ChatDev
by Community
ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration
Agents
ChatDev
Added 10 July 2026
Overview
ChatDev is an open-source framework that simulates a software development company using multiple LLM agents. Agents collaborate in roles like CEO, CTO, and programmer to build complete applications from natural language specifications. All communication and code generation are orchestrated through Python scripts.
Best for
Best for
Developers exploring multi-agent AI collaboration for rapid software prototyping
Use cases
- Generating a full-stack web app from a description
- Automating code review and debugging in a multi-agent workflow
- Prototyping software projects with minimal manual coding
Notes
ChatDev is an open-source framework that simulates a software development company using multiple LLM agents. Agents collaborate in roles like CEO, CTO, and programmer to build complete applications from natural language specifications. All communication and code generation are orchestrated through Python scripts.
33,701 stars on GitHub. Last updated 2026-06-29. Licensed Apache-2.0.
Use cases
- Generating a full-stack web app from a description
- Automating code review and debugging in a multi-agent workflow
- Prototyping software projects with minimal manual coding
Pros
- Modular agent architecture that can be customized for different workflows
- Strong community and high popularity (33k GitHub stars) indicating active maintenance
- Open-source and self-hosted, giving full control over LLM usage and costs
Cons
- Requires users to supply and manage their own LLM API keys, increasing operational complexity
- Generated code may need significant human refinement for production use
- Limited to small-to-medium projects due to coordination overhead among agents
Indexed from awesome-ai-agents and enriched against its public facts.
Pros
- Modular agent architecture that can be customized for different workflows
- Strong community and high popularity (33k GitHub stars) indicating active maintenance
- Open-source and self-hosted, giving full control over LLM usage and costs
Cons
- Requires users to supply and manage their own LLM API keys, increasing operational complexity
- Generated code may need significant human refinement for production use
- Limited to small-to-medium projects due to coordination overhead among agents
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