MetaGPT
by Community
π The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
OSS
MetaGPT
Added 1 June 2026
Overview
MetaGPT is a Python framework that orchestrates multiple AI agents to collaborate on software development tasks. It translates natural language requirements into structured workflows, with agents assuming roles like product manager, architect, and engineer to produce code and documentation.
Best for
Best for
Developers building proof-of-concepts or prototypes who want AI agents to handle multiple development stages in parallel.
Use cases
- Generate complete software projects from text specifications
- Automate multi-stage development workflows with role-based agents
- Prototype applications without writing initial boilerplate
Notes
MetaGPT is a Python framework that orchestrates multiple AI agents to collaborate on software development tasks. It translates natural language requirements into structured workflows, with agents assuming roles like product manager, architect, and engineer to produce code and documentation.
68,466 stars on GitHub. Last updated 2026-01-21. Licensed MIT.
Use cases
- Generate complete software projects from text specifications
- Automate multi-stage development workflows with role-based agents
- Prototype applications without writing initial boilerplate
Pros
- Handles end-to-end software generation from requirements to code
- Well-established community project with 68k+ GitHub stars
- Structured agent collaboration reduces hallucination through role assignment
Cons
- Output quality depends heavily on prompt clarity and LLM capability
- Requires significant computational resources for multi-agent orchestration
- Generated code often needs review and refinement before production use
Indexed from awesome-langchain and enriched against its public facts.
Pros
- Handles end-to-end software generation from requirements to code
- Well-established community project with 68k+ GitHub stars
- Structured agent collaboration reduces hallucination through role assignment
Cons
- Output quality depends heavily on prompt clarity and LLM capability
- Requires significant computational resources for multi-agent orchestration
- Generated code often needs review and refinement before production use
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