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👋 OpenManus

Manus is incredible, but OpenManus can achieve any idea without an Invite Code 🛫!

Our team members @Xinbin Liang and @Jinyu Xiang (core authors), along with @Zhaoyang Yu, @Jiayi Zhang, and @Sirui Hong, we are from @MetaGPT. The prototype is launched within 3 hours and we are keeping building!

It's a simple implementation, so we welcome any suggestions, contributions, and feedback!

Enjoy your own agent with OpenManus!

We're also excited to introduce OpenManus-RL, an open-source project dedicated to reinforcement learning (RL)- based (such as GRPO) tuning methods for LLM agents, developed collaboratively by researchers from UIUC and OpenManus.

Project Demo

seo_website.mp4

Installation

We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.

Method 1: Using conda

1. Create a new conda environment:

conda create -n open_manus python=3.12
conda activate open_manus

2. Clone the repository:

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

3. Install dependencies:

pip install -r requirements.txt

Method 2: Using uv (Recommended)

1. Install uv (A fast Python package installer and resolver):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

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

3. Create a new virtual environment and activate it:

uv venv --python 3.12
source .venv/bin/activate  # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate

4. Install dependencies:

uv pip install -r requirements.txt

Browser Automation Tool (Optional)

playwright install

Configuration

OpenManus requires configuration for the LLM APIs it uses. Follow these steps to set up your configuration:

1. Create a config.toml file in the config directory (you can copy from the example):

cp config/config.example.toml config/config.toml

2. Edit config/config.toml to add your API keys and customize settings:

# Global LLM configuration
[llm]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."  # Replace with your actual API key
max_tokens = 4096
temperature = 0.0

# Optional configuration for specific LLM models
[llm.vision]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."  # Replace with your actual API key

Quick Start

One line for run OpenManus:

python main.py

Then input your idea via terminal!

For MCP tool version, you can run:

python run_mcp.py

For unstable multi-agent version, you also can run:

python run_flow.py

Custom Adding Multiple Agents

Currently, besides the general OpenManus Agent, we have also integrated the DataAnalysis Agent, which is suitable for data analysis and data visualization tasks. You can add this agent to run_flow in config.toml.

# Optional configuration for run-flow
[runflow]
use_data_analysis_agent = true     # Disabled by default, change to true to activate

In addition, you need to install the relevant dependencies to ensure the agent runs properly: Detailed Installation Guide

Technical Architecture

For developers and system architects who want to understand the technical details of OpenManus:

📚 Comprehensive Architecture Documentation

Key documentation includes:

• System Architecture - Complete technical overview with Mermaid diagrams
• Agent Framework - Agent system design and development guide
• Tool System - Tool architecture and custom tool development
• Flow Management - Multi-agent coordination and workflow patterns

Quick Architecture Overview

graph TB
    subgraph "User Interface"
        CLI[Command Line Interface]
    end
    
    subgraph "Application Layer"
        MAIN[main.py - Single Agent]
        FLOW[run_flow.py - Multi-Agent]
        MCP[run_mcp.py - MCP Server]
    end
    
    subgraph "Agent Layer"
        MANUS[Manus Agent]
        SWE[SWE Agent]
        BROWSER[Browser Agent]
        DATA[Data Analysis Agent]
    end
    
    subgraph "Tool Layer"
        PYTHON[Python Execute]
        FILES[File Operations]
        WEB[Web & Browser Tools]
        CHARTS[Data Visualization]
        MCP_TOOLS[MCP Tools]
    end
    
    subgraph "Infrastructure"
        LLM[LLM Providers]
        CONFIG[Configuration]
        SANDBOX[Sandbox Environment]
    end
    
    CLI --> MAIN
    CLI --> FLOW
    CLI --> MCP
    
    MAIN --> MANUS
    FLOW --> MANUS
    FLOW --> SWE
    FLOW --> BROWSER
    FLOW --> DATA
    
    MANUS --> PYTHON
    MANUS --> FILES
    MANUS --> WEB
    MANUS --> CHARTS
    MANUS --> MCP_TOOLS
    
    PYTHON --> SANDBOX
    FILES --> CONFIG
    WEB --> LLM
    CHARTS --> LLM
    MCP_TOOLS --> LLM

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How to contribute

We welcome any friendly suggestions and helpful contributions! Just create issues or submit pull requests.

Or contact @mannaandpoem via 📧email: mannaandpoem@gmail.com

Note: Before submitting a pull request, please use the pre-commit tool to check your changes. Run pre-commit run --all-files to execute the checks.

Community Group

Join our networking group on Feishu and share your experience with other developers!

Star History

Sponsors

Thanks to PPIO for computing source support.

PPIO: The most affordable and easily-integrated MaaS and GPU cloud solution.

Acknowledgement

Thanks to anthropic-computer-use and browser-use for providing basic support for this project!

Additionally, we are grateful to AAAJ, MetaGPT, OpenHands and SWE-agent.

We also thank stepfun(阶跃星辰) for supporting our Hugging Face demo space.

OpenManus is built by contributors from MetaGPT. Huge thanks to this agent community!

Cite

@misc{openmanus2025,
  author = {Xinbin Liang and Jinyu Xiang and Zhaoyang Yu and Jiayi Zhang and Sirui Hong and Sheng Fan and Xiao Tang},
  title = {OpenManus: An open-source framework for building general AI agents},
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.15186407},
  url = {https://doi.org/10.5281/zenodo.15186407},
}