Slackbot MCP
About
A Slackbot LLM agent that allows connecting to an LLM and MCP servers
Explore
- Slack integration with Events API
- LLM-powered responses
- MCP tool integration
- Multi-bot management
- Thread-aware conversations
- Channel-specific configurations
- Python 3.9+
- Poetry for dependency management
- Docker and Docker Compose
- PostgreSQL
- Redis
1. Clone the repository:
git clone <repository-url>
cd slackbot-mcp
2. Install dependencies:
poetry install
3. Set up pre-commit hooks:
poetry run pre-commit install
4. Copy the example environment file:
cp .env.example .env
5. Start the development environment:
docker-compose up -d
6. Run migrations:
poetry run alembic upgrade head
7. Start the development server:
poetry run uvicorn src.slackbot.api.main:app --reload
A powerful Slack bot powered by LLM with MCP integration capabilities.
Features
- Slack integration with Events API
- LLM-powered responses
- MCP tool integration
- Multi-bot management
- Thread-aware conversations
- Channel-specific configurations
Prerequisites
- Python 3.9+
- Poetry for dependency management
- Docker and Docker Compose
- PostgreSQL
- Redis
Development Setup
1. Clone the repository:
git clone <repository-url>
cd slackbot-mcp
2. Install dependencies:
poetry install
3. Set up pre-commit hooks:
poetry run pre-commit install
4. Copy the example environment file:
cp .env.example .env
5. Start the development environment:
docker-compose up -d
6. Run migrations:
poetry run alembic upgrade head
7. Start the development server:
poetry run uvicorn src.slackbot.api.main:app --reload
Project Structure
slackbot-mcp/
├── src/
│ └── slackbot/
│ ├── api/ # FastAPI application and endpoints
│ ├── core/ # Core business logic
│ ├── models/ # SQLAlchemy models
│ ├── services/ # External service integrations
│ └── utils/ # Utility functions
├── tests/
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
├── scripts/ # Utility scripts
├── alembic/ # Database migrations
└── docker/ # Docker configuration files
Contributing
1. Create a new branch for your feature
2. Make your changes
3. Run tests: poetry run pytest
4. Submit a pull request
License
[Your chosen license]
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