# MetaGPT — Multi-Agent Framework That Simulates a Software Company

> Multi-agent collaboration framework where LLM agents take on roles like product manager, architect, and engineer to build software from a single requirement.

## Install

Save in your project root:

# MetaGPT — Multi-Agent Framework That Simulates a Software Company

## Quick Use
```bash
pip install metagpt
metagpt "Create a CLI tool that converts CSV to JSON"
```

## Introduction
MetaGPT assigns distinct roles to LLM agents — product manager, architect, project manager, and engineer — and coordinates them through standardized operating procedures. Given a one-line requirement, the agents collaborate to produce PRDs, system designs, and working code.

## What MetaGPT Does
- Decomposes a natural-language requirement into structured outputs across multiple agent roles
- Generates product requirement documents, system design diagrams, and task breakdowns
- Produces runnable code with tests by following a simulated software development workflow
- Supports human-in-the-loop review at each stage of the pipeline
- Provides a plugin system for custom tools and actions per agent role

## Architecture Overview
MetaGPT implements a message-passing architecture where each agent subscribes to specific message types. A shared environment holds project state, and a standardized operating procedure (SOP) defines the order in which roles act. Agents use structured output schemas to pass artifacts like PRDs and API specs, reducing hallucination through format constraints.

## Self-Hosting & Configuration
- Install via pip or clone the repository for development setup
- Configure LLM providers in config2.yaml with API keys for OpenAI, Claude, or local models
- Set workspace directory and output preferences through environment variables
- Run as a CLI tool or import as a Python library for integration into larger pipelines
- Extend with custom roles by subclassing the Role base class and defining actions

## Key Features
- Role-based agent collaboration following real software engineering workflows
- Structured intermediate artifacts reduce cascading errors between agents
- Multi-model support including OpenAI, Anthropic, and open-source LLMs
- Incremental development mode that builds on existing codebases
- Data interpreter agent for data analysis and visualization tasks

## Comparison with Similar Tools
- **CrewAI** — simpler role definition with less structured inter-agent communication; MetaGPT enforces SOPs
- **AutoGen** — focuses on conversational agent patterns; MetaGPT models a full development pipeline
- **ChatDev** — similar software company simulation; MetaGPT adds structured document artifacts
- **LangGraph** — graph-based agent orchestration; MetaGPT provides pre-built software development roles
- **OpenAI Swarm** — lightweight agent handoffs; MetaGPT offers deeper role specialization

## FAQ
**Q: What LLM providers does MetaGPT support?**
A: OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and any OpenAI-compatible local model server.

**Q: Can MetaGPT work on existing codebases?**
A: Yes. The incremental development mode lets agents analyze and extend existing projects.

**Q: How is MetaGPT different from a single-agent coding tool?**
A: It separates concerns across specialized roles, producing intermediate design documents that guide implementation and reduce errors.

**Q: Does it require GPT-4?**
A: GPT-4 or Claude produce the best results, but it works with GPT-3.5 and open-source models at reduced quality.

## Sources
- https://github.com/FoundationAgents/MetaGPT
- https://docs.deepwisdom.ai/main/en/

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Source: https://tokrepo.com/en/workflows/2bbb11a6-3e8b-11f1-9bc6-00163e2b0d79
Author: AI Open Source