Webex Bot - AI Assistant

A conversational AI bot for Webex that:

• 🤖 Responds to user mentions with AI-generated answers
• 💬 Maintains conversation context within threads
• 🔧 Integrates with MCP (Model Context Protocol) servers for extended capabilities
• 🌐 Supports multiple LLM providers via LiteLLM (OpenAI, Google Gemini, Ollama, OpenRouter, Anthropic, etc.)

Features

• Thread-aware conversations: The bot remembers context within Webex threads, allowing natural follow-up questions
• Rolling thread summarization: Background tasks summarize older messages as threads grow, preserving context indefinitely without hitting context limits
• Room-wide history search: Uses SQLite FTS5 keyword search to look up past messages, solutions, and context across Webex space history
• Smart mention handling: The bot recognizes its own name and doesn't confuse it with questions
• Multiple LLM providers: Use OpenAI, Google Gemini, Ollama (local/cloud), OpenRouter, Anthropic, or any LiteLLM-supported provider
• MCP Integration: Connect to multiple MCP servers via HTTP for extended tool capabilities
• Access control: Restrict bot to approved users, domains, or rooms
• Clean code: Follows Python best practices with ruff linting and formatting

Quick Start

Prerequisites

• Python 3.11+
• UV package manager
• Webex bot token (create one here)
• API key for your LLM provider (e.g., OpenAI, Gemini)

Installation

Option 1: Install from PyPI

Run the package directly from PyPI using UVX:

uvx webex-bot-ai

Option 2: Local Development Setup

For development or running from source:

1. Install dependencies:

git clone https://github.com/mhajder/webex-bot-ai.git
cd webex-bot-ai
uv sync

Option 3: Run with Docker (Persistent Storage)

Run in a container with database volume persistence:

docker run -d \
  --name webex-bot-ai \
  --env-file .env \
  -v webex_bot_data:/app/data \
  ghcr.io/mhajder/webex-bot-ai:latest

Configuration

1. Configure environment:

cp .env.example .env
# Edit .env with your configuration

2. Set required variables in .env:

WEBEX_ACCESS_TOKEN=your_webex_bot_token
OPENAI_API_KEY=your_openai_api_key

3. Run the bot:

webex-bot-ai

Configuration

Bot Settings

LLM Settings

Conversation Settings & Rolling Summarization

Model Examples

# OpenAI
LLM_MODEL=gpt-4o-mini
OPENAI_API_KEY=sk-...

# Google Gemini
LLM_MODEL=gemini/gemini-2.5-flash
GEMINI_API_KEY=AIzaSy...

# Ollama (local)
LLM_MODEL=ollama_chat/gpt-oss:120b
LLM_API_BASE=http://localhost:11434

# OpenRouter
LLM_MODEL=openrouter/meta-llama/llama-3.1-70b-instruct
OPENROUTER_API_KEY=sk-or-...

# Anthropic
LLM_MODEL=claude-3-sonnet-20240229
ANTHROPIC_API_KEY=sk-ant-...

Access Control

# Restrict to specific users
WEBEX_APPROVED_USERS=user1@example.com,user2@example.com

# Restrict to specific email domains
WEBEX_APPROVED_DOMAINS=example.com

# Restrict to specific rooms
WEBEX_APPROVED_ROOMS=room_id_1,room_id_2

MCP Integration

Connect to MCP HTTP transport servers for extended tool capabilities:

MCP_ENABLED=true
MCP_REQUEST_TIMEOUT=30

# Single server
MCP_SERVERS=[{"name": "my-server", "url": "http://localhost:8000/mcp", "enabled": true}]

# Multiple servers with auth
MCP_SERVERS=[
  {"name": "tools-server", "url": "http://localhost:8000/mcp", "enabled": true},
  {"name": "secure-server", "url": "https://api.example.com/mcp", "auth_token": "your-token", "enabled": true}
]

Sentry Error Tracking (Optional)

Enable error tracking and performance monitoring with Sentry:

# Install with Sentry support
uv sync --extra sentry

Configure Sentry via environment variables:

Example configuration:

# Enable Sentry error tracking
SENTRY_DSN=https://your-key@o12345.ingest.us.sentry.io/6789
SENTRY_ENVIRONMENT=production

Usage

1. Start a conversation: Mention the bot in a Webex space:

   @BotName What is AI?
   

2. Follow-up in thread: Reply in the same thread for context-aware responses:

   @BotName Tell me more.
   

3. The bot maintains context within the thread, so you can have natural conversations.

Development

Code Quality

# Lint code
uv run ruff check src/

# Format code
uv run ruff format src/

# Fix linting issues
uv run ruff check src/ --fix

Adding New Features

• Commands: Add new commands in src/commands/
• MCP Tools: Connect to MCP servers via configuration
• LLM Providers: Configure via LLM_MODEL using LiteLLM syntax

Dependencies

• webex_bot - Webex bot framework
• litellm - Universal LLM API
• fastmcp - MCP client/server framework
• pydantic-settings - Configuration management
• sentry-sdk - Error tracking (optional)

License

This project is licensed under the MIT License - see the LICENSE file for details.