This project is an ambitious endeavor to construct a modular, scalable, and fully functional autonomous agent system. It integrates a diverse array of cutting-edge Large Language Model (LLM) providers and state-of-the-art agentic frameworks, aiming to create a truly intelligent and adaptable AI entity.
- Multi-Agent Orchestration: Leverage frameworks like LangGraph, CrewAI, and AutoGen for complex, collaborative agent workflows.
- Model Agnostic LLM Integration: Seamlessly switch between various LLM providers including Kimi K2, Qwen, Mistral, Llama, DeepSeek, Minimax, and GLM (Zhipu AI) via LiteLLM.
- Advanced Memory Systems: Incorporate vector databases (Qdrant) for long-term knowledge retention and Retrieval-Augmented Generation (RAG).
- Extensible Tooling: Integrate with external tools for web browsing, code execution, and messaging platforms (e.g., OpenClaw).
- Scalable Deployment: Containerized deployment using Docker and Kubernetes for production readiness.
- Observability: Built-in monitoring capabilities for system health and agent performance.
super-agent/
├── agents/ # Agent definitions (CrewAI, LangGraph, AutoGen)
├── core/ # Unified LLM interface and base classes
├── memory/ # Vector DB and memory management
├── tools/ # Custom tools and integrations (OpenClaw, Browser)
├── api/ # FastAPI backend for external access
├── frontend/ # Dashboard for monitoring and interaction (Placeholder)
├── config/ # Configuration files and environment variables
├── deploy/ # Docker, K8s, and CI/CD files
├── tests/ # Unit and integration tests
└── docs/ # Project documentation
└── README.md # Project overview
This layer is responsible for coordinating the activities of various AI agents, enabling complex task decomposition, planning, and multi-agent collaboration. We utilize:
- LangGraph: For defining stateful, cyclic agent workflows, allowing for sophisticated decision-making processes.
- CrewAI: For creating role-based multi-agent systems, where each agent has a specific role, goal, and backstory, fostering collaborative problem-solving.
- AutoGen: For building conversational AI agents that can interact with each other to solve tasks, particularly useful for code-centric and research-oriented tasks.
- PydanticAI: Ensures type-safe agent definitions and structured outputs, enhancing reliability and maintainability.
To ensure flexibility and access to the best available models, this layer provides a unified interface to various LLM providers:
- LiteLLM: Acts as a universal API wrapper, standardizing calls to different LLMs such as Kimi K2 (Moonshot AI), Qwen (Alibaba Cloud), Mistral, Llama (via Groq or local deployment), DeepSeek, Minimax, and GLM (Zhipu AI). This abstraction allows for easy swapping of models and reduces vendor lock-in.
Effective memory is crucial for autonomous agents to learn and adapt. This layer includes:
- Vector Databases (Qdrant): For storing and retrieving high-dimensional embeddings of information, enabling Retrieval-Augmented Generation (RAG) and long-term knowledge retention.
- Semantic Memory: Stores and manages conversational context, user preferences, and learned facts, allowing agents to maintain coherent and personalized interactions.
- Episodic Memory: Records past actions, observations, and outcomes, facilitating self-reflection and continuous learning.
Agents gain their autonomy through the ability to interact with the external world. This layer provides a suite of tools and integrations:
- OpenClaw Integration: A placeholder for integrating with OpenClaw (formerly Clawdbot), enabling the agent to interact with users through various messaging applications.
- Browser Automation: Utilizes tools like Playwright or Selenium to enable agents to navigate, extract information from, and interact with web pages.
- Code Execution Sandbox: Provides a secure environment for agents to execute Python code, useful for data analysis, complex calculations, and interacting with local systems.
- Custom Tools: A framework for defining and integrating specialized tools tailored to specific tasks or domains, leveraging the tool ecosystems of LangChain and CrewAI.
Designed for robustness and scalability:
- Docker & Docker Compose: For packaging the agent system into portable containers, simplifying development and deployment.
- Kubernetes: For orchestrating containerized applications in production, ensuring high availability, scalability, and efficient resource utilization.
- Monitoring (Prometheus/Grafana): For real-time tracking of system performance, agent activity, and potential issues, crucial for maintaining a reliable autonomous system.
- Python 3.11+
- Docker
- Git
- GitHub CLI (for repository management)
- Clone the repository:
git clone https://github.com/your-username/super-intelligence-agent.git cd super-intelligence-agent - Set up environment variables:
Copy the example environment file and fill in your API keys:
cp .env.example .env # Open .env and add your LLM API keys and other configurations - Install Python dependencies:
pip install -r requirements.txt
- **Start Qdrant (Vector Database):
docker-compose up -d qdrant
- Run the FastAPI application:
Alternatively, use Docker Compose to run the entire stack:
uvicorn api.main:app --host 0.0.0.0 --port 8000
docker-compose up -d
The agent system exposes a FastAPI endpoint for interaction:
- GET /: Checks the API status.
- POST /chat: Sends a query to the agent system.
- Request Body Example:
{ "query": "What is the capital of France?", "agent_type": "crewai", "model": "mistral/mistral-large-latest" } agent_typecan becrewaiorlanggraph.modelis optional and defaults togpt-4oif not specified.
- Request Body Example:
We welcome contributions to this project! Please see CONTRIBUTING.md for details on how to get started.
This project is licensed under the MIT License - see the LICENSE file for details.
[1] CrewAI Documentation: https://docs.crewai.com/ [2] LangGraph Documentation: https://langchain-ai.github.io/langgraph/ [3] LiteLLM Documentation: https://litellm.ai/ [4] Qdrant Documentation: https://qdrant.tech/ [5] OpenClaw GitHub: https://github.com/OpenClaw/OpenClaw