MetaGPT

Overview

MetaGPT is a multi-agent software framework developed by FoundationAgents that simulates a full software development organization. With over 69,000 GitHub stars, it's one of the largest open-source multi-agent projects. The project's tagline—"Natural Language Driven Software Company"—captures its core premise: describe a product in natural language, and MetaGPT assigns specialized agent roles (product manager, architect, engineer, project manager) to produce code, documentation, and project artifacts.

Unlike simpler agent frameworks where a single agent handles everything, MetaGPT implements a role-based multi-agent system where each agent has a distinct responsibility and communicates through structured messages. This mirrors real-world software development teams.

Features

• Role-Based Multi-Agent System: Product Manager, Architect, Engineer, Project Manager, QA roles
• Natural Language Requirements: Describe products in plain language; agents handle the rest
• SOP-Driven Orchestration: Standard Operating Procedures define agent interaction patterns
• Full Development Pipeline: Requirement analysis → Design → Code generation → Testing → Documentation
• Observability: Track agent conversations, decisions, and outputs throughout the process
• Extensible Roles: Add custom agent roles for domain-specific workflows
• Python-Native: Built on LangChain primitives for easy integration

Installation

pip install metagpt

Quick Start

metagpt "Build a TODO list web application with Flask"

Or programmatically:

from metagpt import MetaGPT

team = MetaGPT(
    project="todo-flask-app",
    requirement="A web-based todo list with CRUD operations"
)
team.run()

Core Concepts

Roles

Each role is a specialized agent with its own system prompt, tools, and responsibilities. Roles collaborate via a message bus.

SOP (Standard Operating Procedure)

SOPs define the workflow sequence—e.g., PM writes PRD → Architect designs → Engineer codes → QA tests.

Artifacts

Each stage produces structured artifacts (PRD, design doc, code, test cases) that flow between agents.

Advanced Features

Custom Role Definition

from metagpt.roles import Role

class DataScientist(Role):
    def __init__(self):
        super().__init__(name="DataScientist", profile="Data Science Expert")
        self.add_action(DataAnalysis())

SOP Customization

from metagpt.software_impl import SoftwareImpl

class CustomSOP(SoftwareImpl):
    def __init__(self):
        super().__init__()
        self.add_step(Research())

Examples

• Full-Stack App Generation: Describe an e-commerce site → get full backend and frontend code
• API Service Creation: Generate REST API with documentation and tests
• Data Analysis Project: PM defines analysis scope → Data Scientist executes → Report produced

Pros

• ✅ Most comprehensive multi-agent software development framework
• ✅ Role-based design mirrors real-world teams
• ✅ End-to-end artifact generation
• ✅ Large community and extensive examples
• ✅ Extensible for custom workflows

Cons

• ❌ Heavy orchestration; overkill for simple tasks
• ❌ High token consumption for multi-agent conversations
• ❌ Output quality varies based on LLM capability
• ❌ Long execution times for complex projects

When to Use

Use MetaGPT when you need end-to-end software project generation from high-level requirements, or when building complex multi-agent systems with clear role separation. Best suited for exploratory development, prototyping, and educational purposes.

Resources

• Official Website
• GitHub Repository
• Documentation