config.example.toml

# RustFox Configuration

[telegram]
# Get your bot token from @BotFather on Telegram
bot_token = "YOUR_TELEGRAM_BOT_TOKEN"
# Only these Telegram user IDs can interact with the bot
# Find your user ID by messaging @userinfobot on Telegram
allowed_user_ids = [123456789]

[openrouter]
# Get your API key from https://openrouter.ai/keys
api_key = "YOUR_OPENROUTER_API_KEY"
# Model to use (see https://openrouter.ai/models)
model = "moonshotai/kimi-k2.5"
# API base URL (usually no need to change)
base_url = "https://openrouter.ai/api/v1"
# Maximum tokens in response
max_tokens = 4096
# System prompt for the AI assistant
system_prompt = """You are a helpful AI assistant with access to tools. \
Use the available tools to help the user with their tasks. \
When using file or terminal tools, operate only within the allowed sandbox directory. \
Be concise and helpful."""

[sandbox]
# The directory where file operations and command execution are allowed
# The bot cannot access files outside this directory
allowed_directory = "/tmp/rustfox-sandbox"

[memory]
# Path to the SQLite database file for persistent memory
# Stores conversations, knowledge base, and vector embeddings
database_path = "rustfox.db"

# Query rewriting for memory search (optional; default: false)
# When enabled, ambiguous follow-up questions are rewritten into self-contained
# search queries using an LLM call before the RAG vector search.
# This improves recall accuracy but adds one extra LLM round-trip per message.
# Can also be toggled per-user at runtime via the /query-rewrite Telegram command.
# query_rewriter_enabled = false

[skills]
# Directory containing skill markdown files
# Skills are natural-language instructions loaded at startup
directory = "skills"

[general]
# Your location, injected into the system prompt so the AI knows your timezone/region
# Uncomment and set to your city/region (e.g. "Tokyo, Japan")
# location = "Tokyo, Japan"

# Agent loop (optional; defaults apply if section omitted)
# [agent]
# max_iterations = 25   # Agent loop cap (default 25)

# LangSmith observability (optional)
# Traces every LLM call and tool execution for debugging in the LangSmith UI.
# Get your API key at https://smith.langchain.com → Settings → API Keys
# [langsmith]
# api_key = "ls__..."
# project = "rustfox"   # LangSmith project name (default: "default")

# Embedding API for vector search (optional)
# When configured, enables hybrid vector + FTS5 search for memory.
# Without this, falls back to FTS5 keyword search only.
# Works with any OpenAI-compatible /v1/embeddings endpoint.
# [embedding]
# api_key = "YOUR_OPENROUTER_API_KEY"
# base_url = "https://openrouter.ai/api/v1"
# model = "qwen/qwen3-embedding-8b"
# dimensions = 1536

# MCP Server Configurations
# Each [[mcp_servers]] block defines an MCP server to connect to
# The bot will discover and register tools from each server

# Example: Git MCP server (requires uvx/npx)
# [[mcp_servers]]
# name = "git"
# command = "uvx"
# args = ["mcp-server-git"]

# Example: Filesystem MCP server
# [[mcp_servers]]
# name = "filesystem"
# command = "npx"
# args = ["-y", "@modelcontextprotocol/server-filesystem", "/tmp/rustfox-sandbox"]

# Example: Google Workspace MCP server (Gmail, Calendar, Drive, Docs, Sheets, Slides)
# Note: use --from because the package name differs from the executable name
#
# OAuth setup:
#   1. https://console.cloud.google.com/apis/credentials
#      → Create/select project → enable Drive, Gmail, Calendar, Docs, Sheets, Slides APIs
#   2. OAuth consent screen → External → add your email as a test user
#   3. Credentials → Create OAuth Client ID → Web application
#      → add Authorised redirect URI: https://developers.google.com/oauthplayground
#      → save your Client ID and Client Secret
#   4. Open https://developers.google.com/oauthplayground
#      → gear icon → check "Use your own OAuth credentials" → enter Client ID + Secret
#      → add scopes: https://www.googleapis.com/auth/drive
#                    https://www.googleapis.com/auth/gmail.modify
#                    https://www.googleapis.com/auth/calendar
#                    https://www.googleapis.com/auth/documents
#                    https://www.googleapis.com/auth/spreadsheets
#                    https://www.googleapis.com/auth/presentations
#      → "Authorize APIs" → sign in → "Exchange authorization code for tokens"
#      → copy the Refresh token value
#
# [[mcp_servers]]
# name = "google-workspace"
# command = "uvx"
# args = ["--from", "google-workspace-mcp", "google-workspace-worker"]
# [mcp_servers.env]
# GOOGLE_WORKSPACE_CLIENT_ID     = "your-client-id.apps.googleusercontent.com"
# GOOGLE_WORKSPACE_CLIENT_SECRET = "your-client-secret"
# GOOGLE_WORKSPACE_REFRESH_TOKEN = "your-refresh-token"  # from step 4 above
# GOOGLE_WORKSPACE_ENABLED_CAPABILITIES = '["drive","docs","gmail","calendar","sheets","slides"]'

# Example: Meta Threads MCP server (publish posts, read replies, analytics)
#
# Token setup:
#   1. Go to https://developers.facebook.com/apps → Create App → Business type
#      → add the "Threads API" product from the dashboard
#   2. Under Threads API → Permissions → request:
#         threads_basic, threads_content_publish,
#         threads_manage_replies, threads_read_replies
#      Add your Threads account as a Test User under App Roles → Roles
#   3. Under Threads API → Access Tokens → Generate Token for your test user
#      → copy the long-lived token (valid ~60 days; regenerate before expiry)
#
# [[mcp_servers]]
# name = "threads"
# command = "npx"
# args = ["-y", "threads-mcp-server"]
# [mcp_servers.env]
# THREADS_ACCESS_TOKEN = "your-long-lived-access-token"

# Example: Web search MCP server with environment variables
# [[mcp_servers]]
# name = "brave-search"
# command = "npx"
# args = ["-y", "@brave/brave-search-mcp-server"]
# [mcp_servers.env]
# BRAVE_API_KEY = "your-brave-api-key"

# ── HTTP-based MCP Servers (Streamable HTTP transport) ────────────────────────
# These servers are reached over HTTPS and do not require a local command.
# Use `url` instead of `command`; optionally set `auth_token` for Bearer auth.

# Example: Notion MCP server (official HTTP MCP — no Node.js required)
# Docs: https://developers.notion.com/guides/mcp/mcp
#
# Recommended: use the setup wizard (cargo run --bin setup) to obtain an OAuth
# access token via the built-in OAuth 2.0 flow.  The wizard also stores the
# refresh token and expiry so the bot can automatically renew the connection.
#
# The bot refreshes the access token automatically when it is within 5 minutes
# of expiry and writes the new credentials back to this file.  Manual setup:
#   1. Create a Notion integration at https://www.notion.so/my-integrations
#      and note your client_id and client_secret.
#   2. Complete the OAuth flow to obtain an access_token and refresh_token.
#   3. Fill in the fields below.
#
# [[mcp_servers]]
# name = "notion"
# url = "https://mcp.notion.com/mcp"
# auth_token = "your-notion-oauth-access-token"
# refresh_token = "your-notion-oauth-refresh-token"
# token_expires_at = 1234567890          # Unix timestamp; set by the wizard
# token_endpoint = "https://api.notion.com/v1/oauth/token"
# oauth_client_id = "your-notion-client-id"
# oauth_client_secret = "your-notion-client-secret"  # omit for public clients

# Example: Exa AI web search (https://mcp.exa.ai)
# Get your API key at https://dashboard.exa.ai/api-keys
#
# Option A — Bearer token (recommended; keeps the key out of URLs and logs)
# [[mcp_servers]]
# name = "exa"
# url = "https://mcp.exa.ai/mcp"
# auth_token = "your-exa-api-key"
#
# Option B — Inline API key in URL (simpler; key will appear in logs)
# [[mcp_servers]]
# name = "exa"
# url = "https://mcp.exa.ai/mcp?exaApiKey=your-exa-api-key"

# ── Self-Learning (optional; defaults apply if section omitted) ─────────────
# [learning]
# user_model_path = "memory/USER.md"          # Honcho-style user model file
# skill_extraction_enabled = true              # Auto-generate skills from tool-heavy tasks
# skill_extraction_threshold = 5              # Min tool calls to trigger extraction
# user_model_update_interval = 10             # Update user model every N user messages
# user_model_cron = "0 0 3 * * SUN"           # Weekly user model refresh (6-field cron)