OpenManus

Overview

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.

Features

• 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.

Prerequisites

Before you begin, ensure you have the following installed:

• Docker (version 20.10 or higher)
• Docker Compose (version 1.29 or higher)
• Node.js (version 20.18 or higher, for local development)
• Python (version 3.9 or higher, for local development)
• Git (for cloning and contributing)

Getting Started

1. Clone the Repository

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

2. Build and Run with Docker

# Build and start all containers
docker-compose up --build

This will launch:

• Backend container with the multi-agent system and integrated tools
• Frontend container serving the Next.js web interface
• FastAPI server for task delegation and execution

3. Test the System

Once running, you can interact with OpenManus via:

• CLI: Use the provided Python client (python client.py)
• API: Send requests to http://localhost:8000 (see API docs below)
• Web UI: Access http://localhost:3000

Example CLI command:

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

Project Structure

OpenManus/
├── docker/               # Docker configurations
│   ├── frontend/        # Next.js frontend container
│   │   └── Dockerfile   # Frontend container configuration
│   └── unified/         # Backend container configuration
│       ├── Dockerfile   # Backend container configuration
│       └── start.sh     # Container startup script
├── src/                 # Source code
│   ├── agents/          # Multi-agent logic (Python)
│   │   ├── nodes/       # Agent node implementations
│   │   ├── browser_agent.py
│   │   ├── coder_agent.py
│   │   ├── coordinator.py
│   │   ├── reporter_agent.py
│   │   └── research_agent.py
│   ├── components/      # React components
│   ├── config/          # Configuration files
│   ├── graph/           # Graph-based workflow
│   ├── llms/            # LLM integrations
│   ├── pages/           # Next.js pages
│   ├── prompts/         # Agent prompts
│   ├── service/         # Backend services
│   ├── tools/           # Tool implementations
│   ├── utils/           # Utility functions
│   ├── workflow/        # Workflow management
│   ├── client.py        # CLI client for testing
│   └── server.py        # FastAPI server
├── docs/                # Documentation and API specs
├── package.json         # Next.js frontend dependencies
├── next.config.js       # Next.js configuration
├── docker-compose.yml   # Docker Compose configuration
└── README.md           # This file

Configuration

Edit the docker-compose.yml file to customize:

services:
  backend:
    build: 
      context: .
      dockerfile: docker/unified/Dockerfile
    ports:
      - "8000:8000"  # FastAPI port
    environment:
      - WEB_BROWSER_API_KEY=your_key_here
    volumes:
      - ./src:/app/src
      - ./data:/app/data

  frontend:
    build:
      context: .
      dockerfile: docker/frontend/Dockerfile
    ports:
      - "3000:3000"  # Web UI port
    depends_on:
      - backend

API Documentation

The agent server exposes a REST API at http://localhost:8000. 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.

Contributing

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.

Roadmap

• 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.

Inspiration

OpenManus is inspired by:

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

License

This project is licensed under the UNLICENSE. See LICENSE for details.

Contact

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!