langchain-rust vs langgraph
Side-by-side comparison of two AI agent tools
langchain-rustopen-source
🦜️🔗LangChain for Rust, the easiest way to write LLM-based programs in Rust
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| langchain-rust | langgraph | |
|---|---|---|
| Stars | 1.3k | 28.0k |
| Star velocity /mo | 30 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.3931143798228021 | 0.8081963872278098 |
Pros
- +Supports multiple LLM providers (OpenAI, Claude, Ollama) with consistent API
- +Comprehensive vector store integrations including Postgres, Qdrant, and SurrealDB
- +Native Rust performance and memory safety for production AI applications
- +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
- -Smaller ecosystem and community compared to Python LangChain
- -Requires Rust knowledge which has a steeper learning curve
- -Documentation and examples are more limited than the main LangChain project
- -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 RAG systems with vector databases for semantic document retrieval
- •Creating conversational AI applications with persistent memory and context
- •Developing high-performance AI pipelines that require Rust's safety and speed
- •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