What is MetaGPT?

Hand MetaGPT a one-line requirement and a team of LLM roles works through it in turn, leaving a requirements document, a system design and source files in a folder under `workspace/`. Which roles show up depends on where you installed from, and the gap is not cosmetic: the published 0.8.2 package hires a product manager, an architect, a project manager and an engineer, while current main — the README's clone-and-install route — hires a team leader, a product manager, an architect, an engineer and a data analyst, with the project manager commented out. Under the demo sits a general framework: you subclass Role and Action, declare which upstream actions each role watches, and a shared Environment routes the messages that put the team in order. A separate agent, the Data Interpreter, does data work alone, planning the steps, writing Python and retrying what fails. The catch is scope — MetaGPT's own FAQ says functions start being left unimplemented past roughly 500 lines of generated code.

What can you do with MetaGPT?

One sentence in, a project folder out — metagpt "Create a 2048 game" fills a folder under workspace/ with the requirements document, the system design and the source files, and generate_repo() does the same from Python. Turning the design diagrams into pictures is a separate step that hands the diagram text to mmdc, installed with npm install -g @mermaid-js/mermaid-cli; without it you get a logged warning and the diagram source rather than a failed run, and mermaid.engine can be switched to playwright, pyppeteer, ink or none.
Roles, actions, and who watches whom — You subclass Role and Action, then call _watch to name the upstream actions a role reacts to. Every role observes a shared Environment and publishes its result back into it, so the order of work falls out of those subscriptions rather than from a graph you draw yourself.
Cap the spend, then pick the run back up — Team.invest(3.0) — or --investment, which defaults to $3 — sets a token budget that stops the run with an error once the accumulated cost reaches it, and --n-round (default 5) separately bounds the rounds of message passing. If the run then fails or you interrupt it, the whole team is written to disk and --recover-path picks it up from there.
The Data Interpreter plans, writes and runs Python — Ask for an analysis with a plot and it breaks the work into a plan, writes code for each step and executes it in a live Jupyter kernel, so variables survive from one step into the next. A step that raises is rewritten and rerun up to three times with the error text put back in front of the model. None of the software-company roles need to be around it.
Registering your own tool takes three steps, not one — @register_tool() on a function or class under metagpt/tools/libs turns its Google-style docstring into the schema the model reads. That alone does nothing: DataInterpreter.tools defaults to empty and the recommender that picks between tools is only built when it is non-empty, so you also import the module and construct DataInterpreter(tools=["magic_function"]).
The research the team publishes ships in the repo — examples/aflow searches a space of workflows represented as code with a Monte Carlo tree search, generating the agent workflow itself instead of you writing it; examples/spo rewrites prompts with no ground-truth labels; examples/sela applies the same tree search to automated machine learning. Each runs on its own, with no software company involved.

Before you choose MetaGPT

PyPI still serves 0.8.2 from March 2025, so the newer roles exist only on main, which has taken three commits since July 2025 and where a reported hang that stalls the engineer on Linux was still open in August 2026.Reported in#2110
Incremental mode expects a project MetaGPT itself created, and the guide puts requirements about architecture files or how one function should behave out of scope, so it is not a route into an existing repository.

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Frequently asked questions

Is MetaGPT free for commercial use?

MetaGPT is released under the MIT licence — OSI-approved open source, which permits commercial use.

How can MetaGPT be deployed?

MetaGPT is available as Self-hosted / Runs locally.

Documentation

Reproduced from the FoundationAgents/MetaGPT README, published under MIT. Read the original ↗

MetaGPT: The Multi-Agent Framework

News

🚀 Mar. 10, 2025: 🎉 mgx.dev is the #1 Product of the Week on @ProductHunt! 🏆

🚀 Mar. 4, 2025: 🎉 mgx.dev is the #1 Product of the Day on @ProductHunt! 🏆

🚀 Feb. 19, 2025: Today we are officially launching our natural language programming product: MGX (MetaGPT X) - the world’s first AI agent development team. More details on Twitter.

🚀 Feb. 17, 2025: We introduced two papers: SPO and AOT, check the code!

🚀 Jan. 22, 2025: Our paper AFlow: Automating Agentic Workflow Generation accepted for oral presentation (top 1.8%) at ICLR 2025, ranking #2 in the LLM-based Agent category.

👉👉 Earlier news

Software Company as Multi-Agent System

MetaGPT takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.
Internally, MetaGPT includes product managers / architects / project managers / engineers. It provides the entire process of a software company along with carefully orchestrated SOPs.
1. Code = SOP(Team) is the core philosophy. We materialize SOP and apply it to teams composed of LLMs.

A software company consists of LLM-based roles

Get Started

Installation

Ensure that Python 3.9 or later, but less than 3.12, is installed on your system. You can check this by using: python --version.
You can use conda like this: conda create -n metagpt python=3.9 && conda activate metagpt

pip install --upgrade metagpt
# or `pip install --upgrade git+https://github.com/geekan/MetaGPT.git`
# or `git clone https://github.com/geekan/MetaGPT && cd MetaGPT && pip install --upgrade -e .`

Install node and pnpm before actual use.

For detailed installation guidance, please refer to cli_install or docker_install

Configuration

You can init the config of MetaGPT by running the following command, or manually create ~/.metagpt/config2.yaml file:

# Check https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html for more details
metagpt --init-config  # it will create ~/.metagpt/config2.yaml, just modify it to your needs

You can configure ~/.metagpt/config2.yaml according to the example and doc:

llm:
  api_type: "openai"  # or azure / ollama / groq etc. Check LLMType for more options
  model: "gpt-4-turbo"  # or gpt-3.5-turbo
  base_url: "https://api.openai.com/v1"  # or forward url / other llm url
  api_key: "YOUR_API_KEY"

Usage

After installation, you can use MetaGPT at CLI

metagpt "Create a 2048 game"  # this will create a repo in ./workspace

or use it as library

from metagpt.software_company import generate_repo
from metagpt.utils.project_repo import ProjectRepo

repo: ProjectRepo = generate_repo("Create a 2048 game")  # or ProjectRepo("<path>")
print(repo)  # it will print the repo structure with files

You can also use Data Interpreter to write code:

import asyncio
from metagpt.roles.di.data_interpreter import DataInterpreter

async def main():
    di = DataInterpreter()
    await di.run("Run data analysis on sklearn Iris dataset, include a plot")

asyncio.run(main())  # or await main() in a jupyter notebook setting

QuickStart & Demo Video

https://github.com/user-attachments/assets/888cb169-78c3-4a42-9d62-9d90ed3928c9

Tutorial

🗒 Online Document
💻 Usage
🔎 What can MetaGPT do?
🛠 How to build your own agents?
- MetaGPT Usage & Development Guide | Agent 101
- MetaGPT Usage & Development Guide | MultiAgent 101
🧑‍💻 Contribution
- Develop Roadmap
🔖 Use Cases
❓ FAQs

Support

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Contributor form

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Contact Information

If you have any questions or feedback about this project, please feel free to contact us. We highly appreciate your suggestions!

Email: alexanderwu@deepwisdom.ai
GitHub Issues: For more technical inquiries, you can also create a new issue in our GitHub repository.

We will respond to all questions within 2-3 business days.