OpenManus is an open-source project aimed at replicating the capabilities of the Manus AI agent, a groundbreaking general-purpose AI developed by Monica. Manus is known for its ability to autonomously execute complex tasks—ranging from personalized travel planning to stock analysis—surpassing models like GPT-4 on the GAIA benchmark. OpenManus seeks to bring these capabilities to the open-source community using a modular, containerized framework built with Docker, Python, and JavaScript.

This repository provides a starting point for developers and researchers to build, deploy, and experiment with a multi-agent AI system. Our goal is to create a flexible and extensible platform that mirrors Manus's autonomous task execution while fostering community contributions.

• Multi-Agent System: Collaborative AI agents working together to solve complex tasks.
• Dockerized Environment: Easy setup and deployment with containerization.
• Task Execution: Supports tasks like travel planning, data analysis, and content generation.
• Tool Integration: Web browsing, code execution, and data retrieval capabilities.
• Modular Design: Easily extendable with new agents, tools, or features.
• Community-Driven: Open to contributions and enhancements.

Before you begin, ensure you have the following installed:

• Docker (version 20.10 or higher)
• Docker Compose (version 1.29 or higher)
• Python (3.9 or higher)
• Node.js (16.x or higher)
• Git (for cloning and contributing)

git clone https://github.com/henryalps/OpenManus.git
cd OpenManus

# Build the Docker images
docker-compose build

# Start the services
docker-compose up

This will launch the core components:

• Agent Server: Handles task delegation and execution.
• Tool Layer: Provides web browsing, code execution, and data processing tools.
• Frontend: A basic interface for interacting with the agent (optional).

Once running, you can interact with OpenManus via:

• CLI: Use the provided Python client (python client.py).
• API: Send requests to http://localhost:5000 (see API docs below).
• Web UI: Access http://localhost:3000 (if enabled).

Example CLI command:

python client.py --task "Plan a 3-day trip to Tokyo"

OpenManus/
├── docker/               # Docker configurations and images
│   ├── agent/            # Dockerfile for the agent server
│   ├── tools/            # Dockerfile for tool integrations
│   └── frontend/         # Dockerfile for the web UI
├── src/                  # Source code
│   ├── agents/           # Multi-agent logic (Python)
│   ├── tools/            # Tool implementations (Python/JavaScript)
│   ├── client.py         # CLI client for testing
│   └── server.py         # Main API server
├── docs/                 # Documentation and API specs
├── docker-compose.yml    # Docker Compose configuration
└── README.md             # This file

Edit the docker-compose.yml file to customize:

• Ports (e.g., 5000 for API, 3000 for UI).
• Environment variables (e.g., API keys for external tools).
• Volume mounts for persistent data.

Example:

services:
  agent:
    build: ./docker/agent
    ports:
      - "5000:5000"
    environment:
      - WEB_BROWSER_API_KEY=your_key_here

The agent server exposes a REST API at http://localhost:5000. Key endpoints:

POST /task: Submit a task for execution.

Body: { "task": "Analyze Tesla stock trends" }
Response: { "status": "success", "result": "..." }

GET /status: Check system health.

Response: { "status": "running" }

Full API docs are available in docs/api.md.

We welcome contributions! To get started:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m "Add your feature").
4. Push to your branch (git push origin feature/your-feature).
5. Open a Pull Request.

Please read CONTRIBUTING.md for guidelines.

• Implement core multi-agent coordination.
• Add support for GAIA benchmark tasks.
• Integrate advanced NLP models (e.g., LLaMA, Grok).
• Enhance toolset with real-time web scraping and visualization.
• Release v1.0 with stable task execution.

OpenManus is inspired by:

• The official Manus project (manus.im).
• The open-Manus community effort (GitHub).
• GAIA benchmark for general AI assistants (arXiv).

This project is licensed under the MIT License. See LICENSE for details.

For questions or collaboration, reach out via GitHub Issues or email henryalps@gmail.com.

Happy coding! Let's build the future of AI agents together!