the Gateway using MCP Streamable HTTP transport @@ -49,7 +49,7 @@ def parse_args(): parser = argparse.ArgumentParser( - description="Raw MCP tool discovery and invocation against AgentCore Gateway" + description="Raw MCP tool discovery and invocation against AgentCore gateway" ) parser.add_argument( "--query", @@ -63,7 +63,7 @@ def main(): args = parse_args() print("=" * 60) - print("AgentCore Web Search Tool — Raw MCP Calls") + print("AgentCore web search tool — Raw MCP Calls") print("=" * 60) # Create MCP client diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py index b10080a6c..30c6a7121 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py @@ -1,4 +1,4 @@ -"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. +"""Set up AgentCore gateway with Web Search Tool — delegates to shared utility. After running this script, load credentials with `source .env.web-search` to use with the other web search examples. diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md index 23547339a..f8a3e63ea 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md @@ -10,7 +10,7 @@ This example shows the complete agent integration: a Strands agent automatically - Python 3.10+ - AWS account with Amazon Bedrock enabled in **us-east-1** -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- AWS credentials with IAM, Cognito, and AgentCore gateway permissions - Claude Sonnet 4 model access enabled in Bedrock ```bash diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py index 8d42e1b8b..98157b22e 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py @@ -99,7 +99,7 @@ def main(): region = args.region print("=" * 60) - print("AgentCore Web Search Tool — Resource Cleanup") + print("AgentCore web search tool — Resource Cleanup") print("=" * 60) print(f"\nRegion: {region}") print(f"Gateway ID: {args.gateway_id}") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py index b10080a6c..30c6a7121 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py @@ -1,4 +1,4 @@ -"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. +"""Set up AgentCore gateway with Web Search Tool — delegates to shared utility. After running this script, load credentials with `source .env.web-search` to use with the other web search examples. diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py index 87d6bddc0..b58416fc1 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py @@ -68,7 +68,7 @@ def run_query(agent, query: str): def parse_args(): parser = argparse.ArgumentParser( - description="Strands agent with Web Search Tool via AgentCore Gateway" + description="Strands agent with Web Search Tool via AgentCore gateway" ) parser.add_argument( "--query", @@ -82,7 +82,7 @@ def main(): args = parse_args() print("=" * 60) - print("AgentCore Web Search Tool — Strands Agent") + print("AgentCore web search tool — Strands Agent") print("=" * 60) mcp_client = create_mcp_client() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md index b7c6ad2a1..5a85c0dfb 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/README.md @@ -2,7 +2,7 @@ ## Overview -This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore Gateway and `create_react_agent` from LangGraph for the agent loop. +This demo shows the same Web Search Tool integration using LangChain and LangGraph instead of Strands. It uses `langchain-mcp-adapters` to connect to the AgentCore gateway and `create_react_agent` from LangGraph for the agent loop. ![Langchain web search Architecture](images/langchain-web-search-architecture.png) @@ -10,7 +10,7 @@ This demo shows the same Web Search Tool integration using LangChain and LangGra - Python 3.10+ - AWS account with Amazon Bedrock enabled in **us-east-1** -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- AWS credentials with IAM, Cognito, and AgentCore gateway permissions - Claude Sonnet 4 model access enabled in Bedrock ```bash diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py index 8d42e1b8b..98157b22e 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py @@ -99,7 +99,7 @@ def main(): region = args.region print("=" * 60) - print("AgentCore Web Search Tool — Resource Cleanup") + print("AgentCore web search tool — Resource Cleanup") print("=" * 60) print(f"\nRegion: {region}") print(f"Gateway ID: {args.gateway_id}") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py index e1fe26293..c11b1b6f9 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py @@ -1,4 +1,4 @@ -"""Set up AgentCore Gateway with Web Search Tool — delegates to shared utility. +"""Set up AgentCore gateway with Web Search Tool — delegates to shared utility. Usage: python setup_gateway.py diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py index 491e6ae97..00d9c0f43 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py @@ -105,7 +105,7 @@ async def run_agent(query: str): def parse_args(): parser = argparse.ArgumentParser( - description="LangChain agent with Web Search Tool via AgentCore Gateway" + description="LangChain agent with Web Search Tool via AgentCore gateway" ) parser.add_argument( "--query", @@ -119,7 +119,7 @@ def main(): args = parse_args() print("=" * 60) - print("AgentCore Web Search Tool — LangChain Agent") + print("AgentCore web search tool — LangChain Agent") print("=" * 60) print(f"\nQuery: {args.query}\n") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md index 82bcb6707..ba817c7df 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/README.md @@ -1,8 +1,8 @@ -# AgentCore Web Search Tool +# AgentCore web search tool ![Web Search Tool Architecture](images/tutorial-architecture.png) -[Amazon Bedrock AgentCore Web Search Tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-connector-web-search-tool.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore Gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. +[Amazon Bedrock AgentCore web search tool](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-connector-web-search-tool.html) exposes web search as a fully managed, MCP-compliant tool through Amazon Bedrock AgentCore gateway. Your agents discover and invoke it using the standard Model Context Protocol — no custom integrations, no infrastructure to manage. > 🔒 **Search Privacy**: The Web Search Tool queries an AWS-maintained search index. Queries do not route to any third-party search engines or external providers. @@ -26,14 +26,14 @@ - AWS account with Amazon Bedrock enabled in **us-east-1** - Python 3.10+ -- AWS credentials with IAM, Cognito, and AgentCore Gateway permissions +- AWS credentials with IAM, Cognito, and AgentCore gateway permissions - Model access enabled for Claude Sonnet 4 (cross-region inference profile) ```bash pip install -r requirements.txt ``` -> **Note:** The Web Search Tool connector is currently available in **us-east-1** only. +> **Note:** The Web Search Tool connector is available in [supported regions](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-connector-web-search-tool.html#gateway-target-connector-web-search-tool-availability). Set your region accordingly when running the setup script. ## Core Concepts @@ -41,11 +41,11 @@ pip install -r requirements.txt The Web Search Tool uses the **connector** target type — a fully AWS-managed integration that requires no schema, no endpoint configuration, and no outbound credential setup. You specify `connectorId: "web-search"` and the Gateway handles everything else. -![Web search with Amazon Bedrock AgentCore Gateway](images/agentcore-web-search-architecture.png) +![Web search with Amazon Bedrock AgentCore gateway](images/agentcore-web-search-architecture.png) ### How It Works -1. **Gateway setup** — Create an AgentCore Gateway and add a Web Search Tool target using `connectorId: "web-search"` +1. **Gateway setup** — Create an AgentCore gateway and add a Web Search Tool target using `connectorId: "web-search"` 2. **Tool discovery** — Your agent calls `tools/list` on the Gateway endpoint and discovers `WebSearch` with its input schema 3. **Search invocation** — Your agent calls `tools/call` with a natural language query (up to 200 characters) 4. **Structured results** — The tool returns results with text snippets, URLs, titles, and publication dates @@ -107,5 +107,5 @@ python 01-raw-mcp/cleanup.py --gateway-id --user-pool-id --role-name < ## Documentation - [Web Search 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Args: gateway_url: The Gateway MCP endpoint URL. diff --git a/01-features/03-connect-your-agent-to-anything/README.md b/01-features/03-connect-your-agent-to-anything/README.md index 6fa5e51d9..a9a6e4ee7 100644 --- a/01-features/03-connect-your-agent-to-anything/README.md +++ b/01-features/03-connect-your-agent-to-anything/README.md @@ -8,11 +8,11 @@ Give your agents access to powerful built-in tool environments — sandboxed cod |:-------|:--------------| | [`01-code-interpreter/`](./01-code-interpreter/) | Sandboxed Python execution environment — run code, execute shell commands, upload and read files, use the AWS CLI, all in an isolated per-session sandbox | | [`02-browser/`](./02-browser/) | Fully managed headless Chromium browser — drive it with Nova Act, Browser-Use, Strands, or raw Playwright via the Chrome DevTools Protocol | -| [`03-web-search/`](./03-web-search/) | Real-time web search as an MCP-compliant tool — ground your agents in current information via AgentCore Gateway with zero infrastructure to manage | +| [`03-web-search/`](./03-web-search/) | Real-time web search as an MCP-compliant tool — ground your agents in current information via AgentCore gateway with zero infrastructure to manage | ## How these tools work -Code Interpreter and Browser follow the same pattern: AgentCore provisions an isolated sandbox session on demand, your agent calls tool APIs within that session, and the session terminates when you stop it. Web Search uses a different pattern — it's exposed as an MCP-compliant connector through AgentCore Gateway, so your agent discovers and invokes it via standard MCP protocol calls. All three require no infrastructure to manage. +Code Interpreter and Browser follow the same pattern: AgentCore provisions an isolated sandbox session on demand, your agent calls tool APIs within that session, and the session terminates when you stop it. Web Search uses a different pattern — it's exposed as an MCP-compliant connector through AgentCore gateway, so your agent discovers and invokes it via standard MCP protocol calls. All three require no infrastructure to manage. ### Code Interpreter @@ -47,7 +47,7 @@ with browser_session("us-west-2") as client: ### Web Search Tool -- **What it is**: A fully managed web search connector exposed through AgentCore Gateway via MCP +- **What it is**: A fully managed web search connector exposed through AgentCore gateway via MCP - **Use it for**: Agents that need real-time information — current events, latest releases, fact-checking, competitive intelligence - **Entry point**: Create a Gateway with `connectorId: "web-search"`, then connect any MCP client @@ -79,7 +79,7 @@ python 02-browser/01-nova-act/getting_started.py \ # Web Search Tool pip install -r 03-web-search/requirements.txt -python 03-web-search/01-setup-gateway/setup_gateway.py +python 03-web-search/01-raw-mcp/setup_gateway.py # Load the credentials written by setup: source .env.web-search python 03-web-search/03-strands-agent/web_search_strands.py @@ -89,5 +89,5 @@ python 03-web-search/03-strands-agent/web_search_strands.py - [Code Interpreter — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/code-interpreter-overview.html) - [Browser Tool — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/browser-tool-overview.html) -- [AgentCore Gateway — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) +- [AgentCore gateway — Developer Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) - [boto3 Data Plane Reference (`bedrock-agentcore`)](https://docs.aws.amazon.com/boto3/latest/reference/services/bedrock-agentcore.html) diff --git a/01-features/README.md b/01-features/README.md index b19e3290d..2ab63944f 100644 --- a/01-features/README.md +++ b/01-features/README.md @@ -21,6 +21,7 @@ AgentCore services work together or independently with any open-source framework | **payments** | Microtransaction payments for agents via the x402 protocol — wallet integration, configurable spending limits, and end-to-end observability. | | **code interpreter** | Isolated sandbox for agents to execute Python, JavaScript, and TypeScript code. | | **browser** | Managed cloud browser for agents to interact with web applications, navigate sites, fill forms, and extract information. | +| **web search** | Real-time web search as a managed MCP connector through AgentCore gateway. Agents discover and invoke it via standard MCP protocol — no search APIs to provision. | | **harness** | Serverless agent orchestration layer — model, tools, system prompt, and context management in a single API call, without managing a runtime. | ![AgentCore map](../00-getting-started/images/agentcore-map.png) @@ -78,7 +79,7 @@ data = boto3.client('bedrock-agentcore', region_name='us-west-2') |:--|:-------|:--------------| | 01 | [harness](01-harness/) | Serverless agent orchestration environment — model, tools, sandbox, and session management in a single API call | | 02 | [host your agent](02-host-your-agent/) | Deploy agents and MCP tool servers on AgentCore runtime; multi-protocol (HTTP, MCP, A2A, AG-UI), streaming, sessions, async, VPC, and coding agents | -| 03 | [connect your agent to anything](03-connect-your-agent-to-anything/) | Built-in managed tools: sandboxed Python code execution (Code Interpreter) and headless browser automation (Browser Tool) | +| 03 | [connect your agent to anything](03-connect-your-agent-to-anything/) | Built-in managed tools: sandboxed Python code execution (Code Interpreter), headless browser automation (Browser Tool), and real-time web search (Web Search Tool) | | 04 | [manage context of your agent](04-manage-context-of-your-agent/) | Short-term session memory and long-term persistent memory for context-aware agents | | 05 | [authenticate and authorize](05-authenticate-and-authorize/) | Inbound auth (Cognito, Entra ID, Okta, PingFederate) and outbound auth (OAuth2, API keys, 3LO, M2M, OBO) | | 06 | [observe, evaluate, and optimize](06-observe-evaluate-optimize-your-agent/) | Trace and debug with OpenTelemetry, evaluate with LLM-as-a-judge and ground-truth evaluators, optimize prompts and tool descriptions | diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/README.md b/02-use-cases/01-conversational-agents/deep-research-agent/README.md index d016196dd..2eb4f1167 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/README.md +++ b/02-use-cases/01-conversational-agents/deep-research-agent/README.md @@ -12,7 +12,7 @@ Single-shot web search works for simple factual queries. For questions that requ |:------------|:--------| | Use case type | Research / Question answering | | Agent type | Single agent | -| AgentCore components | AgentCore Gateway, AgentCore Runtime | +| AgentCore components | AgentCore gateway, AgentCore runtime | | Agentic framework | Strands Agents | | LLM model | Anthropic Claude Sonnet 4 | | Use case vertical | Cross-vertical | @@ -46,8 +46,8 @@ Single-shot web search works for simple factual queries. For questions that requ - **Configurable depth** — set `--max-iter` (or `DEEP_RESEARCH_MAX_ITER`) to tune depth vs. cost - **Transparent reasoning** — plan, search queries, and reflections are visible in the output - **Cited answers** — every factual claim is backed by a source URL -- **AgentCore Runtime** — production-ready hosting via `BedrockAgentCoreApp` -- **CLI + runtime modes** — run locally with `--query` or deploy to AgentCore Runtime +- **AgentCore runtime** — production-ready hosting via `BedrockAgentCoreApp` +- **CLI + runtime modes** — run locally with `--query` or deploy to AgentCore runtime ## Tuning the Loop @@ -130,7 +130,7 @@ export COGNITO_SCOPE="agentcore-websearch/invoke" export AWS_DEFAULT_REGION="us-east-1" ``` -### 4. (Optional) Deploy to AgentCore Runtime +### 4. (Optional) Deploy to AgentCore runtime For runtime deployments, environment variables **must** be pre-configured in the container environment (no interactive provisioning in runtime mode): @@ -140,7 +140,7 @@ container environment (no interactive provisioning in runtime mode): python deep_research_agent.py # starts the runtime server when deployed ``` -When deployed, invoke via the AgentCore Runtime API: +When deployed, invoke via the AgentCore runtime API: ```json { @@ -199,7 +199,7 @@ See the Prerequisites section above for the full list of permissions needed to c | File | Description | |:-----|:------------| -| `deep_research_agent.py` | Main agent — Plan/Search/Reflect loop, AgentCore Runtime entrypoint, and CLI | +| `deep_research_agent.py` | Main agent — Plan/Search/Reflect loop, AgentCore runtime entrypoint, and CLI | | `gateway_setup.py` | Auto-detection and provisioning of Gateway + Web Search infrastructure | | `cleanup.py` | Deletes all provisioned AWS resources and local credentials | | `requirements.txt` | Python dependencies | @@ -218,7 +218,7 @@ The system prompt encodes the loop directly. Claude: ### Gateway Integration -The agent connects to AgentCore Gateway via MCP Streamable HTTP. The Gateway exposes the Web Search connector as a standard MCP `WebSearch` tool. Tool discovery (`tools/list`) and invocation (`tools/call`) happen automatically through the Strands `MCPClient`. +The agent connects to AgentCore gateway via MCP Streamable HTTP. The Gateway exposes the Web Search connector as a standard MCP `WebSearch` tool. Tool discovery (`tools/list`) and invocation (`tools/call`) happen automatically through the Strands `MCPClient`. ### Auth Flow @@ -232,4 +232,4 @@ deep_research_agent.py ## Related Resources - [`01-features/03-connect-your-agent-to-anything/03-web-search/`](../../../01-features/03-connect-your-agent-to-anything/03-web-search/) — Gateway setup, raw MCP, and basic agent demos -- [AgentCore Gateway documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) +- [AgentCore gateway documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py index 1de63f7b7..5e52cebf4 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py +++ b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py @@ -560,7 +560,7 @@ def ensure_gateway(interactive: bool = True) -> GatewayConfig: print(" • AgentCore Gateway (MCP protocol)") print(" • Web Search connector target") print() - print(" Region: us-east-1 (only supported region for Web Search)") + print(f" Region: {REGION}") print(" Estimated time: ~60 seconds") print("-" * 60) diff --git a/README.md b/README.md index 0ce6004cb..166733217 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ Focused examples for individual AgentCore capabilities: - **[Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html)** — Convert APIs, Lambda functions, and services into MCP-compatible tools - **[Identity](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/identity.html)** — Agent identity and access management across AWS and third-party apps - **[Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html)** — Managed memory infrastructure for personalized agent experiences -- **[Tools](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/code-interpreter-tool.html)** — Built-in Code Interpreter and Browser Tool +- **[Tools](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/code-interpreter-tool.html)** — Built-in Code Interpreter, Browser Tool, Web Search Tool - **[Observability](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability.html)** — Trace, debug, and monitor agent performance with OpenTelemetry - **[Evaluation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/evaluations.html)** — Built-in and custom evaluators for on-demand and online evaluation - **[Policy](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/policy.html)** — Fine-grained access control with Cedar policies @@ -159,7 +159,7 @@ agentcore add online-eval # Enable continuous evaluation agentcore deploy # Sync changes to AWS ``` -Congratulations! Your agent is now running on Amazon Bedrock AgentCore Runtime. +Congratulations! Your agent is now running on Amazon Bedrock AgentCore runtime. For the full CLI reference, see the [AgentCore CLI documentation](https://github.com/aws/agentcore-cli). From f1f3fb81f7dd3b34c84f585e9caa13ad9a42503e Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 20:24:15 -0400 Subject: [PATCH 16/21] Update README.md Signed-off-by: Naga Gaddamu --- .../03-web-search/02-strands-agent/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md index f8a3e63ea..7e9871327 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md @@ -44,7 +44,7 @@ python web_search_strands.py --query "Current price of Bitcoin" ## How It Works -The script connects to the Gateway, creates an agent, and runs a research query: +The script creates an agent that connects to the Gateway, and runs a research query to the agent: ### Step 1: Connect to the Gateway From a4defdee698ca65ae2afc0bbae814345fddc9730 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 20:24:32 -0400 Subject: [PATCH 17/21] Update README.md Signed-off-by: Naga Gaddamu --- .../03-web-search/02-strands-agent/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md index 7e9871327..063b1cd2a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/README.md @@ -44,7 +44,7 @@ python web_search_strands.py --query "Current price of Bitcoin" ## How It Works -The script creates an agent that connects to the Gateway, and runs a research query to the agent: +The script creates an agent that connects to the Gateway, and runs a research query to the agent. ### Step 1: Connect to the Gateway From 0209197f0d98544e83baf9553f748b423b2f9b68 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 20:26:54 -0400 Subject: [PATCH 18/21] Update web_search_strands.py Signed-off-by: Naga Gaddamu --- .../03-web-search/02-strands-agent/web_search_strands.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py index b58416fc1..36a872d39 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py @@ -1,8 +1,7 @@ """ Web Search with a Strands AI Agent. -Demonstrates a Strands agent that uses the Web Search Tool via AgentCore -Gateway to answer real-time questions: +Demonstrates a Strands agent that uses the Web Search Tool via AgentCore gateway to answer real-time questions: 1. Connect to the Gateway using MCP Streamable HTTP transport 2. Discover the WebSearch tool via tools/list 3. Create a Strands agent with the discovered tools From 5bda933ce90c4d05fc280d2e2a0fd6e48d7aeccf Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 20:30:04 -0400 Subject: [PATCH 19/21] Update web_search_langchain.py Signed-off-by: Naga Gaddamu --- .../03-web-search/03-langchain-agent/web_search_langchain.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py index 00d9c0f43..3584dfc56 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py @@ -1,8 +1,7 @@ """ Web Search with a LangChain Agent. -Demonstrates a LangChain agent that uses the Web Search Tool via AgentCore -Gateway to answer real-time questions: +Demonstrates a LangChain agent that uses the Web Search Tool via AgentCore gateway to answer real-time questions: 1. Connect to the Gateway using langchain-mcp-adapters MultiServerMCPClient 2. Discover tools from the Gateway 3. Create a LangChain agent with ChatBedrockConverse From 472f705e77834c6e8a990247e57dce505b3f10a9 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Mon, 22 Jun 2026 22:07:07 -0400 Subject: [PATCH 20/21] Fixing linting issues and CodeQL alerts --- .../03-web-search/utils/gateway_setup.py | 7 ++++--- .../deep-research-agent/gateway_setup.py | 3 ++- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py index 31070b292..dcd4373f4 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py @@ -341,7 +341,7 @@ def main(): # Write credentials to a local .env file next to the script that invoked setup caller_dir = os.path.dirname(os.path.abspath(sys.argv[0])) if sys.argv[0] else os.getcwd() env_file = os.path.join(caller_dir, ".env.web-search") - + # START nosec - intentional for local development workflow with open(env_file, "w") as f: f.write(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"\n") f.write(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"\n") @@ -352,7 +352,8 @@ def main(): f.write(f'export GATEWAY_ID="{gateway_id}"\n') f.write(f'export USER_POOL_ID="{cognito_config["user_pool_id"]}"\n') f.write(f'export ROLE_NAME="{role_name}"\n') - f.write(f'# Cleanup resource IDs\n') + f.write('# Cleanup resource IDs\n') + #END nosec - intentional for local development workflow print(f"\n✅ Credentials written to: {env_file}") print(" Load them with: source .env.web-search\n") @@ -361,7 +362,7 @@ def main(): print(f" IAM Role: {role_name}") print(f" Cognito Pool: {cognito_config['user_pool_id']}") print(f"\n⚠️ Keep {env_file} secure — it contains your client secret.") - print(f" Add it to .gitignore to avoid committing it.") + print(" Add it to .gitignore to avoid committing it.") if __name__ == "__main__": diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py index 5e52cebf4..6c22115e1 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py +++ b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py @@ -82,6 +82,7 @@ def export_to_env(self): def print_env_vars(self): """Write credentials to a local .env file and print non-sensitive info.""" env_file = ".env.web-search" + # START nosec - intentional for local development workflow with open(env_file, "w") as f: f.write(f'export AGENTCORE_GATEWAY_URL="{self.gateway_url}"\n') f.write(f'export COGNITO_DOMAIN="{self.cognito_domain}"\n') @@ -89,7 +90,7 @@ def print_env_vars(self): f.write(f'export COGNITO_CLIENT_SECRET="{self.cognito_client_secret}"\n') f.write(f'export COGNITO_SCOPE="{self.cognito_scope}"\n') f.write(f'export AWS_DEFAULT_REGION="{self.region}"\n') - + # END nosec - intentional for local development workflow print(f"\n ✅ Credentials written to: {env_file}") print(f" Load them with: source {env_file}\n") print(f" Gateway URL: {self.gateway_url}") From 008bc4bace27094d80e7258c60d1238d522bd8d7 Mon Sep 17 00:00:00 2001 From: Naga Gaddamu Date: Tue, 23 Jun 2026 13:40:25 -0400 Subject: [PATCH 21/21] Fixed Ruff issues --- .../03-web-search/01-raw-mcp/cleanup.py | 36 +++------- .../03-web-search/01-raw-mcp/raw_mcp_call.py | 8 +-- .../03-web-search/01-raw-mcp/setup_gateway.py | 1 + .../03-web-search/02-strands-agent/cleanup.py | 36 +++------- .../02-strands-agent/setup_gateway.py | 1 + .../02-strands-agent/web_search_strands.py | 4 +- .../03-langchain-agent/cleanup.py | 36 +++------- .../03-langchain-agent/setup_gateway.py | 11 ++-- .../web_search_langchain.py | 14 ++-- .../03-web-search/utils/gateway_auth.py | 4 +- .../03-web-search/utils/gateway_setup.py | 61 ++++++----------- .../03-web-search/utils/web_search_agent.py | 9 +-- .../deep-research-agent/cleanup.py | 36 +++------- .../deep_research_agent.py | 7 +- .../deep-research-agent/gateway_setup.py | 65 ++++++------------- 15 files changed, 95 insertions(+), 234 deletions(-) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py index 98157b22e..4d231f659 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/cleanup.py @@ -28,13 +28,9 @@ def delete_gateway(gateway_client, gateway_id): """Delete all targets and the gateway itself.""" print("\n[1/3] Deleting Gateway resources...") try: - targets = gateway_client.list_gateway_targets( - gatewayIdentifier=gateway_id, maxResults=100 - ) + targets = gateway_client.list_gateway_targets(gatewayIdentifier=gateway_id, maxResults=100) for item in targets["items"]: - gateway_client.delete_gateway_target( - gatewayIdentifier=gateway_id, targetId=item["targetId"] - ) + gateway_client.delete_gateway_target(gatewayIdentifier=gateway_id, targetId=item["targetId"]) print(f" Deleted target: {item['name']}") time.sleep(10) @@ -49,9 +45,7 @@ def delete_cognito(cognito_client, user_pool_id): print("\n[2/3] Deleting Cognito resources...") try: domain = user_pool_id.replace("_", "").lower() - cognito_client.delete_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.delete_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) cognito_client.delete_user_pool(UserPoolId=user_pool_id) print(f" Deleted user pool: {user_pool_id}") except Exception as e: @@ -64,9 +58,7 @@ def delete_iam_role(iam_client, role_name): try: policies = iam_client.list_role_policies(RoleName=role_name) for policy_name in policies["PolicyNames"]: - iam_client.delete_role_policy( - RoleName=role_name, PolicyName=policy_name - ) + iam_client.delete_role_policy(RoleName=role_name, PolicyName=policy_name) print(f" Deleted policy: {policy_name}") iam_client.delete_role(RoleName=role_name) @@ -76,21 +68,11 @@ def delete_iam_role(iam_client, role_name): def parse_args(): - parser = argparse.ArgumentParser( - description="Clean up Web Search Tool Gateway resources" - ) - parser.add_argument( - "--gateway-id", required=True, help="Gateway ID to delete" - ) - parser.add_argument( - "--user-pool-id", required=True, help="Cognito User Pool ID to delete" - ) - parser.add_argument( - "--role-name", required=True, help="IAM role name to delete" - ) - parser.add_argument( - "--region", default=REGION, help="AWS region (default: us-east-1)" - ) + parser = argparse.ArgumentParser(description="Clean up Web Search Tool Gateway resources") + parser.add_argument("--gateway-id", required=True, help="Gateway ID to delete") + parser.add_argument("--user-pool-id", required=True, help="Cognito User Pool ID to delete") + parser.add_argument("--role-name", required=True, help="IAM role name to delete") + parser.add_argument("--region", default=REGION, help="AWS region (default: us-east-1)") return parser.parse_args() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py index 4d86c18a5..1b0a6d924 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/raw_mcp_call.py @@ -48,9 +48,7 @@ def parse_args(): - parser = argparse.ArgumentParser( - description="Raw MCP tool discovery and invocation against AgentCore gateway" - ) + parser = argparse.ArgumentParser(description="Raw MCP tool discovery and invocation against AgentCore gateway") parser.add_argument( "--query", default=DEFAULT_QUERY, @@ -99,9 +97,7 @@ def main(): return ws_tool_name = ws_tools[0].tool_name - result = mcp_client.call_tool_sync( - "raw-mcp-demo", ws_tool_name, {"query": args.query} - ) + result = mcp_client.call_tool_sync("raw-mcp-demo", ws_tool_name, {"query": args.query}) # Step 3: Display results print("[3] Results:\n") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py index 30c6a7121..8824d5c10 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/01-raw-mcp/setup_gateway.py @@ -7,6 +7,7 @@ python setup_gateway.py --gateway-name my-gateway python setup_gateway.py --region us-east-1 """ + import sys import os diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py index 98157b22e..4d231f659 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/cleanup.py @@ -28,13 +28,9 @@ def delete_gateway(gateway_client, gateway_id): """Delete all targets and the gateway itself.""" print("\n[1/3] Deleting Gateway resources...") try: - targets = gateway_client.list_gateway_targets( - gatewayIdentifier=gateway_id, maxResults=100 - ) + targets = gateway_client.list_gateway_targets(gatewayIdentifier=gateway_id, maxResults=100) for item in targets["items"]: - gateway_client.delete_gateway_target( - gatewayIdentifier=gateway_id, targetId=item["targetId"] - ) + gateway_client.delete_gateway_target(gatewayIdentifier=gateway_id, targetId=item["targetId"]) print(f" Deleted target: {item['name']}") time.sleep(10) @@ -49,9 +45,7 @@ def delete_cognito(cognito_client, user_pool_id): print("\n[2/3] Deleting Cognito resources...") try: domain = user_pool_id.replace("_", "").lower() - cognito_client.delete_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.delete_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) cognito_client.delete_user_pool(UserPoolId=user_pool_id) print(f" Deleted user pool: {user_pool_id}") except Exception as e: @@ -64,9 +58,7 @@ def delete_iam_role(iam_client, role_name): try: policies = iam_client.list_role_policies(RoleName=role_name) for policy_name in policies["PolicyNames"]: - iam_client.delete_role_policy( - RoleName=role_name, PolicyName=policy_name - ) + iam_client.delete_role_policy(RoleName=role_name, PolicyName=policy_name) print(f" Deleted policy: {policy_name}") iam_client.delete_role(RoleName=role_name) @@ -76,21 +68,11 @@ def delete_iam_role(iam_client, role_name): def parse_args(): - parser = argparse.ArgumentParser( - description="Clean up Web Search Tool Gateway resources" - ) - parser.add_argument( - "--gateway-id", required=True, help="Gateway ID to delete" - ) - parser.add_argument( - "--user-pool-id", required=True, help="Cognito User Pool ID to delete" - ) - parser.add_argument( - "--role-name", required=True, help="IAM role name to delete" - ) - parser.add_argument( - "--region", default=REGION, help="AWS region (default: us-east-1)" - ) + parser = argparse.ArgumentParser(description="Clean up Web Search Tool Gateway resources") + parser.add_argument("--gateway-id", required=True, help="Gateway ID to delete") + parser.add_argument("--user-pool-id", required=True, help="Cognito User Pool ID to delete") + parser.add_argument("--role-name", required=True, help="IAM role name to delete") + parser.add_argument("--region", default=REGION, help="AWS region (default: us-east-1)") return parser.parse_args() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py index 30c6a7121..8824d5c10 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/setup_gateway.py @@ -7,6 +7,7 @@ python setup_gateway.py --gateway-name my-gateway python setup_gateway.py --region us-east-1 """ + import sys import os diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py index b58416fc1..9b39f33fb 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/02-strands-agent/web_search_strands.py @@ -67,9 +67,7 @@ def run_query(agent, query: str): def parse_args(): - parser = argparse.ArgumentParser( - description="Strands agent with Web Search Tool via AgentCore gateway" - ) + parser = argparse.ArgumentParser(description="Strands agent with Web Search Tool via AgentCore gateway") parser.add_argument( "--query", default=None, diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py index 98157b22e..4d231f659 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/cleanup.py @@ -28,13 +28,9 @@ def delete_gateway(gateway_client, gateway_id): """Delete all targets and the gateway itself.""" print("\n[1/3] Deleting Gateway resources...") try: - targets = gateway_client.list_gateway_targets( - gatewayIdentifier=gateway_id, maxResults=100 - ) + targets = gateway_client.list_gateway_targets(gatewayIdentifier=gateway_id, maxResults=100) for item in targets["items"]: - gateway_client.delete_gateway_target( - gatewayIdentifier=gateway_id, targetId=item["targetId"] - ) + gateway_client.delete_gateway_target(gatewayIdentifier=gateway_id, targetId=item["targetId"]) print(f" Deleted target: {item['name']}") time.sleep(10) @@ -49,9 +45,7 @@ def delete_cognito(cognito_client, user_pool_id): print("\n[2/3] Deleting Cognito resources...") try: domain = user_pool_id.replace("_", "").lower() - cognito_client.delete_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.delete_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) cognito_client.delete_user_pool(UserPoolId=user_pool_id) print(f" Deleted user pool: {user_pool_id}") except Exception as e: @@ -64,9 +58,7 @@ def delete_iam_role(iam_client, role_name): try: policies = iam_client.list_role_policies(RoleName=role_name) for policy_name in policies["PolicyNames"]: - iam_client.delete_role_policy( - RoleName=role_name, PolicyName=policy_name - ) + iam_client.delete_role_policy(RoleName=role_name, PolicyName=policy_name) print(f" Deleted policy: {policy_name}") iam_client.delete_role(RoleName=role_name) @@ -76,21 +68,11 @@ def delete_iam_role(iam_client, role_name): def parse_args(): - parser = argparse.ArgumentParser( - description="Clean up Web Search Tool Gateway resources" - ) - parser.add_argument( - "--gateway-id", required=True, help="Gateway ID to delete" - ) - parser.add_argument( - "--user-pool-id", required=True, help="Cognito User Pool ID to delete" - ) - parser.add_argument( - "--role-name", required=True, help="IAM role name to delete" - ) - parser.add_argument( - "--region", default=REGION, help="AWS region (default: us-east-1)" - ) + parser = argparse.ArgumentParser(description="Clean up Web Search Tool Gateway resources") + parser.add_argument("--gateway-id", required=True, help="Gateway ID to delete") + parser.add_argument("--user-pool-id", required=True, help="Cognito User Pool ID to delete") + parser.add_argument("--role-name", required=True, help="IAM role name to delete") + parser.add_argument("--region", default=REGION, help="AWS region (default: us-east-1)") return parser.parse_args() diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py index c11b1b6f9..2b66af2f7 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/setup_gateway.py @@ -1,10 +1,11 @@ """Set up AgentCore gateway with Web Search Tool — delegates to shared utility. - - Usage: - python setup_gateway.py - python setup_gateway.py --gateway-name my-gateway - python setup_gateway.py --region us-east-1 + +Usage: +python setup_gateway.py +python setup_gateway.py --gateway-name my-gateway +python setup_gateway.py --region us-east-1 """ + import sys import os diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py index 00d9c0f43..9876778fe 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/03-langchain-agent/web_search_langchain.py @@ -46,9 +46,7 @@ # ── Configuration ───────────────────────────────────────────────────────────── REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") -MODEL_ID = os.getenv( - "BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6" -) +MODEL_ID = os.getenv("BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6") GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_URL", "") DEFAULT_QUERY = "What is today's news around the world?" @@ -84,11 +82,9 @@ async def run_agent(query: str): print(f" Discovered {len(tools)} tool(s)") # Create and run the agent - #agent = create_react_agent(model, tools=tools) + # agent = create_react_agent(model, tools=tools) agent = create_agent(model, tools=tools) - result = await agent.ainvoke( - {"messages": [{"role": "user", "content": query}]} - ) + result = await agent.ainvoke({"messages": [{"role": "user", "content": query}]}) # Print the final response print("\n[Agent Response]") @@ -104,9 +100,7 @@ async def run_agent(query: str): def parse_args(): - parser = argparse.ArgumentParser( - description="LangChain agent with Web Search Tool via AgentCore gateway" - ) + parser = argparse.ArgumentParser(description="LangChain agent with Web Search Tool via AgentCore gateway") parser.add_argument( "--query", default=DEFAULT_QUERY, diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py index bb8cd0bb2..184648023 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_auth.py @@ -90,9 +90,7 @@ def create_streamable_http_transport(gateway_url: str = "", **token_kwargs): """ gateway_url = gateway_url or GATEWAY_URL if not gateway_url: - raise ValueError( - "Gateway URL not configured. Set AGENTCORE_GATEWAY_URL environment variable." - ) + raise ValueError("Gateway URL not configured. Set AGENTCORE_GATEWAY_URL environment variable.") token = get_oauth_token(**token_kwargs) diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py index dcd4373f4..da67202a3 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/gateway_setup.py @@ -81,9 +81,7 @@ def create_gateway_role(iam_client, role_name, account_id, region): "Action": "sts:AssumeRole", "Condition": { "StringEquals": {"aws:SourceAccount": account_id}, - "ArnLike": { - "aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*" - }, + "ArnLike": {"aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*"}, }, } ], @@ -148,18 +146,14 @@ def create_cognito_resources(cognito_client, region): create_resp = cognito_client.create_user_pool(PoolName=pool_name) user_pool_id = create_resp["UserPool"]["Id"] domain = user_pool_id.replace("_", "").lower() - cognito_client.create_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.create_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) print(f" Created user pool: {user_pool_id}") else: print(f" User pool exists: {user_pool_id}") # Create resource server try: - cognito_client.describe_resource_server( - UserPoolId=user_pool_id, Identifier=resource_server_id - ) + cognito_client.describe_resource_server(UserPoolId=user_pool_id, Identifier=resource_server_id) except cognito_client.exceptions.ResourceNotFoundException: cognito_client.create_resource_server( UserPoolId=user_pool_id, @@ -171,13 +165,9 @@ def create_cognito_resources(cognito_client, region): # Find or create M2M client client_id, client_secret = None, None - for client in cognito_client.list_user_pool_clients( - UserPoolId=user_pool_id, MaxResults=60 - )["UserPoolClients"]: + for client in cognito_client.list_user_pool_clients(UserPoolId=user_pool_id, MaxResults=60)["UserPoolClients"]: if client["ClientName"] == "agentcore-websearch-client": - desc = cognito_client.describe_user_pool_client( - UserPoolId=user_pool_id, ClientId=client["ClientId"] - ) + desc = cognito_client.describe_user_pool_client(UserPoolId=user_pool_id, ClientId=client["ClientId"]) client_id = client["ClientId"] client_secret = desc["UserPoolClient"]["ClientSecret"] break @@ -200,10 +190,7 @@ def create_cognito_resources(cognito_client, region): print(f" Client exists: {client_id}") domain = user_pool_id.replace("_", "").lower() - discovery_url = ( - f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}" - f"/.well-known/openid-configuration" - ) + discovery_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/openid-configuration" scope_string = " ".join(scope_names) return { @@ -222,9 +209,7 @@ def create_gateway(gateway_client, name, role_arn, cognito_config): name=name, roleArn=role_arn, protocolType="MCP", - protocolConfiguration={ - "mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"} - }, + protocolConfiguration={"mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"}}, authorizerType="CUSTOM_JWT", authorizerConfiguration={ "customJWTAuthorizer": { @@ -255,15 +240,11 @@ def create_web_search_target(gateway_client, gateway_id): "mcp": { "connector": { "source": {"connectorId": "web-search"}, - "configurations": [ - {"name": "WebSearch", "parameterValues": {}} - ], + "configurations": [{"name": "WebSearch", "parameterValues": {}}], } } }, - credentialProviderConfigurations=[ - {"credentialProviderType": "GATEWAY_IAM_ROLE"} - ], + credentialProviderConfigurations=[{"credentialProviderType": "GATEWAY_IAM_ROLE"}], ) target_id = target_response["targetId"] @@ -281,9 +262,7 @@ def create_web_search_target(gateway_client, gateway_id): def parse_args(): - parser = argparse.ArgumentParser( - description="Set up AgentCore gateway with Web Search Tool target" - ) + parser = argparse.ArgumentParser(description="Set up AgentCore gateway with Web Search Tool target") parser.add_argument( "--gateway-name", default="web-search-gateway", @@ -325,9 +304,7 @@ def main(): # Step 3: Gateway print("\n[3/4] Creating AgentCore gateway...") gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) - gateway_id, gateway_url = create_gateway( - gateway_client, args.gateway_name, role_arn, cognito_config - ) + gateway_id, gateway_url = create_gateway(gateway_client, args.gateway_name, role_arn, cognito_config) # Step 4: Web Search Target print("\n[4/4] Creating Web Search Tool target...") @@ -343,17 +320,17 @@ def main(): env_file = os.path.join(caller_dir, ".env.web-search") # START nosec - intentional for local development workflow with open(env_file, "w") as f: - f.write(f"export AGENTCORE_GATEWAY_URL=\"{gateway_url}\"\n") - f.write(f"export COGNITO_DOMAIN=\"{cognito_config['domain']}\"\n") - f.write(f"export COGNITO_CLIENT_ID=\"{cognito_config['client_id']}\"\n") - f.write(f"export COGNITO_CLIENT_SECRET=\"{cognito_config['client_secret']}\"\n") # noqa: E501 - f.write(f"export COGNITO_SCOPE=\"{cognito_config['scope']}\"\n") - f.write(f"export AWS_DEFAULT_REGION=\"{region}\"\n") + f.write(f'export AGENTCORE_GATEWAY_URL="{gateway_url}"\n') + f.write(f'export COGNITO_DOMAIN="{cognito_config["domain"]}"\n') + f.write(f'export COGNITO_CLIENT_ID="{cognito_config["client_id"]}"\n') + f.write(f'export COGNITO_CLIENT_SECRET="{cognito_config["client_secret"]}"\n') # noqa: E501 + f.write(f'export COGNITO_SCOPE="{cognito_config["scope"]}"\n') + f.write(f'export AWS_DEFAULT_REGION="{region}"\n') f.write(f'export GATEWAY_ID="{gateway_id}"\n') f.write(f'export USER_POOL_ID="{cognito_config["user_pool_id"]}"\n') f.write(f'export ROLE_NAME="{role_name}"\n') - f.write('# Cleanup resource IDs\n') - #END nosec - intentional for local development workflow + f.write("# Cleanup resource IDs\n") + # END nosec - intentional for local development workflow print(f"\n✅ Credentials written to: {env_file}") print(" Load them with: source .env.web-search\n") diff --git a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py index 0c5538d1b..377967e9a 100644 --- a/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py +++ b/01-features/03-connect-your-agent-to-anything/03-web-search/utils/web_search_agent.py @@ -27,10 +27,7 @@ # ── Configuration ───────────────────────────────────────────────────────────── REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") -MODEL_ID = os.getenv( - "BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6" - -) +MODEL_ID = os.getenv("BEDROCK_MODEL_ID", "global.anthropic.claude-sonnet-4-6") SYSTEM_PROMPT = """You are a helpful research assistant with access to real-time web search. @@ -61,9 +58,7 @@ def create_mcp_client(gateway_url: str = "", **token_kwargs) -> MCPClient: Returns: MCPClient instance (must be used as a context manager). """ - transport_factory = create_streamable_http_transport( - gateway_url=gateway_url, **token_kwargs - ) + transport_factory = create_streamable_http_transport(gateway_url=gateway_url, **token_kwargs) return MCPClient(transport_factory) diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py b/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py index 1f99865ed..aff660cc0 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py +++ b/02-use-cases/01-conversational-agents/deep-research-agent/cleanup.py @@ -29,13 +29,9 @@ def delete_gateway(gateway_client, gateway_id): """Delete all targets and the gateway itself.""" print("\n[1/3] Deleting Gateway resources...") try: - targets = gateway_client.list_gateway_targets( - gatewayIdentifier=gateway_id, maxResults=100 - ) + targets = gateway_client.list_gateway_targets(gatewayIdentifier=gateway_id, maxResults=100) for item in targets["items"]: - gateway_client.delete_gateway_target( - gatewayIdentifier=gateway_id, targetId=item["targetId"] - ) + gateway_client.delete_gateway_target(gatewayIdentifier=gateway_id, targetId=item["targetId"]) print(f" Deleted target: {item['name']}") time.sleep(10) @@ -50,9 +46,7 @@ def delete_cognito(cognito_client, user_pool_id): print("\n[2/3] Deleting Cognito resources...") try: domain = user_pool_id.replace("_", "").lower() - cognito_client.delete_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.delete_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) cognito_client.delete_user_pool(UserPoolId=user_pool_id) print(f" Deleted user pool: {user_pool_id}") except Exception as e: @@ -65,9 +59,7 @@ def delete_iam_role(iam_client, role_name): try: policies = iam_client.list_role_policies(RoleName=role_name) for policy_name in policies["PolicyNames"]: - iam_client.delete_role_policy( - RoleName=role_name, PolicyName=policy_name - ) + iam_client.delete_role_policy(RoleName=role_name, PolicyName=policy_name) print(f" Deleted policy: {policy_name}") iam_client.delete_role(RoleName=role_name) @@ -85,21 +77,11 @@ def delete_env_file(): def parse_args(): - parser = argparse.ArgumentParser( - description="Clean up Deep Research Agent Gateway resources" - ) - parser.add_argument( - "--gateway-id", required=True, help="Gateway ID to delete" - ) - parser.add_argument( - "--user-pool-id", required=True, help="Cognito User Pool ID to delete" - ) - parser.add_argument( - "--role-name", required=True, help="IAM role name to delete" - ) - parser.add_argument( - "--region", default=REGION, help="AWS region (default: us-east-1)" - ) + parser = argparse.ArgumentParser(description="Clean up Deep Research Agent Gateway resources") + parser.add_argument("--gateway-id", required=True, help="Gateway ID to delete") + parser.add_argument("--user-pool-id", required=True, help="Cognito User Pool ID to delete") + parser.add_argument("--role-name", required=True, help="IAM role name to delete") + parser.add_argument("--region", default=REGION, help="AWS region (default: us-east-1)") return parser.parse_args() diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py b/02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py index 3c48102ac..7494a8299 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py +++ b/02-use-cases/01-conversational-agents/deep-research-agent/deep_research_agent.py @@ -63,9 +63,7 @@ # ── Configuration ────────────────────────────────────────────────────────────── REGION = os.getenv("AWS_DEFAULT_REGION", "us-east-1") -MODEL_ID = os.getenv( - "BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-6" -) +MODEL_ID = os.getenv("BEDROCK_MODEL_ID", "us.anthropic.claude-sonnet-4-6") MAX_SEARCH_ITERATIONS = int(os.getenv("DEEP_RESEARCH_MAX_ITER", "4")) # Configure logging @@ -284,8 +282,7 @@ def deep_research_runtime(payload): def parse_args(): parser = argparse.ArgumentParser( description=( - "Deep Research Agent — iterative Plan/Search/Reflect/Synthesize " - "loop via AgentCore Gateway Web Search Tool" + "Deep Research Agent — iterative Plan/Search/Reflect/Synthesize loop via AgentCore Gateway Web Search Tool" ) ) parser.add_argument( diff --git a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py index 6c22115e1..f96f64732 100644 --- a/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py +++ b/02-use-cases/01-conversational-agents/deep-research-agent/gateway_setup.py @@ -140,23 +140,17 @@ def _find_existing_gateway() -> dict | None: # List all gateways paginator_kwargs = {} while True: - response = client.list_gateways( - maxResults=50, **paginator_kwargs - ) + response = client.list_gateways(maxResults=50, **paginator_kwargs) for gw in response.get("items", []): gw_id = gw["gatewayId"] # Check if this gateway has a web-search target try: - targets = client.list_gateway_targets( - gatewayIdentifier=gw_id - ) + targets = client.list_gateway_targets(gatewayIdentifier=gw_id) for target in targets.get("items", []): target_name = target.get("name", "").lower() if "web-search" in target_name or "websearch" in target_name: # Get the full gateway details for the URL - gw_detail = client.get_gateway( - gatewayIdentifier=gw_id - ) + gw_detail = client.get_gateway(gatewayIdentifier=gw_id) return { "gateway_id": gw_id, "gateway_url": gw_detail["gatewayUrl"], @@ -191,9 +185,7 @@ def _find_cognito_for_gateway(gateway_id: str) -> dict | None: # Look for pools matching our naming conventions if "agentcore" in pool_name.lower() and "websearch" in pool_name.lower(): pool_id = pool["Id"] - clients = cognito_client.list_user_pool_clients( - UserPoolId=pool_id, MaxResults=60 - )["UserPoolClients"] + clients = cognito_client.list_user_pool_clients(UserPoolId=pool_id, MaxResults=60)["UserPoolClients"] for client_info in clients: if "websearch" in client_info["ClientName"].lower(): @@ -250,9 +242,7 @@ def _create_gateway_role(iam_client, role_name, account_id, region): "Action": "sts:AssumeRole", "Condition": { "StringEquals": {"aws:SourceAccount": account_id}, - "ArnLike": { - "aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*" - }, + "ArnLike": {"aws:SourceArn": f"arn:aws:bedrock-agentcore:{region}:{account_id}:*"}, }, } ], @@ -316,18 +306,14 @@ def _create_cognito_resources(cognito_client, region): create_resp = cognito_client.create_user_pool(PoolName=pool_name) user_pool_id = create_resp["UserPool"]["Id"] domain = user_pool_id.replace("_", "").lower() - cognito_client.create_user_pool_domain( - Domain=domain, UserPoolId=user_pool_id - ) + cognito_client.create_user_pool_domain(Domain=domain, UserPoolId=user_pool_id) print(f" Created user pool: {user_pool_id}") else: print(f" User pool exists: {user_pool_id}") # Create resource server try: - cognito_client.describe_resource_server( - UserPoolId=user_pool_id, Identifier=resource_server_id - ) + cognito_client.describe_resource_server(UserPoolId=user_pool_id, Identifier=resource_server_id) except cognito_client.exceptions.ResourceNotFoundException: cognito_client.create_resource_server( UserPoolId=user_pool_id, @@ -339,13 +325,9 @@ def _create_cognito_resources(cognito_client, region): # Find or create M2M client client_id, client_secret = None, None - for client in cognito_client.list_user_pool_clients( - UserPoolId=user_pool_id, MaxResults=60 - )["UserPoolClients"]: + for client in cognito_client.list_user_pool_clients(UserPoolId=user_pool_id, MaxResults=60)["UserPoolClients"]: if client["ClientName"] == "agentcore-websearch-client": - desc = cognito_client.describe_user_pool_client( - UserPoolId=user_pool_id, ClientId=client["ClientId"] - ) + desc = cognito_client.describe_user_pool_client(UserPoolId=user_pool_id, ClientId=client["ClientId"]) client_id = client["ClientId"] client_secret = desc["UserPoolClient"]["ClientSecret"] break @@ -368,10 +350,7 @@ def _create_cognito_resources(cognito_client, region): print(f" Client exists: {client_id}") domain = user_pool_id.replace("_", "").lower() - discovery_url = ( - f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}" - f"/.well-known/openid-configuration" - ) + discovery_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/openid-configuration" scope_string = " ".join(scope_names) return { @@ -390,9 +369,7 @@ def _create_gateway_and_target(gateway_client, name, role_arn, cognito_config): name=name, roleArn=role_arn, protocolType="MCP", - protocolConfiguration={ - "mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"} - }, + protocolConfiguration={"mcp": {"supportedVersions": ["2025-03-26"], "searchType": "SEMANTIC"}}, authorizerType="CUSTOM_JWT", authorizerConfiguration={ "customJWTAuthorizer": { @@ -419,15 +396,11 @@ def _create_gateway_and_target(gateway_client, name, role_arn, cognito_config): "mcp": { "connector": { "source": {"connectorId": "web-search"}, - "configurations": [ - {"name": "WebSearch", "parameterValues": {}} - ], + "configurations": [{"name": "WebSearch", "parameterValues": {}}], } } }, - credentialProviderConfigurations=[ - {"credentialProviderType": "GATEWAY_IAM_ROLE"} - ], + credentialProviderConfigurations=[{"credentialProviderType": "GATEWAY_IAM_ROLE"}], ) if _wait_for_targets_ready(gateway_client, gateway_id): @@ -470,9 +443,7 @@ def provision_gateway(gateway_name: str | None = None) -> GatewayConfig: # Step 3: Gateway + Target print("\n [3/4] Creating AgentCore Gateway...") gateway_client = boto3.client("bedrock-agentcore-control", region_name=region) - gateway_id, gateway_url = _create_gateway_and_target( - gateway_client, name, role_arn, cognito_config - ) + gateway_id, gateway_url = _create_gateway_and_target(gateway_client, name, role_arn, cognito_config) print("\n [4/4] Setup complete ✓") @@ -570,10 +541,14 @@ def ensure_gateway(interactive: bool = True) -> GatewayConfig: print("\n Aborted. To set up manually, you can either:") print() print(" Option 1 — Use the bundled setup module:") - print(" python -c \"from gateway_setup import provision_gateway; cfg = provision_gateway(); cfg.print_env_vars()\"") + print( + ' python -c "from gateway_setup import provision_gateway; cfg = provision_gateway(); cfg.print_env_vars()"' + ) print() print(" Option 2 — Use the standalone setup script:") - print(" python ../../01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py") + print( + " python ../../01-features/03-connect-your-agent-to-anything/03-web-search/01-setup-gateway/setup_gateway.py" + ) print() print(" Then export the printed environment variables and re-run the agent.") raise SystemExit(1)