LangChain vs LangGraph
for AI engineers.
LangGraph for production. LangChain for prototypes.
What this actually means for AI engineers.
For AI engineers building production agents, LangGraph is clearly the right choice. LangChain pioneered the chain abstraction but its limitations are now well-known: brittle composition, poor handling of branching logic, weak state persistence. LangGraph (from the same team) is graph-shaped, supports persistent state, handles human-in-the-loop, and is what production agents should run on. Many teams skip both and write their own graph orchestration once they understand the patterns.
AI engineers-specific gotchas
- LangChain's abstraction can leak — sometimes write the prompt yourself
- LangGraph's checkpointing requires careful storage choice (Redis, Postgres)
- Type safety in TS is improving but inconsistent
- Both pair well with LangSmith for observability
- LangGraph's human-in-the-loop primitives are genuinely useful
An AI team replaces a LangChain-based agent with LangGraph. The graph model makes it natural to handle retries, fallbacks, and human approval steps. Resulting agent reliability rises from ~85% to ~97%.
Pick by use case.
LangChain
Simple chains, quick prototypes.
LangGraph
Production agents, branching workflows, retry/fallback logic.
Direct comparison.
| Feature | LangChain | LangGraph |
|---|---|---|
| Conceptual model | Linear chain | Graph / state machine |
| Branching / conditionals | Awkward | Native |
| Persistence / checkpointing | Limited | Native |
| Production readiness | Mixed | Designed for it |
| Learning curve | Lower | Higher |
| Observability | LangSmith | LangSmith |
We've shipped both.
If you're evaluating these as a AI engineers, brief us — we can save you weeks.
Talk to usCommon AI engineers questions.
Should we just write our own graph orchestration?
Maybe — once you understand the patterns. LangGraph is a useful starting point.
What about Pydantic AI or DSPy?
Different abstractions. Pydantic AI for structured outputs; DSPy for prompt programming.