# OpenBMB/ChatDev

ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration

Repository: https://github.com/OpenBMB/ChatDev
Canonical: https://ross.abutalabs.com/products/chatdev
Homepage: https://arxiv.org/abs/2307.07924
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-07-24T08:01:27+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 63, longevity 78
- inputs: {"age_days": 1102, "days_push": 40, "days_rel": 164, "gap_med": 59, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 34126, forks 4266 (observed 2026-08-28T04:11:58.703317+00:00)

## What it is
ChatDev 2.0 (DevAll) is a zero-code multi-agent orchestration platform where users configure agents, workflows, and tasks to build LLM-powered collaborative systems without coding. Its legacy version (ChatDev 1.0) simulated a virtual software company with role-playing agents automating the software development life cycle.

## Use cases
- orchestrate multiple LLM agents to complete complex tasks
- build multi-agent workflows without writing code
- automate software development with AI agents acting as CEO, CTO, and programmers
- run deep research or data visualization pipelines via agent collaboration
- experiment with communicative agent collaboration paradigms from research
- generate 3D content or reports through configured agent teams

## When to choose
- you want a configurable, zero-code platform for multi-agent LLM systems
- you're researching multi-agent collaboration and orchestration strategies
- you want to automate end-to-end software development with role-playing agents
- you need an open-source, well-cited academic agent framework

## When to avoid
- you need a production-grade single-agent chatbot with simple API integration
- you require fine-grained programmatic control over agent internals rather than configuration
- your project needs minimal LLM token costs, since multi-agent pipelines are token-intensive
- you need a framework with broad third-party plugin ecosystem support

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, llm-inference, workflow-automation, prompt-engineering
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: python, cross-platform
- tags: multi-agent, llm-agents, zero-code, orchestration, software-development-agents, chatdev, ai-agents, automation, docker

## Member repositories
- OpenBMB/ChatDev (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:58.703317+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:27:51.315308+00:00, confidence not recorded.
  - readme: https://github.com/OpenBMB/ChatDev (fetched 2026-08-28T04:11:58.703317+00:00, sha 977351cb74c4)
  - homepage: https://arxiv.org/abs/2307.07924 (fetched 2026-08-29T07:48:28.004453+00:00, sha dd84327f6761)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T07:48:28.007206+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T07:48:28.010511+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T07:48:28.012288+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T07:48:28.008852+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.