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To debug a LangGraph agent, first enable LangSmith tracing and reproduce the failing input. Inspect the trace’s nested runs to find the model call, tool, or retrieval associated with the unexpected result; use Studio’s Graph mode to inspect traversed nodes and intermediate state. If you need to test a change from saved execution state, use LangGraph checkpoint history to replay or fork the run—traces explain what happened, while checkpoints let you resume or branch execution.
1. Enable tracing and reproduce the problem
For a LangGraph application that uses LangChain components, the documented setup uses the LANGSMITH_TRACING=true and LANGSMITH_API_KEY environment variables. Set the API key for the workspace where you want the trace to appear, and configure provider credentials separately for the models or services your application calls. If your LangSmith workspace is in a region other than the default US region, configure the corresponding LANGSMITH_ENDPOINT for that workspace.
After configuring tracing, run the failing input again. LangSmith’s documented integration captures LangChain calls automatically. Include useful run context—such as the project or environment, application version, tags, and metadata—so you can distinguish a local reproduction from a production run. Do not include sensitive context merely for convenience.
These environment variables and integration steps reflect the official documentation guidance; the pages reviewed do not specify a stable package version or publication date. Check the documentation and your installed LangGraph and LangChain package versions if labels, APIs, or behavior differ.
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2. Find the relevant work in the trace
A trace is a collection of execution runs. Each run represents a unit of work, such as a model call, tool invocation, or retrieval, and runs can be nested inside the larger execution. Start with the failing top-level execution, then follow its children to identify where the observed behavior first diverges from what you expected.
Use Details to investigate execution
In LangSmith, open the trace’s Details view to examine run details, including inputs and outputs. Follow the nested runs around the point where the answer became incorrect, a tool returned unexpected data, or execution failed. This view is the better choice when you need to identify a particular call or inspect its execution data.
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Use Trajectory to read the agent’s sequence
The Trajectory view presents a simpler ordered conversation, including the user message, tool calls, and response. Use it to understand the sequence of interactions at a glance; switch to Details when you need the nested execution information.
3. Add tracing to code that is missing
If the trace contains LangChain calls but not a custom function or provider SDK call, explicitly instrument that code with LangSmith tracing utilities. The documented options include the @traceable decorator or traceable wrapper, as supported by the language and integration you are using. This creates nested runs for work that automatic instrumentation does not capture.
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After adding instrumentation, reproduce the same input and check that the missing operation now appears as a run. A trace that shows the surrounding calls but omits custom code cannot explain what happened inside that uninstrumented operation.
4. Inspect nodes and intermediate graph state in Studio
A trace helps you follow recorded execution calls; when your question is about the graph’s node traversal or intermediate state, use Studio’s Graph mode. Studio provides visualization, interaction, and debugging for graphs that implement the Agent Server API protocol. It can connect to a deployed graph or a graph running locally through Agent Server; it is not required for basic tracing.
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Open the relevant graph execution in Studio and use Graph mode to inspect the nodes traversed and the intermediate states. This helps answer questions such as which node ran before a bad result or what state was present as the graph progressed. If Studio is unavailable for your setup, tracing still provides the recorded runs, while checkpoint APIs provide access to persisted state history when checkpoints are enabled.
5. Replay from a checkpoint to test a hypothesis
When the application has saved checkpoints, LangGraph’s get_state_history lets you locate earlier state. Choose a checkpoint before the node you want to investigate, then invoke using that checkpoint’s configuration. Earlier work is not repeated, but subsequent nodes execute again.
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Replay is execution, not merely reading cached results: downstream LLM calls, API requests, and interrupts may run again and return different results. Consider the side effects before replaying against live services. Where possible, use an environment or downstream system appropriate for a debugging run.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Fork a checkpoint to try a different state
To test whether a changed state value would alter routing or the final output, use update_state on a prior checkpoint and invoke with the resulting configuration. This creates a new branch from the saved state while retaining the prior history; it does not erase or roll back the original execution.
Replay and fork answer different questions. Replay repeats downstream execution from saved state. Fork changes state at a prior point and runs a separate branch so you can compare its downstream behavior with the original history.
Which debugging view should you use?
| Option | Best for | What it shows or does |
|---|---|---|
| LangSmith trace Details | Finding a failed or unexpected nested operation | Execution runs and their inputs and outputs. |
| LangSmith Trajectory | Reading the agent’s interaction sequence | A simplified ordered conversation with user messages, tool calls, and the response. |
| Studio Graph mode | Inspecting graph traversal and intermediate state | Traversed nodes and intermediate graph state; requires an Agent Server-compatible graph. |
| Checkpoint replay | Re-running downstream work from saved state | Subsequent nodes run again, including calls that can produce different results or side effects. |
| Checkpoint fork | Testing a modified state separately | Creates a branch from prior state and preserves the original history. |
Common problems and what to check
- No trace appears: confirm tracing is enabled, the API key and workspace are correct, and the regional endpoint matches the workspace when it is outside the default US region. For JavaScript deployments, callback background settings may also matter, particularly when comparing serverless with non-serverless execution.
- A custom tool or SDK call is absent: explicitly instrument the custom code with supported LangSmith tracing utilities, then reproduce the run.
- The trace does not answer a state question: inspect the run in Studio Graph mode for intermediate graph state, or use checkpoint state history for persisted state.
- A replay differs from the original: downstream nodes execute again, so model, API, or interrupt results may change.
- Trace data contains sensitive values: review what the application records and redact or minimize data before transmission. The LangChain observability documentation describes a Python anonymizer for redacting matching sensitive data.
Trace size and data handling
LangChain’s LangSmith observability concepts documentation states a maximum of 25,000 runs per trace; additional runs sent after that maximum are rejected. This is a LangSmith trace limit, not a LangGraph graph-size limit. Traces may contain application inputs and outputs, so decide what is appropriate to record and apply data minimization or anonymization to sensitive values.
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