OpenManus

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Open-source Python framework for building general-purpose AI agents

Product preview

OpenManus

What it is

OpenManus is an open-source project for building general-purpose AI agents, started in March 2025 by contributors from the MetaGPT team as an open alternative to invite-only agent products. It is written in Python and is configured with a TOML file where you add the model, endpoint and API key of the LLM you want to use. Agents can automate a browser, connect to external tools through the Model Context Protocol, and optionally run a data analysis agent. An experimental multi-agent flow coordinates several agents on one task. It is free under the MIT license and runs locally or in Docker.

Key features

General agent you can run locally
Browser automation through Browser Use
MCP tool integration
Experimental multi-agent flow

What we liked

+ Free and MIT licensed
+ Works with your choice of LLM provider
+ Very active open-source community

Worth noting

· Requires Python setup and your own API keys
· Multi-agent mode is still marked unstable

Editor's notes

More context from AI BirdView

OpenManus grew out of a simple goal: give developers a general-purpose agent they can run and modify themselves, without waiting for an invite. The project quickly became one of the most watched agent repositories on GitHub, and it remains a practical starting point for anyone who wants to see how a tool-using agent is put together, from planning steps to calling tools and reading results.

Clear, readable Python code that is easy to study and extend
Bring your own model through an OpenAI-style configuration
Built-in browser control and MCP support for connecting tools
Optional data analysis agent for charts and quick reports
Docker support for keeping experiments isolated

It is best for developers, researchers and tinkerers who want a hackable agent framework rather than a polished consumer app. A practical tip: run it inside Docker or a separate virtual environment with a spending limit on your API key, and start with small, well-defined tasks so you can watch how the agent plans before trusting it with longer jobs.