
MetaGPT
A framework where several LLM agents play software-company roles to build software from one sentence.
- Stars
- 71.1k
- Forks
- 9.1k
- Licence
- MIT
- Language
- Python
- Last update
- 10 months ago
- Type
- Framework
What it is
MetaGPT is a Python framework that assigns different roles, such as product manager, architect, project manager, and engineer, to LLM-based agents. You give it a one-line requirement and the agents work through a software-company style process to produce artifacts like user stories, requirements, data structures, APIs, and code. It is used by people who want to experiment with multi-agent collaboration on complex tasks.
- Generate a small project from a one-line request, such as the README's example of creating a 2048 game
- Run a data analysis task with the Data Interpreter component on a dataset and get a plot
- Build your own agents using the agent and multi-agent tutorials in the docs
- Explore the example use cases listed in the docs, such as Debate, Researcher, and Receipt Assistant
Great for
- Developers experimenting with multi-agent LLM workflows
- Researchers interested in agent collaboration using standard operating procedures
- Students exploring how role-based AI teams are built
- People who want a starting-point project to generate small software prototypes
Not ideal for
- Users who need reliable, production-ready code without reviewing output, since the README does not make such guarantees
- People without an LLM API key or local model setup, since configuration is required before use
- Users who want a no-code, point-and-click tool; the README centers on CLI and Python usage
How to get it
- 1
Install with a package manager
You'll need: Python 3.9 or later, but less than 3.12 · Node.js · pnpm · An LLM API key or compatible local model endpoint
Install from PyPI
pip install --upgrade metagptCreate initial config file
metagpt --init-config - 2
Run it
- Install Python 3.9 to 3.11, Node.js, and pnpm
- Run pip install --upgrade metagpt
- Run metagpt --init-config and edit ~/.metagpt/config2.yaml with your LLM settings
- Run metagpt "Create a 2048 game" and check the generated repo in ./workspace
- 3
Get the most out of it
- Use the Data Interpreter for data analysis tasks, as shown in the README example
- Follow the Agent 101 and MultiAgent 101 tutorials to build custom roles
- Point the config file at other providers such as Azure, Ollama, or Groq, as the README notes
- Check the examples directory for research work such as SPO and AOT referenced in the news section
Downloads
Strengths
- Clear, documented multi-role design built around a software-company process
- Supports several LLM providers through a single YAML configuration file
- Open source under the MIT license with an active project and a large star count
Trade-offs
- Output quality depends on the underlying LLM and API costs are paid by the user
- The README requires a specific Python version range and Node.js with pnpm, which adds setup steps
- Documentation is spread across an external site, so some details are not in the README itself
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| MetaGPT (this) | 71.1k | Framework | intermediate | 10 months ago | Developers experimenting with multi-agent LLM workflows |
| crewAI | 60.4k | Library | intermediate | 4 days ago | Python developers prototyping multi-agent workflows |
| OpenHands | 92.5k | App | intermediate | 4 days ago | Developers who want coding agents running on a server while their laptop is off |
| deer-flow | 84.9k | AI model / agent | advanced | 4 days ago | Developers who want to experiment with multi-agent workflows on their own infrastructure |
| botpress | 15k | Dev tool | intermediate | 6 days ago | Developers who want to write integrations and bots as TypeScript code using the Botpress CLI and SDK |
| deep-research | 19.9k | AI model / agent | intermediate | 7 months ago | Developers who want a readable example of a deep research agent to learn from or extend |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.