# How do I replay a failed LangGraph run?

Call `graph.get_state_history(config)` on the run's `thread_id` to get every checkpoint LangGraph saved for it, ordered most recent first, then pick the one where `next` names the node that failed. Invoking the graph again with that checkpoint's id skips every node before it, since their results are already saved, and re-runs forward from exactly the state the graph had at that point.

That state doesn't have to stay as it was. `update_state()` writes new values onto a chosen checkpoint and hands back a new checkpoint id, so you can change what the failing node received, an argument, a retrieved document, before running forward again. The original checkpoint isn't touched, so the failing path stays there to compare against. This is real replay rather than a reconstruction, because the graph reruns against the same state object it actually had, not [a fresh session built from a paraphrase of what happened](/answers/failure-replay/why-cant-i-just-reproduce-the-failure-locally), which is the same standard any agent's failure replay has to meet, not just LangGraph's.

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Sources:
- LangGraph docs: Checkpointers: https://docs.langchain.com/oss/python/langgraph/checkpointers (fetched 2026-09-15)

Source: https://tessary.ai/answers/langgraph-evals/how-do-i-replay-a-failed-langgraph-run
More on Langgraph evals: https://tessary.ai/answers/langgraph-evals
From Tessary, agent reliability for AI agents in production: https://tessary.ai