ChatDev

Virtual software company powered by multi-agent LLM collaboration for end-to-end app development.

4.5 (6)

Прегледано от Daniel Nikulshyn·Актуализирано май 2026 г.

Преглед

ChatDev is an open-source framework that simulates a virtual software company, where multiple AI agents take on roles like CEO, CTO, programmer, designer, and tester. These agents communicate through structured dialogues to plan, code, document, and review software based on a user's initial requirements. By organizing agents into a waterfall-style workflow with defined responsibilities, ChatDev demonstrates how large language models can coordinate complex, multi-step tasks. It is primarily aimed at researchers, developers, and educators exploring multi-agent systems, autonomous software engineering, and collaborative AI workflows.

Ключови функции

Role-based AI agents (CEO, CTO, coder, tester)
Waterfall-style development pipeline
Automated code, docs, and asset generation
Inter-agent dialogue and review process
Configurable workflows and prompts
Support for multiple LLM backends

Плюсове и минуси

Плюсове

Open-source and customizable
Demonstrates structured multi-agent collaboration
Covers full software lifecycle from design to testing
Useful for research and learning
Active community and ongoing development

Минуси

Output quality depends heavily on underlying LLM
Best suited for small or prototype projects
Requires technical setup and API costs
Generated code often needs human review

Отзиви

4.5

Средно от 6 оценки.

Влез, за да оставиш отзив.

Nadia Petrova

Compared a few options

Evaluated this against two competitors. Where it wins: configurable workflows and prompts and active community and ongoing development. On balance the feature set — especially automated code, docs, and asset generation — justifies the 5 stars for our use case.

George Papadakis

Does the job

Pretty happy overall. Support for multiple LLM backends just works and demonstrates structured multi-agent collaboration. Requires technical setup and API costs can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Grace Okafor

Solid for our team

We rolled this out across the team last quarter and covers full software lifecycle from design to testing. Inter-agent dialogue and review process fits neatly into how we already work, and configurable workflows and prompts removed a step we used to do by hand. but it has held up under daily use.

Joanna Kowalski

Use it every day

Honestly didn't expect to like it this much. Role-based AI agents (CEO, CTO, coder, tester) is exactly what I needed, and covers full software lifecycle from design to testing. I do wish best suited for small or prototype projects, but I reach for it almost every day now and it just clicks.

Robert Ainsworth

Does the job

Pretty happy overall. Configurable workflows and prompts just works and demonstrates structured multi-agent collaboration. Best suited for small or prototype projects can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Ingrid Bauer

Years in this space

I've evaluated a lot of these over the years. What stands out here is configurable workflows and prompts — handled better than most — and useful for research and learning. Output quality depends heavily on underlying LLM is my one real gripe. Worth the time if this is your use case.