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Reorganize docs structure and add forge-ui, TOON pages
- Homepage: reorganize features into Components section, add Example MCP Servers, move Tool RAG to Key Innovations - Add forge-ui docs page with screenshots - Add TOON Format docs page - Sidebar: rename Architecture to Components, add Techniques section with Tool RAG and TOON - Remove code examples from orchestrator, armory, interfaces docs (keep tables and diagrams) - Add new blog posts to sidebar
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‎.vitepress/config.ts‎

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@@ -48,22 +48,32 @@ export default defineConfig({
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]
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},
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{
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text: 'Architecture',
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text: 'Components',
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items: [
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{ text: 'Overview', link: '/docs/' },
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{ text: 'Orchestrator', link: '/docs/orchestrator' },
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{ text: 'Armory', link: '/docs/armory' },
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{ text: 'forge-ui', link: '/docs/forge-ui' },
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{ text: 'Anvil', link: '/docs/anvil' },
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{ text: 'Tool RAG', link: '/docs/tool-rag' },
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{ text: 'Interfaces', link: '/docs/interfaces' },
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]
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},
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{
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text: 'Techniques',
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items: [
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{ text: 'Tool RAG', link: '/docs/tool-rag' },
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{ text: 'TOON Format', link: '/docs/toon' },
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]
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}
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],
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'/blog/': [
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{
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text: 'Blog',
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items: [
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{ text: 'All Posts', link: '/blog/' },
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{ text: 'Cutting Context by 60%', link: '/blog/token-optimization-toon-rag' },
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{ text: 'Tool RAG: Dynamic Discovery', link: '/blog/tool-rag-dynamic-discovery' },
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{ text: 'TOON Format Support', link: '/blog/toon-format-support' },
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{ text: 'Multi-Provider Orchestrator', link: '/blog/multi-provider-orchestrator' },
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{ text: 'Armory MCP Gateway', link: '/blog/armory-mcp-gateway' },
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{ text: 'Streaming Tool Calls', link: '/blog/streaming-tool-calls' },

‎docs/armory.md‎

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Orchestrators connect to the Armory as a single MCP server. They don't need to know about the various backends - they just see a collection of tools.
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```python
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# Pydantic AI connects to Armory as MCP server
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from pydantic_ai import Agent
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from pydantic_ai.mcp import MCPServerSSE
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armory = MCPServerSSE('http://localhost:8000/mcp')
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agent = Agent('openai:gpt-4o', toolsets=[armory])
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async with agent.run_mcp_servers():
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# All tools from all backends are available
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result = await agent.run("Search and summarize")
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```
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### Protocol Translation
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The Armory translates between different protocol formats automatically:
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### Result Transformation (JSON → TOON)
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Tool results are converted to TOON format for token efficiency:
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```python
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class ResultTransformer:
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def transform(self, result: Any) -> str:
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# TOON for tabular data (30-40% savings)
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if self._is_tabular(result):
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return toon.encode(result)
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# JSON for complex nested structures
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return json.dumps(result)
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```
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Tool results are automatically converted to TOON format for token efficiency. Send `Accept: text/toon` header to enable.
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**Before (JSON):**
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```json
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args: ["-m", "forge_mcp_servers.notes"]
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```
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## Usage
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```python
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from agentic_forge import Armory
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# Load from config
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armory = Armory.from_config("armory.yaml")
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# Or configure programmatically
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armory = Armory()
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await armory.register_mcp_server("http://localhost:3001/mcp", prefix="fs_")
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await armory.register_rest_api("https://api.weather.com", tools=[...])
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await armory.register_local_function(my_function)
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# Start the server
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await armory.start() # Exposes MCP at http://localhost:8000/mcp
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```
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## Benefits
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| Problem | Solution |

‎docs/forge-ui.md‎

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# forge-ui
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Vue.js chat interface for interacting with AI agents through the Forge Orchestrator. Features real-time streaming, multi-model support, and tool call visualization.
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## Features
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| Feature | Description |
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|---------|-------------|
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| SSE Streaming | Real-time response streaming via Server-Sent Events |
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| Multi-Model Support | Switch between OpenAI, Anthropic, Google, and 300+ OpenRouter models |
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| Tool Call Visualization | Expandable tool calls showing arguments and results |
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| Thinking Blocks | Collapsible reasoning display for models that support it |
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| Conversation Management | Save, export, and import conversations as JSON |
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| Dark/Light Mode | Theme toggle with persistent preference |
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## Screenshots
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### Welcome Screen
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Start a new conversation or import a previously exported one.
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<div class="screenshot-container">
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<img src="/screenshots/forge-ui-landing.png" alt="Forge UI Welcome Screen" style="max-width: 100%; height: auto; border-radius: 8px;" />
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</div>
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### Chat Interface
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Real-time conversation with tool call chips and markdown rendering.
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<div class="screenshot-container">
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<img src="/screenshots/forge-ui.png" alt="Forge UI Chat Interface" style="max-width: 100%; height: auto; border-radius: 8px;" />
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</div>
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### Tool Call Details
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Click any tool call to inspect arguments and results.
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<div class="screenshot-container">
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<img src="/screenshots/forge-ui-tc-detail.png" alt="Tool Call Details" style="max-width: 100%; height: auto; border-radius: 8px;" />
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</div>
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### Model Management
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Browse and configure models from multiple providers. Fetch available models, add custom endpoints, and manage favorites.
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<div class="screenshot-container">
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<img src="/screenshots/forge-ui-model-selection.png" alt="Model Management" style="max-width: 100%; height: auto; border-radius: 8px;" />
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</div>
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## Installation
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```bash
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# Clone the repository
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git clone https://github.com/agentic-forge/forge-ui.git
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cd forge-ui
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# Install dependencies
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npm install
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# Start development server
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npm run dev
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```
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The UI runs on port 4040 by default: [http://localhost:4040](http://localhost:4040)
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## Configuration
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forge-ui connects to the Orchestrator API. Configure the endpoint in the Advanced settings or via environment variable:
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```bash
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VITE_ORCHESTRATOR_URL=http://localhost:4041
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```
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## Architecture
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```
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forge-ui
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├── src/
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│ ├── components/
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│ │ ├── ChatMessage.vue # Message rendering with markdown
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│ │ ├── ToolCallChip.vue # Expandable tool call display
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│ │ ├── ThinkingBlock.vue # Collapsible reasoning
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│ │ └── ModelSelector.vue # Model picker dropdown
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│ ├── views/
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│ │ └── ChatView.vue # Main chat interface
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│ └── services/
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│ └── api.ts # SSE streaming client
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```
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## Keyboard Shortcuts
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| Shortcut | Action |
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|----------|--------|
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| `Enter` | Send message |
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| `Shift+Enter` | New line in input |
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| `Escape` | Cancel generation |
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## Technology Stack
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| Layer | Technology |
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|-------|------------|
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| Framework | Vue 3 + TypeScript |
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| UI Components | PrimeVue 4 (Aura theme) |
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| Styling | CSS Variables + PrimeVue tokens |
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| Streaming | Server-Sent Events (SSE) |
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| State | Vue Composition API |
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## Links
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- [GitHub Repository](https://github.com/agentic-forge/forge-ui)
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- [Orchestrator Docs](/docs/orchestrator) — Backend API that forge-ui connects to
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- [Armory Docs](/docs/armory) — Tool gateway that provides MCP tools

‎docs/interfaces.md‎

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data: {}
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```
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## Python SDK
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For Python projects, direct import is the cleanest approach:
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```python
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from agentic_forge import Orchestrator, ModelRouter, ToolRouter
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# Setup
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orchestrator = Orchestrator(
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model_router=ModelRouter(...),
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tool_router=ToolRouter(...)
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)
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# Synchronous (simple scripts)
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result = orchestrator.run_sync("Search for AI news")
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print(result.content)
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# Async (applications)
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async def main():
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result = await orchestrator.run("Search for AI news")
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print(result.content)
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# Streaming
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async for event in orchestrator.run_stream("Search..."):
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if event.type == "token":
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print(event.token, end="", flush=True)
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elif event.type == "tool_call":
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print(f"\n[Calling {event.tool_name}...]")
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# With hooks for observability
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@orchestrator.on("tool_calls")
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def log_tools(data):
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print(f"Tools: {[c.name for c in data['calls']]}")
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```
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## CLI Interface
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### Commands
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| GET | /api/v1/tools | List all available tools |
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| GET | /api/v1/health | Health check |
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## Non-Python Integration
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For projects not written in Python, use the REST + SSE API:
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```javascript
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// JavaScript/TypeScript
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const baseUrl = 'http://localhost:8001';
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// Create conversation and send message
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const conv = await fetch(`${baseUrl}/conversations`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ system_prompt: 'You are a helpful assistant' })
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}).then(r => r.json());
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await fetch(`${baseUrl}/conversations/${conv.id}/messages`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ content: 'Search for AI news' })
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});
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// Listen to SSE stream
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const eventSource = new EventSource(`${baseUrl}/conversations/${conv.id}/stream`);
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eventSource.addEventListener('token', (e) => {
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const data = JSON.parse(e.data);
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process.stdout.write(data.token);
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});
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eventSource.addEventListener('tool_call', (e) => {
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const data = JSON.parse(e.data);
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console.log(`\n[${data.name}: ${data.status}]`);
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});
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eventSource.addEventListener('complete', () => {
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console.log('\nDone!');
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eventSource.close();
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});
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```
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## Summary
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| Interface | Use Case | Protocol |

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