English | [中文](README_zh.md) | [한국어](README_ko.md) | [日本語](README_ja.md)
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[](https://discord.gg/DYn29wFk9z)
[](https://huggingface.co/spaces/lyh-917/OpenManusDemo)
[](https://doi.org/10.5281/zenodo.15186407)
# 👋 OpenManus
Manus is incredible, but OpenManus can achieve any idea without an *Invite Code* 🛫!
Our team members [@Xinbing Liang](https://github.com/mannaandpoem) and [@Jinyu Xiang](https://github.com/XiangJinyu) (core authors), along with [@Zhaoyang Yu](https://github.com/MoshiQAQ), [@Jiayi Zhang](https://github.com/didiforgithub), and [@Sirui Hong](https://github.com/stellaHSR), we are from [@MetaGPT](https://github.com/geekan/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](https://github.com/OpenManus/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
## 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:
```bash
conda create -n open_manus python=3.12
conda activate open_manus
```
2. Clone the repository:
```bash
git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
### Method 2: Using uv (Recommended)
1. Install uv (A fast Python package installer and resolver):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Clone the repository:
```bash
git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
```
3. Create a new virtual environment and activate it:
```bash
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate
```
4. Install dependencies:
```bash
uv pip install -r requirements.txt
```
### Browser automation
OpenManus starts Browser Use CLI 3.0 as a default MCP server:
```bash
uvx browser-use --cli-mcp
```
`uvx` keeps Browser Use and its fast-moving dependencies isolated from the
OpenManus environment. The agent receives the canonical Browser Use skill and
the native `browser_exec` and `browser_screenshot` tools.
Local mode attaches to Chrome or Chromium automatically and needs no API key.
For diagnostics or to install Chromium, run:
```bash
uvx browser-use --doctor
uvx browser-use install
```
For an isolated Browser Use Cloud browser, authenticate before starting
OpenManus. The agent can then start and select a named remote browser:
```bash
export BROWSER_USE_API_KEY="bu_..."
```
Existing browsers can be selected with `BU_CDP_URL`, `BU_CDP_WS`, or `BU_NAME`.
Set `OPENMANUS_DISABLE_BROWSER_USE=1` to disable the default Browser Use MCP
server.
BrowserGym still requires its Playwright browser:
```bash
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):
```bash
cp config/config.example.toml config/config.toml
```
2. Edit `config/config.toml` to add your API keys and customize settings:
```toml
# 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:
```bash
python main.py
```
Then input your idea via terminal!
For MCP tool version, you can run:
```bash
python run_mcp.py
```
For unstable multi-agent version, you also can run:
```bash
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`.
```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](app/tool/chart_visualization/README.md##Installation)
## 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
[](https://star-history.com/#FoundationAgents/OpenManus&Date)
## Sponsors
Thanks to [PPIO](https://ppinfra.com/user/register?invited_by=OCPKCN&utm_source=github_openmanus&utm_medium=github_readme&utm_campaign=link) for computing source support.
> PPIO: The most affordable and easily-integrated MaaS and GPU cloud solution.
## Acknowledgement
Thanks to [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo), [browser-use](https://github.com/browser-use/browser-use) and [crawl4ai](https://github.com/unclecode/crawl4ai) for providing basic support for this project!
Additionally, we are grateful to [AAAJ](https://github.com/metauto-ai/agent-as-a-judge), [MetaGPT](https://github.com/geekan/MetaGPT), [OpenHands](https://github.com/All-Hands-AI/OpenHands) and [SWE-agent](https://github.com/SWE-agent/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
```bibtex
@misc{openmanus2025,
author = {Xinbing 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},
}
```
