LangChain vs LangGraph
Side-by-side comparison of two AI agent tools
Short answer
- LangChain is growing faster: +23,217 GitHub stars in the last 30 days vs +2,370 for LangGraph.
- Pick LangChain for: the agent engineering platform. Pick LangGraph for: build resilient language agents as graphs.
From GitHub data refreshed daily.
LangChainopen-source
The agent engineering platform
LangGraphopen-source
Build resilient language agents as graphs.
Metrics
| LangChain | LangGraph | |
|---|---|---|
| Stars | 147.4k | 42.6k |
| Star velocity /mo | 23.2k | 2.4k |
| Commits (90d) | 546 | 128 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9025020701905048 | 0.8220244037908294 |
Pros
- +Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
- +Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
- +Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript
- +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
- -Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
- -Potential over-engineering for simple use cases that might be better served by direct API calls
- -Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns
- -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
- •Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
- •Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
- •Developing chatbots and conversational AI with memory, context management, and integration with external data sources
- •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, LangChain or LangGraph?
- LangChain has more GitHub stars (147,383 vs 42,605).
- Which is more actively developed, LangChain or LangGraph?
- LangChain had more commits in the last 90 days (546 vs 128).
- Should I use LangChain 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.