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

Our team members @mannaandpoem @XiangJinyu @MoshiQAQ @didiforgithub from @MetaGPT built it within 3 hours!

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

Enjoy your own agent with OpenManus!

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/mannaandpoem/OpenManus.git cd OpenManus

3. Install dependencies:

pip install -r requirements.txt

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

One line for run OpenManus:

python main.py

Then input your idea via terminal!

For unstable version, you also can run:

python run_flow.py

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

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

After comprehensively gathering feedback from community members, we have decided to adopt a 3-4 day iteration cycle to gradually implement the highly anticipated features.

• Enhance Planning capabilities, optimize task breakdown and execution logic
• Introduce standardized evaluation metrics (based on GAIA and TAU-Bench) for continuous performance assessment and optimization
• Expand model adaptation and optimize low-cost application scenarios
• Implement containerized deployment to simplify installation and usage workflows
• Enrich example libraries with more practical cases, including analysis of both successful and failed examples
• Frontend/backend development to improve user experience
• RAG enhancement: Implement external knowledge graph retrieval and fusion mechanisms

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

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

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