Open Manus

Open Source

General Questions

What is Open Manus?

Open Manus is an open-source project on GitHub that aims to provide a framework for building general AI agents. Its motto, "No fortress, purely open ground," signifies its commitment to an accessible and collaborative development environment for AI agents.

What kind of AI Agents can I build with Open Manus?

Open Manus is designed for building general AI agents. It currently integrates a Data Analysis Agent suitable for data analysis and visualization tasks, and the project is continuously evolving to support more types of agents.

What are Foundation Models in the context of Open Manus?

While the project description doesn't explicitly detail its use of "Foundation Models," the name "FoundationAgents/OpenManus" suggests that Open Manus aims to leverage or build upon foundational AI models to create its agents. This typically refers to large, pre-trained models that can be adapted for various tasks.

Is Open Manus open source?

Yes, Open Manus is completely open source. It is hosted on GitHub under the MIT License, encouraging contributions and community involvement.

Installation and Configuration

How can I install Open Manus?

Open Manus offers two installation methods: using conda or uv. The uv method is recommended for faster installation and better dependency management. Detailed instructions are available in the project's README on GitHub.

How do I configure Open Manus with my LLM API keys?

You need to create a config.toml file in the config directory (you can copy from config.example.toml) and then edit it to add your API keys and customize settings for your Large Language Models (LLMs).

Contribution and Community

How can I contribute to Open Manus?

The Open Manus team welcomes suggestions and contributions. You can contribute by creating issues, submitting pull requests on GitHub, or contacting them via email at [email protected]. Remember to use the pre-commit tool before submitting pull requests.

Where can I find the Open Manus community?

You can join their networking group on Feishu to connect with other developers and share your experiences.

How can I contact Open Manus for support?

For support and contributions, you can contact the Open Manus team via email at [email protected].

Where can I find the Open Manus GitHub Link?

You can find the Open Manus project on GitHub at: https://github.com/FoundationAgents/OpenManus

Product Features of Open Manus

Overview

Open Manus is an open-source project designed to be a flexible and accessible framework for building general AI agents. It emphasizes an "open ground" philosophy, aiming to remove barriers like invite codes, making advanced AI agent development available to everyone. Developed by core members from MetaGPT, Open Manus provides a simple yet powerful implementation for creating and deploying AI agents, with a strong focus on community contributions and continuous improvement.

Main Purpose and Target User Group

The main purpose of Open Manus is to empower developers, researchers, and AI enthusiasts to easily build, customize, and deploy their own AI agents without restrictions. It aims to democratize access to AI agent development, fostering innovation and collaboration within the open-source community.

Target User Groups

• AI Developers: Individuals and teams looking for an open-source framework to build and experiment with AI agents.
• Researchers: Academics and scientists interested in exploring and contributing to the field of AI agent development, particularly in areas like reinforcement learning for LLM agents (e.g., OpenManus-RL).
• Students and Enthusiasts: Learners who want to understand and get hands-on experience with AI agent creation.
• Startups and Small Teams: Organizations seeking an affordable and flexible solution for integrating AI agents into their products or workflows.

Function Details and Operations

• General AI Agent Framework: Provides a foundational structure for developing various types of AI agents.
• Multi-Agent Support: Capable of running multiple agents, including a dedicated Data Analysis Agent for data processing and visualization tasks.
• LLM API Integration: Configurable to work with various Large Language Model (LLM) APIs (e.g., OpenAI's GPT models) by setting API keys, base URLs, and model parameters.
• Browser Automation Tool Integration: Supports playwright for browser automation, enabling agents to interact with web environments.
• Flexible Execution Modes:
  • main.py: For running the general Open Manus agent with user input via the terminal.
  • run_mcp.py: For a "MCP tool version" (specific functionality not detailed, but implies a distinct operational mode).
  • run_flow.py: For an "unstable multi-agent version," allowing for more complex multi-agent interactions.
• Configuration Management: Uses config.toml for easy customization of LLM settings, API keys, and agent activation (e.g., enabling the Data Analysis Agent).
• Dependency Management: Offers two installation methods:
  • Conda: Traditional Python environment management.
  • uv (Recommended): A fast Python package installer and resolver for quicker installation and better dependency handling.
• Pre-commit Hooks: Integrates pre-commit for code quality checks, ensuring consistent formatting and style before pull requests.

User Benefits

• Accessibility: "No fortress, purely open ground" philosophy removes barriers, making AI agent development accessible to everyone without invite codes.
• Flexibility & Customization: Users can easily configure LLM models, integrate various tools, and add custom agents to suit their specific needs.
• Rapid Prototyping: The project was prototyped within 3 hours, indicating its ease of setup and use for quick development cycles.
• Community-Driven Development: Welcomes suggestions, contributions, and feedback, fostering a collaborative environment for continuous improvement.
• Cost-Effective: Being open-source, it reduces development costs associated with proprietary AI agent solutions.
• Learning Opportunity: Provides a practical platform for learning about AI agent architecture and implementation.
• Reinforcement Learning Integration: Future plans include OpenManus-RL for advanced tuning of LLM agents using RL methods.

Compatibility and Integration

• Python 3.12: Recommended Python version for environment setup.
• LLM APIs: Designed to integrate with various LLM providers, with explicit configuration for OpenAI models (GPT-4o).
• Browser Automation: Compatible with playwright for web interaction capabilities.
• Dependency Management Tools: Supports conda and uv for managing project dependencies.
• Version Control: Hosted on GitHub, leveraging standard Git workflows for collaboration.
• Operating Systems: Installation instructions are provided for both Unix/macOS and Windows environments.

Customer Feedback and Case Studies

• Project Demo: A seo_website.mp4 video is linked, showcasing the project in action.
• Community Engagement: Encourages users to join their Feishu networking group to share experiences.
• Acknowledgements: Thanks to projects like anthropic-computer-use, browser-use, AAAJ, MetaGPT, OpenHands, and SWE-agent for foundational support, indicating a reliance on and integration with established open-source tools and communities.
• Sponsorship: PPIO provides computing source support, suggesting real-world application and resource needs.
• Hugging Face Demo Space: Supported by stepfun (阶跃星辰), indicating potential for broader deployment and accessibility of demos.

Access and Activation Method

• GitHub Repository: The primary access point is the public GitHub repository: https://github.com/FoundationAgents/OpenManus.
• Cloning the Repository: Users can clone the repository using git clone https://github.com/FoundationAgents/OpenManus.git.
• Installation:
  • Method 1 (Conda): Create a conda environment, activate it, and install dependencies via pip install -r requirements.txt.
  • Method 2 (uv - Recommended): Install uv, create and activate a virtual environment, then install dependencies via uv pip install -r requirements.txt.
• Configuration: Copy config/config.example.toml to config/config.toml and edit it to add LLM API keys and customize settings.
• Running the Agent: Execute python main.py for the general agent, python run_mcp.py for the MCP tool version, or python run_flow.py for the multi-agent version.
• Contribution: Users can contribute by creating issues or submitting pull requests after running pre-commit run --all-files.