To replay an AI agent run, first inspect its trace to find the earliest unexpected step; then use a framework checkpoint or a controlled rerun if you need to execute work again. Those are different operations: a trace records what happened, while a checkpoint saves workflow state. A fresh rerun may not reproduce the original exactly if model responses, tools, or external conditions change.
What “replay” means when debugging an agent
Agent systems can produce several kinds of execution records, and the word “replay” does not mean the same thing in every framework or observability product.
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- Trace inspection: Read the saved record of a past execution. LangChain describes a trace as an ordered collection of runs; a thread can group traces across turns. A trace may include the request, retrieved context, tool arguments and results, intermediate steps, and final response. Inspection explains the recorded execution but does not necessarily run the agent again. LangChain’s tracing concepts.
- Checkpoint time travel or resume: Examine or restore persisted workflow state in a framework that supports checkpointing. LangGraph documents checkpointers as enabling time travel and replay of prior graph executions for reviewing or debugging specific steps. LangGraph persistence and replay.
- Fresh rerun: Execute the application again with saved inputs. This is a reproduction attempt, not proof of an identical replay: model outputs, tool results, external data, and runtime conditions may differ.
- Recorded-call replay: An application-specific harness can substitute previously captured tool responses for live calls. Document which calls are stubbed and which remain live; there is no universal implementation implied by the term.
LangSmith is one example of an observability option: its official overview describes agent tracing and monitoring, support for common frameworks and OpenTelemetry, and evaluation and cost-monitoring capabilities. It is not required to debug an agent. LangSmith observability overview.
Find the first step that went wrong
Start with the earliest divergence from the behavior you expected, not the final answer or the last error. Later failures often follow from an earlier bad input, retrieval result, tool response, or routing decision.
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- Preserve the failure record. Save the run or trace ID, timestamp, code revision, model and configuration identifiers, and relevant environment details. Keep secrets and unnecessary personal data out of trace payloads; redact them before sharing or exporting records.
- Follow the execution tree from the root. Read nested model, retriever, and tool runs in order. Note the first output or transition that contradicts the expected workflow, along with its parent and child runs.
- Compare the recorded inputs and outputs at that point. Check the user input, retrieved documents and versions, tool arguments and responses, and state passed between steps. A polished but incorrect answer may be downstream of stale retrieval context or a wrong tool result rather than a faulty final model call.
- Trace the consequence downstream. Once you identify the earliest suspect step, follow its output into later calls to see how it shaped the failure. This distinguishes a root cause from symptoms without assuming the final response identifies the cause.
LangSmith’s overview describes tracing and monitoring as ways to inspect agent execution; the same debugging logic applies to any trace viewer that records the fields your application needs. LangSmith observability overview.
Choose how to replay or reproduce the failure
Use trace inspection when you need to understand the historical run
Inspect the stored record when the question is what the agent actually received, retrieved, called, or returned at the time. A trace is useful only to the extent that the application recorded the relevant fields; it is not itself a guarantee that you can re-execute the run.
Use checkpoint time travel when the workflow saved state
If the application uses a checkpointer, follow that framework’s documented method to inspect or resume from the desired saved state. LangGraph’s documentation says compiling a graph with a checkpointer enables workflows that include time-travel debugging and fault-tolerant execution. Exact APIs and behavior depend on the framework, version, checkpoint backend, and graph design. LangGraph persistence.
Resuming can repeat work in the node where execution stopped. Smaller node boundaries can make it easier to inspect progress and limit how much work is repeated after a failure, but they add workflow-design complexity. LangGraph replay documentation.
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Use a fresh rerun when there is no checkpoint
Run the application again with the captured inputs and configuration as closely as possible. Record which model and tool calls are live, which responses are replayed from recordings, and what external state could have changed. Without controls over model and external-service behavior, describe the result as a reproduction attempt rather than an exact replay.
Make the reproduction useful
Change one plausible cause at a time—such as a retrieval filter, tool schema, prompt, or routing condition—and compare the new execution with the original at the first divergent step. Capture relevant inputs, outputs, model/configuration identifiers, and tool results where permitted, while respecting privacy and data-handling requirements.
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- Keep the original trace and the reproduction record distinguishable.
- Record any changed dependencies, data versions, or configuration so a difference in outcome can be interpreted.
- If your team has an evaluation workflow, turn a useful production example into a regression case. LangSmith’s overview discusses evaluation and backtesting against production examples, but that feature is not necessary for basic debugging. LangSmith observability overview.
- Look for recurrence around a particular tool, node, model configuration, or retrieval source; monitoring failure rate and latency can help reveal an operational pattern.
If failed traces could not upload
LangChain Support documents a workaround for traces captured by its SDK mechanism: save failed trace JSON locally using LANGSMITH_FAILED_TRACES_DIR, with the optional size limit LANGSMITH_FAILED_TRACES_MAX_MB, and post the saved trace JSON to the endpoint later. The article describes this as a workaround, not a general trace-import facility, and says to verify each POST succeeds before deleting its file. Check the current SDK documentation before relying on these environment variables. LangChain Support: How to upload failed traces to LangSmith.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWhat to check when choosing tracing or replay tools
Choose based on the failure you need to investigate and your application’s constraints, rather than assuming a trace viewer can resume execution.
Quick Recap
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- Framework and language: Confirm support for your stack and the execution model you use.
- Trace detail: Check whether it captures nested model, retrieval, and tool runs and the fields needed to diagnose failures.
- Execution capability: Decide whether you need historical inspection, checkpoint resume/time travel, or controlled re-execution. These are separate capabilities.
- Data handling: Review deployment, privacy, retention, filtering, and whether traces may leave your environment.
- Operational fit: Consider evaluation or regression support, search, retention, and cost; availability can vary by product and plan.
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