README.md
English | δΈζ | νκ΅μ΄ | ζ₯ζ¬θͺ
Manus is incredible, but OpenManus can achieve any idea without an Invite Code π«!
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.
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We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
conda create -n open_manus python=3.12
conda activate open_manus
git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
pip install -r requirements.txt
curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate
uv pip install -r requirements.txt
playwright install
OpenManus requires configuration for the LLM APIs it uses. Follow these steps to set up your configuration:
config.toml file in the config directory (you can copy from the example):cp config/config.example.toml config/config.toml
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
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
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
We welcome any friendly suggestions and helpful contributions! Just create issues or submit pull requests.
Or contact @mannaandpoem via π§email: [email protected]
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.
Join our networking group on Feishu and share your experience with other developers!
<div align="center" style="display: flex; gap: 20px;"> </div>Thanks to PPIO for computing source support.
PPIO: The most affordable and easily-integrated MaaS and GPU cloud solution.
Thanks to anthropic-computer-use, browser-use and crawl4ai for providing basic support for this project!
Additionally, we are grateful to AAAJ, MetaGPT, OpenHands and SWE-agent.
We also thank stepfun(ιΆθ·ζθΎ°) for supporting our Hugging Face demo space.
OpenManus is built by contributors from MetaGPT. Huge thanks to this agent community!
@misc{openmanus2025,
author = {Xinbin Liang and Jinyu Xiang and Zhaoyang Yu and Jiayi Zhang and Sirui Hong and Sheng Fan and Xiao Tang and Bang Liu and Yuyu Luo and Chenglin Wu},
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},
}