ChatDev vs LangGraph
Side-by-side comparison of two AI agent tools
Short answer
- LangGraph is growing faster: +2,365 GitHub stars in the last 30 days vs +402 for ChatDev.
- Pick ChatDev for: chatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration. Pick LangGraph for: build resilient language agents as graphs.
From GitHub data refreshed daily.
ChatDevopen-source
ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration
LangGraphopen-source
Build resilient language agents as graphs.
Metrics
| ChatDev | LangGraph | |
|---|---|---|
| Stars | 34.4k | 42.7k |
| Star velocity /mo | 401.8421052631579 | 2.4k |
| Commits (90d) | 3 | 132 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | — | 43.7M |
| Overall score | 0.432443435811532 | 0.8091319530692536 |
Pros
- +Zero-code configuration makes multi-agent systems accessible to non-technical users
- +Proven track record with strong community adoption (31,000+ GitHub stars)
- +Versatile platform capable of handling diverse scenarios from software development to research automation
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
Cons
- -Recently transitioned from 1.0 to 2.0, potentially introducing stability concerns during the migration period
- -Limited technical documentation available for the new 2.0 platform features
- -May be overly complex for simple automation tasks that don't require multi-agent coordination
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
Use Cases
- •Automated software development with virtual teams of specialized AI agents (CEO, CTO, Programmer roles)
- •Complex research automation requiring coordination between multiple AI agents with different expertise
- •Data visualization and 3D generation projects that benefit from multi-agent workflow orchestration
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
FAQ
- Which is more popular, ChatDev or LangGraph?
- LangGraph has more GitHub stars (42,656 vs 34,437).
- Which is more actively developed, ChatDev or LangGraph?
- LangGraph had more commits in the last 90 days (132 vs 3).
- Should I use ChatDev or LangGraph?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.