A practical decision framework for choosing between LangChain chains and LangGraph for production AI applications, drawn from auditing both in the wild.
Your workflow is genuinely linear — RAG retrieve → augment → generate, or summarise → translate → format
You are prototyping and want to ship a useful demo this week
Your team is new to LLM frameworks and needs a low-friction starting point
The whole feature is small enough that you would rewrite it before it grows complex
Your workflow has any agentic behaviour — tool use with re-entry, multi-step planning with self-correction, multi-agent collaboration
You need explicit state inspection and time-travel debugging
You are building a feature that will live in production for years and grow capabilities over time
You want first-class observability over the workflow graph, not just per-step traces
Step 1 — Sketch your workflow on a whiteboard. If you can draw it as a sequence with at most one or two branches, LangChain is fine. If it has loops or conditional re-entry, jump to LangGraph.
Step 2 — Estimate the lifespan of the feature. Anything that will live in production over six months and grow over time benefits from LangGraph's explicit state.
Step 3 — Account for observability needs. If a customer-facing AI feature needs end-to-end traceability for compliance or trust, LangGraph's graph visualisation pays for the steeper learning curve.
Step 4 — Do not over-frame. If you can express your workflow as straight Python or Go calling the model SDK directly, do that. Frameworks earn their keep when they save you from reimplementing graph or state machine semantics.
Step 5 — Migration is possible. Teams that started on LangChain and outgrew it migrate to LangGraph successfully — but the migration is a real engagement, not a rename. Plan for it explicitly if you start on LangChain knowing the workflow will grow.
In production engagements where LangChain chains have been pushed past their natural fit, the symptom is always the same — the chain has a hand-rolled loop or branching mechanism inside it that started small and now has a quarter's worth of incidents stuck to it. LangGraph would have made the loop and the state explicit from day one, with a fraction of the operational scar tissue. Our recommendation: if there is any agentic behaviour in scope, default to LangGraph even if the first version is a single linear flow. It is the cheapest decision when the workflow grows, and it does not cost anything more when it stays small.