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MetaGPT
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MetaGPT

Active·★ 70.8k·MIT·Updated 2026-01-21
★ Most Popular★ Trending★ Essential

MetaGPT is a Python multi-agent framework that models a software company: given a one-line requirement, a team of LLM agents acting as product manager, architect, project manager and engineer produces user stories, competitive analysis, requirements, data structures, API designs, documents and code. Its core idea is Code = SOP(Team), applying standard operating procedures to a team of agents.

It is best suited to researchers and developers exploring role-based multi-agent collaboration, and to anyone who wants to see a full idea-to-repository pipeline in action. Typical uses include generating a small project (for example `metagpt "Create a 2048 game"`), running the Data Interpreter for data analysis tasks, and studying the team's published research such as AFlow (an ICLR 2025 oral paper on automating agentic workflow generation). The team also runs a commercial product, MGX (MetaGPT X), built on these ideas.

Getting started: install with `pip install --upgrade metagpt` on Python 3.9 to 3.11, install Node.js and pnpm, then run `metagpt --init-config` and set your model provider and API key in `~/.metagpt/config2.yaml`. It works with OpenAI, Azure, Ollama, Groq and other providers.

#Multi-Agent System#Large Language Models#Software Development#AI Agent Framework#Automated Programming#Coding
$ Install
$ pip install --upgrade metagpt
↗ Visit site★ GitHub
01

Features

01Accepts single-line requirements and generates comprehensive software artifacts (e.g., user stories, APIs, documentation).
02Emulates a software company by assigning distinct roles (e.g., PM, Architect, Engineer) to LLM agents.
03Orchestrates collaborative processes using Standard Operating Procedures (SOPs) for complex task completion.
04Supports integration with various Large Language Models and APIs (e.g., OpenAI, Azure, Ollama, Groq).
05Enables advanced capabilities like data interpretation and automated workflow generation.
02

Why choose it

+A clear, opinionated model of multi-agent collaboration (roles plus SOPs) that is easy to understand and demonstrate.
+Produces a broad set of artefacts from one requirement, not just code: requirements, designs and documentation.
+Backed by active research output (for example AFlow, SPO and AOT), with code for these papers in the repository.
+MIT licensed and usable both as a CLI and as a Python library.
03

Trade-offs

!Development has slowed: the repository's last push was in January 2026. The team also runs a separate commercial product, MGX.
!Narrow Python support (3.9 to 3.11 per the README), plus a Node.js and pnpm requirement.
!The software-company pipeline shines on small, well-specified projects; generated code for real-world systems still needs substantial human review.
!Running a full team of agents consumes many model calls, so costs add up with stronger models.
04

Compatibility

OpenAI
Supported
Verified via docs
Azure OpenAI
Supported
Verified via docs
Ollama
Supported
Verified via docs
Groq
Supported
Verified via docs
05

Quick start

1
$ pip install --upgrade metagpt
06

Use cases

↳Data Interpreter
↳Debate
↳Researcher
07

How it compares

≈vs CrewAI: both use role-based agents, but CrewAI is a general-purpose orchestration framework with event-driven Flows for production automation, while MetaGPT ships a fixed software-company workflow.
≈vs Microsoft AutoGen: AutoGen offers lower-level conversation and orchestration primitives (and is now in maintenance mode); MetaGPT gives you a complete pipeline with predefined roles.
≈vs ChatDev: both simulate a software team; MetaGPT emphasises SOP-driven roles and structured documents alongside code.
08

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07How it compares08Alternatives
Stats
GitHub Stars★ 70.8k
Last commit8mo ago
StatusActive
LicenseMIT
CategoryMulti-Agent
Trend (30d)
+2.8k↑ 4.8%
Links
Documentation↗Discussion↗Issues↗Releases↗

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