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layout default
title MetaGPT Tutorial
nav_order 194
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format_version v2

MetaGPT Tutorial: Multi-Agent Software Development with Role-Based Collaboration

MetaGPTView Repo is a multi-agent framework where GPT-powered agents assume real-world software roles -- Product Manager, Architect, Engineer, and QA -- to collaboratively build complete software from a single one-line requirement. It encodes Standardized Operating Procedures (SOPs) into agent prompts, enabling structured, role-based collaboration that mirrors how professional development teams actually work.

In one sentence: Give MetaGPT a product idea, and a virtual software company of AI agents designs, architects, codes, and tests it for you.

Stars License: MIT Python

Why This Track Matters

MetaGPT introduces a paradigm where multiple AI agents collaborate through structured roles and standardized processes, closely mirroring the way real software teams operate. This approach is directly relevant to Genesis-style agent teams and any system that requires coordinated, multi-step AI workflows.

This track focuses on:

  • Understanding role-based multi-agent collaboration where each agent has a defined responsibility
  • Learning how Standardized Operating Procedures (SOPs) constrain and guide agent behavior
  • Building custom actions and tools that extend agent capabilities
  • Designing production-ready multi-agent pipelines with memory, context sharing, and cost optimization

Current Snapshot (auto-updated)

Mental Model

flowchart TD
    A["One-Line Requirement"] --> B["ProductManager Agent"]
    B --> C["PRD Document"]
    C --> D["Architect Agent"]
    D --> E["System Design & API Specs"]
    E --> F["Engineer Agent"]
    F --> G["Code Implementation"]
    G --> H["QA Agent"]
    H --> I["Test Cases & Bug Reports"]
    I -->|feedback| F

    B -.->|SOP| S1["Competitive Analysis → PRD"]
    D -.->|SOP| S2["Design Review → Tech Spec"]
    F -.->|SOP| S3["Code Review → Implementation"]
    H -.->|SOP| S4["Test Plan → Verification"]

    J["Shared Memory / Message Bus"] --- B
    J --- D
    J --- F
    J --- H

    classDef input fill:#e1f5fe,stroke:#01579b
    classDef agent fill:#f3e5f5,stroke:#4a148c
    classDef artifact fill:#fff3e0,stroke:#ef6c00
    classDef infra fill:#e8f5e8,stroke:#1b5e20

    class A input
    class B,D,F,H agent
    class C,E,G,I artifact
    class J,S1,S2,S3,S4 infra
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Chapter Guide

Welcome to your journey through multi-agent software development! This tutorial explores how MetaGPT orchestrates AI agents into a functioning software team.

  1. Chapter 1: Getting Started - Installation, configuration, and your first multi-agent software run
  2. Chapter 2: Agent Roles - ProductManager, Architect, Engineer, and QA roles in depth
  3. Chapter 3: SOPs and Workflows - Standardized Operating Procedures and role collaboration patterns
  4. Chapter 4: Action System - Actions, action nodes, and building custom actions
  5. Chapter 5: Memory and Context - Memory management and context sharing between agents
  6. Chapter 6: Tool Integration - Web browsing, code execution, and custom tool creation
  7. Chapter 7: Multi-Agent Orchestration - Team composition, task decomposition, and parallel execution
  8. Chapter 8: Production Deployment - Configuration, cost optimization, and enterprise patterns

What You Will Learn

By the end of this tutorial, you will be able to:

  • Run a full software generation pipeline from a single requirement using MetaGPT's built-in roles
  • Understand the SOP-driven architecture that constrains agents into productive workflows
  • Create custom agent roles with specialized actions and behaviors
  • Build custom actions and action nodes for domain-specific tasks
  • Manage shared memory and context across multi-agent conversations
  • Integrate external tools including web search, code execution, and APIs
  • Orchestrate complex multi-agent teams with hierarchical and parallel execution
  • Deploy MetaGPT in production with cost controls, caching, and monitoring

Prerequisites

  • Python 3.9+ (3.10+ recommended)
  • Basic understanding of LLM concepts and API usage
  • Familiarity with async/await patterns in Python
  • An OpenAI API key (or compatible LLM provider key)

Source References

Related Tutorials

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Full Chapter Map

  1. Chapter 1: Getting Started
  2. Chapter 2: Agent Roles
  3. Chapter 3: SOPs and Workflows
  4. Chapter 4: Action System
  5. Chapter 5: Memory and Context
  6. Chapter 6: Tool Integration
  7. Chapter 7: Multi-Agent Orchestration
  8. Chapter 8: Production Deployment