MetaGPT is a multi-agent framework that assigns distinct roles to GPTs to form a collaborative software development team. It takes a single line of natural language requirements and outputs comprehensive project artifacts.
One-Minute Overview#
MetaGPT is a multi-agent framework that turns GPTs into a full software company. Instead of relying on a single prompt, it uses Standard Operating Procedures (SOPs) to coordinate different AI agents (e.g., Product Manager, Architect, Engineer) to collaborate on the entire software development lifecycle, from requirements to coding.
Core Value: By simulating human team collaboration via SOPs, it elevates LLM capabilities from "generating code snippets" to "building complete, maintainable software projects."
Quick Start#
Installation Difficulty: Moderate - Requires Python environment setup, API Key configuration, and Node.js for some features.
# 1. Install the package (Requires Python 3.9-3.11)
pip install --upgrade metagpt
# 2. Initialize configuration
metagpt --init-config
# 3. Edit ~/.metagpt/config2.yaml and add your OpenAI (or compatible) API Key
# 4. Run a simple command
metagpt "Create a 2048 game"
Is this suitable for me?
- ✅ Rapid Prototyping: Ideal for generating full project skeletons with docs and code.
- ✅ Automated Workflows: Perfect for building Agent teams to execute multi-step tasks.
- ✅ Research: Studying multi-agent systems and SOP implementation.
- ❌ Simple Code Completion: If you just need a few lines of code, a standard IDE plugin might be sufficient.
- ❌ No API Budget: Running a full "company" simulation consumes significant Tokens.
Core Capabilities#
1. Software Company Simulation - Solves Task Decomposition#
- MetaGPT defines roles like Product Manager (requirements), Architect (design), Engineer (coding), and Project Manager.
- Value: Through role division, it produces logically consistent and structured software projects rather than scattered code fragments.
2. SOP-Driven Development - Solves Output Consistency#
- The philosophy
Code = SOP(Team)ensures all agents follow strict workflows (PRD -> Design -> Code -> Test). - Value: Significantly reduces hallucinations and structural chaos, producing engineering-standard code.
3. Data Interpreter - Solves Complex Analysis Tasks#
- Includes a specialized Data Interpreter role capable of writing code for data analysis, plotting, and file manipulation.
- Value: Useful for scientific research or business reporting, automating the pipeline from data cleaning to visualization.
Tech Stack & Integration#
Languages: Python (3.9 - 3.11) Dependencies: OpenAI API (Azure/Ollama/Groq compatible), Node.js & pnpm Integration: Python Library / CLI
Maintenance Status#
- Development Activity: Very High. Recently accepted for ICLR 2025 (Oral) and launched the MGX product.
- Recent Updates: Actively evolving with the latest LLM advancements.
Commercial & Licensing#
License: MIT License
- ✅ Commercial Use: Allowed.
- ✅ Modification: Allowed.
- ⚠️ Restrictions: Standard MIT disclaimer applies.
Documentation & Resources#
- Quality: Comprehensive, covering installation to advanced multi-agent development.
- Official Docs: https://docs.deepwisdom.ai/
- Tutorials: Includes "Agent 101" and "MultiAgent 101" guides。