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MetaGPT

Multi-agent framework for turning specs into software outputs

MetaGPT assigns different roles to LLMs to simulate a software team. It can turn a short requirement into artifacts like user stories, requirements, APIs, and code repositories.

Chrome Extension
Code Execution
File Access
Multi-Agent
B2B
For Developers
For Teams
Open Source
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About

What It Is

MetaGPT is an open-source multi-agent framework for building software-oriented AI workflows. According to its README, it organizes LLMs into roles such as product manager, architect, project manager, and engineer so they can work together on complex tasks using defined SOPs.

It is aimed primarily at developers and researchers building agentic systems, especially for software generation and related workflows. You can install it with pip, run it from the CLI, or use it as a Python library. The project also documents configuration through ~/.metagpt/config2.yaml and supports multiple LLM backends including OpenAI, Azure, Ollama, and Groq.

What to Know

MetaGPT is more of a framework than a finished end-user app. It looks strongest for developers who want to experiment with multi-agent orchestration, code generation, and structured task decomposition. The documentation shows it can generate a repo from a prompt and includes examples like a data interpreter, researcher, and debate workflow, but it is not positioned as a general consumer assistant.

Because it depends on external model providers or locally configured models, setup is not completely plug-and-play. The README does not provide public pricing details, and privacy or enterprise governance details are not clearly described in the crawled content. If you want a turnkey SaaS agent or a non-technical tool, this is probably not the right fit.

Key Features
Assigns LLMs to software-team roles such as product manager, architect, PM, and engineer
Generates software artifacts from a one-line requirement
Creates a project repository structure from a prompt
Runs from the command line via the `metagpt` CLI
Can be used as a Python library
Use Cases
Generating a starter codebase from a natural-language product requirement
Prototyping multi-agent software workflows for research or internal tools
Running scripted data-analysis tasks through the Data Interpreter example
Agenticness: Guided Assistant 💬

Executes tasks you assign, one step at a time, within narrow domains.

High evidence
Last evaluated: Mar 28, 2026

Dimension Breakdown

Action Capability
Autonomy
Adaptation
State & Memory
Safety

Categories

Pricing
  • Open source: MIT-licensed project
  • Pricing: Not publicly available for any hosted or paid offering mentioned in the crawled content
Details
AddedMarch 26, 2026
RefreshedMarch 28, 2026
Quick Facts
DeploymentSelf-hosted
AutonomySemi-autonomous
Model supportMulti-model
Open sourceYes
Team supportIndividual only
Pricing modelFree / open source
Interfacecli, api

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