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Start with a trace, not a guess. A LangChain agent may repeat model decisions, cycle through graph nodes, invoke the same tool repeatedly, or spend too long waiting on a single model or tool call. Those failures can look alike from the outside, but they require different fixes. LangChain describes an agent as a model calling tools in a loop, shaped by a harness that includes its prompt, tools, and middleware. The trace shows which part is failing.
That distinction matters: a timeout does not prove that the agent is looping, and a recursion-limit error does not by itself tell you whether the graph is incorrectly routed or simply needs to perform more legitimate work. Before changing a limit, adding retries, or rewriting a prompt, identify what the run actually did. Then change the component responsible and replay the same case to see whether the trace changed as expected.
Start with a trace and classify the failure
Enable tracing and reproduce the problem with the same input and runtime configuration that triggered it. LangChain’s Agents guide recommends LangSmith traces to inspect each step of the agent loop, debug tool calls, and evaluate outputs. Follow the run in order and note the model decisions, tool calls, results, errors, and elapsed time.
Read the run as a sequence, rather than relying on the final error message alone. When did the model run? Which tool name and arguments did it emit? What result or error came back? Did the next model turn receive that result? Which step took the most time? A trace that shows repeated graph transitions points to a different line of investigation than one that shows a single slow external request.
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Classify what the trace shows before changing code:
- Repeated graph steps: the same node or edge runs repeatedly. Inspect routing, state updates, and the condition that should end the graph.
- Repeated model decisions: the model emits the same tool call or answer again. Inspect the prompt, available tools, tool result, and whether the next model turn receives that result.
- Wrong tool selection: the model emits an unsuitable tool name or arguments, or dispatch sends a valid call to the wrong implementation. Inspect both the structured model output and the dispatch path.
- Slow span: one model request, external operation, retry, or repeated component consumes the time. Identify which operation is slow before adjusting its timeout or retry policy.
These categories can overlap: for example, a repeated graph route can cause many otherwise normal model calls, while a slow tool may trigger retries that make an agent appear stuck. Follow the order of events to separate cause from appearance. If a tool call repeats because the graph routes back to the same model node, tool selection may be working as configured even though the overall workflow is not. If the trace shows one tool call waiting for a long time, raising a graph recursion limit is unlikely to address the slow operation.
The agent’s harness is a useful way to organize the investigation. The prompt tells the model how to approach the task; the tools define the actions it can request; middleware may affect how model or tool failures are handled. A bad outcome can arise in one of those parts or in the graph around them. The point of tracing is not to assume which part is at fault, but to see what the model chose, what the surrounding system did, and what information came back.
When the agent keeps looping
Find what repeats
In the trace, distinguish a graph node repeating from the model repeatedly requesting the same tool. For a graph cycle, follow the edges and inspect the state after each step. Check whether the state field used by the exit condition is actually updated and whether an error path unintentionally routes back to the model. LangGraph’s recursion-limit guidance describes a recursion-limit error as graph execution that has not reached a stop condition within its configured limit.
Look at the transitions, not only the repeated node name. Is the value that should change between iterations changing? Does the route depend on a condition that can actually become true? If the graph receives an error, does its recovery route eventually terminate, or does it send the run back through the same action without changing the relevant state? These checks help distinguish a legitimate multi-step workflow from a cycle that cannot reach its exit.
If the model repeats a tool call, compare the tool’s returned observation with what the model sees on the next turn. Confirm that a successful result is appended to the conversation and associated with the correct tool call. If the observation is missing, stale, or uninformative, the model may have no reason to choose a different action. Also inspect whether the tool result actually answers the request the model made. A call that succeeds technically but returns no useful information can leave the model in a position where it repeats the same decision.
Read the model step and tool step together. The model emits a structured call with a name and arguments; the tool runs and returns a result or error; the model then receives that observation and decides what to do next. If the emitted call is the same each time, inspect the prompt, available tools, descriptions, schemas, and returned observation. If the graph repeats before a new model decision occurs, concentrate on state updates and routing instead.
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A recursion limit is a safety ceiling, not a loop repair. Raising it can make a wrongly routed graph run longer without making progress. First fix the termination condition, state transition, or error route. Increase the bound only when the trace shows legitimate progress through a workflow that is known to require more steps than the current limit permits. Exact settings vary by graph and installed LangGraph version, so consult the documentation for the version in use.
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Also bound the workflow deliberately: define a clear stop condition and a finite step limit appropriate to the task. Make the terminal outcome visible so that a run that cannot proceed does not silently continue. A bounded workflow helps limit the impact of an unexpected cycle, but it does not replace correcting the route or state that caused it.
When you do adjust a bound, treat it as a change to the allowed execution ceiling, not proof that the underlying issue is fixed. Replay the same input and inspect whether the graph makes meaningful progress and reaches the intended end. If it simply executes more repetitions before stopping, the trace has confirmed that the routing or termination problem remains.
When a LangChain agent times out
Locate the slow operation
Use elapsed time in the trace to separate model generation from an external tool or API call, retry delays, and time spent in repeated steps. A timeout alone does not prove that the agent is looping: one slow provider response, an unresponsive external service, or an application deadline that is too short can produce the same symptom.
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Set the timeout at the layer that owns the slow request—the model provider/client or the tool making the external call. LangChain integrations do not have one established timeout default that applies to every provider. For example, the ChatAmazonNova integration documentation exposes a timeout parameter and max_retries; those options illustrate provider-specific configuration, not framework-wide defaults. Do not copy a value from one integration as a universal recommendation.
Keep the application’s overall deadline in view as well as the component timeout. A component that continues retrying can use up time needed for later steps, and a timeout that is too short for the operation may end a run before it can finish. The sources do not establish universal values for these settings, so choose them for the specific provider, tool, and application deadline, then verify the behavior in a fresh trace.
Use retries for the failure they can fix
For transient network or rate-limit failures, finite retries with backoff may help. They will not fix a bad route, missing user information, or a tool that consistently returns an error. Check whether the trace shows retries consuming the deadline; cap attempts and ensure the final failure is observable. LangChain’s agent guidance demonstrates model and tool retry middleware, while the LangGraph error-handling guide separates transient, recoverable, user-fixable, and unexpected errors.
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There is no single retry policy that fits every operation. Keep attempts finite, make the last failure visible, and inspect whether retries actually address a transient problem. A retry loop that repeatedly receives the same persistent error can make a timeout worse while obscuring the original failure.
When the agent calls the wrong tool
Inspect the model’s actual tool call
Before changing an executor, inspect the model response’s structured tool_calls data. Check the emitted tool name and arguments, then verify that the intended tool was included in the tools supplied to the model. LangChain’s integration example represents calls as structured names and arguments and notes that tool calling requires a compatible model.
This gives you a clean first split in the diagnosis. If the model emitted the wrong name or unsuitable arguments, focus on what it was asked to do, which tools it could see, how those tools were described, the argument schemas, and whether the integration supports tool calling. If the model emitted the intended name and arguments but the wrong implementation ran, trace the dispatch mapping instead. If the right tool ran but its result was associated with the wrong request, inspect the returned result and its tool-call identifier.
Tool selection depends on the model integration. Confirm the chosen model supports tool calling and consult that provider’s integration documentation for its supported behavior. Do not assume that a setting or tool-choice mode supported by one provider will work with another.
Make the available tools distinguishable
- Give each tool a clear name and a description that states when it should—and should not—be used.
- Make argument schemas reflect the information the tool actually requires; ambiguous or overly broad schemas make it harder for the model to form the intended call.
- Review prompt instructions for conflicting guidance about which tool to use.
- Verify that dispatch maps the returned tool name to the intended implementation.
- Return the result with the matching tool-call identifier so the model can associate the observation with its request.
Descriptions and schemas are part of the interface presented to the model. If two tools look interchangeable in their names or descriptions, the model has less information to distinguish them. State the intended use in direct terms and make clear what each action does. If required arguments are not represented clearly in the schema, the model may produce a call that is incomplete or ill-suited to the task.
After a tool runs, verify the rest of the interaction too. A correct model call can still lead to a poor next decision if dispatch resolves the name incorrectly or the result is not returned in association with the matching call. The trace should let you compare the emitted name and arguments with the executed tool and the observation the model receives afterward.
Provider-specific tool-choice controls need special care. The ChatAmazonNova documentation describes auto, required, and none, and notes that the provider does not support every possible tool-choice form. Treat those labels as integration-specific rather than portable defaults. Consult the selected integration’s documentation instead of assuming that identical-looking settings behave the same across providers.
Choose error handling that will not create another loop
Classify the failure and respond accordingly. The LangGraph error-handling guide distinguishes transient infrastructure errors, errors the model may recover from, missing information that needs a user, and unexpected failures.
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- Transient network or rate-limit error: retry a bounded number of times, with backoff where appropriate.
- Recoverable tool or parsing error: return a useful error observation to the model if it can reasonably adjust its action.
- Missing user information: ask for clarification instead of retrying with the same incomplete input.
- Unexpected error: preserve it for debugging rather than repeatedly hiding it behind retries.
LangChain’s agent guide demonstrates ModelRetryMiddleware and ToolRetryMiddleware. Middleware can help, but retries should remain bounded and matched to the operation’s failure mode. LangGraph also explains that splitting slow external work into smaller nodes can make intermediate progress visible and isolate failures so they can receive operation-specific retry handling.
Consider the tradeoff when choosing the size of a node. Keeping an external operation visible in its own step can make it easier to see where time is spent and which operation failed. Smaller nodes can also expose intermediate progress and allow a retry policy to target the external operation rather than treating a larger sequence as one opaque failure. This is not a reason to split every operation mechanically; use the structure that makes the actual slow or failing step observable and separately manageable.
Recovery also needs a terminal path. If an error is returned to the model, it should contain enough information for a reasonable adjustment; if the model cannot fix the issue, the run should not repeat the same action without bound. If the error needs user input, pause for that input rather than attempting again with the same request. If the failure is unexpected, retain the error and trace details to support diagnosis.
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A repeatable diagnostic workflow
- Reproduce: use the same input and runtime configuration, and capture a trace of the run.
- Locate: identify the repeated or slowest step and determine whether it is a model call, tool call, retry, or graph route.
- For tool selection: inspect emitted names and arguments, tool definitions and schemas, model compatibility, dispatch, and returned observations.
- For graph recursion: inspect state transitions, routing, and termination before changing the recursion limit.
- For timeouts: configure the timeout at the slow component’s own client or tool layer; retry only transient failures and cap attempts.
- Verify: rerun the same case and compare traces. Confirm that the loop ends, the intended tool runs, or the slow span improves before treating the issue as resolved.
Keep the verification case fixed while evaluating the change. If the input or runtime configuration changes at the same time as the code, it becomes harder to tell what caused the new behavior. Compare the ordered steps in the new trace with the original: did the graph reach its stop condition, did the model call the intended tool with the right arguments, did the correct implementation run, and did the relevant slow span improve?
When asking for help, include the LangChain and LangGraph versions, the model integration, a minimal reproduction, the relevant error, and the trace details needed to identify the failing step. API names and provider behavior can change, so version context matters. A concise reproduction and ordered trace are more useful than a description that only says the agent is stuck, since they show whether the run repeated decisions, cycled through nodes, retried a failure, or waited on one operation.
Frequently asked questions
Does a recursion-limit error mean my agent has an infinite loop?
Not necessarily. It means graph execution did not reach a stop condition within the configured limit. Inspect graph routing, state updates, and error paths first. A workflow may also be making legitimate progress that requires more steps, but raising the limit is appropriate only when the trace supports that explanation.
How do I tell whether the model or the graph is repeating?
Follow the ordered trace. Repeated graph nodes or edges point you toward state transitions and routing. Repeated model tool calls point you toward the prompt, available tools, tool definitions, model output, and returned observations. The two can overlap, so check whether a graph cycle is causing otherwise normal model calls to repeat.
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Where should I set a timeout for a LangChain agent?
Set it at the layer that owns the slow request, such as the model provider/client or the tool’s external-service client. LangChain integrations do not have one universal timeout default established by these sources. Check the selected integration’s documentation; provider-specific parameters should not be assumed to apply everywhere.
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Should I retry every tool or model error?
No. Use bounded retries for transient infrastructure errors such as network or rate-limit failures. A recoverable tool or parsing error may be returned to the model for adjustment; missing user details call for clarification; unexpected failures should remain visible for debugging. A retry policy that repeats an unfixable failure can create another loop.
What should I inspect when the agent calls the wrong tool?
Start with the structured tool call emitted by the model: its name and arguments. Then verify the intended tool was supplied, its description and schema are clear, the model integration supports tool calling, dispatch selects the right implementation, and the result is returned with the matching call identifier. Tool-choice controls can vary by provider, so check that integration’s documentation.
Frequently Asked Questions
Does a recursion-limit error mean my agent has an infinite loop?
Not necessarily. It means graph execution did not reach a stop condition within the configured limit. Inspect routing, state updates, and error paths first; raise the limit only if the trace shows legitimate progress that needs more steps.
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How do I tell whether the model or the graph is repeating?
Use the ordered trace. Repeated graph nodes or edges point to routing and state transitions; repeated model tool calls point to the prompt, tools, model output, and returned observations. These patterns can overlap.
Where should I set a timeout for a LangChain agent?
Set it at the layer that owns the slow request, such as the provider/client or the external tool’s client. Timeout settings are integration-specific; consult the selected provider’s documentation rather than assuming a universal LangChain default.
Should I retry every tool or model error?
No. Use bounded retries for transient network or rate-limit failures. A recoverable tool or parsing error may be returned to the model for adjustment, missing user details call for clarification, and unexpected failures should remain visible.
What should I inspect when the agent calls the wrong tool?
Inspect the model’s structured tool-call name and arguments, whether the intended tool was supplied, its description and schema, model compatibility, dispatch mapping, and the returned result’s call identifier. Tool-choice behavior can vary by provider.
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Trace the run before changing it. Identify whether the agent repeats graph steps, repeats model decisions, selects or dispatches the wrong tool, or waits on a slow operation. Fix the matching cause, keep retries and execution bounds finite, and verify the result by rerunning the same case and comparing traces.
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