Start with a trace, not a code change. A LangChain agent is a model that calls tools in a loop, while its prompt, tools, and middleware shape that behavior. The trace shows whether the failure is repeated model decisions, repeated graph routing, a tool being called again, or one slow operation. Classifying the symptom first helps you change the component responsible rather than masking the problem.
The phrases “agent keeps looping,” “LangChain agent times out,” and “agent calls the wrong tool” describe visible symptoms, not diagnoses. One timeout can come from a single slow API request or from repeated work; a repeated tool call can reflect a model decision, a missing observation, or graph routing. The practical question is: what happened, in what order, and where did the run stop making progress?
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Start by tracing the failing run
Reproduce the problem with the same input and runtime configuration, then inspect the run’s ordered model and tool steps. LangChain recommends using LangSmith to trace each step of the agent loop, debug tool calls, and evaluate outputs. See the LangChain agents documentation.
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Read the trace as an ordered sequence, not just as a final error message. For each model step, note the response and any structured tool call, including the tool name and arguments. For each tool step, note which implementation ran, its result or error, and whether that observation returned to the next model turn. Compare the timestamps or elapsed durations to see whether time is concentrated in one span or spread across repeated steps. This gives you a concrete place to investigate instead of changing the prompt, limits, and retry settings all at once.
- Repeated graph steps: follow the graph’s edges and state updates. Check whether the stop condition can become true and whether an error path routes back to the model.
- Repeated or unsuitable tool calls: inspect the model’s structured tool-call output, including tool name and arguments, then check the result returned to the model.
- One slow span: determine whether the delay is in model generation, an external tool, retries, or a component being invoked repeatedly.
Do not treat the symptom alone as proof of a particular root cause. Identify the repeating or slowest operation in the trace before changing settings. If the trace is not available, reproduce the failure with enough logging or observability to establish the order of model decisions, tool calls, results, and errors; otherwise it is easy to confuse a slow call with a loop.
When the agent keeps looping
First determine what is repeating. LangChain describes the agent as a model calling tools in a loop until its task is complete; in a graph-based application, repeated node execution may also indicate that routing or state updates are not moving the run toward a terminal condition. The agent documentation explains the loop, while LangGraph’s recursion-limit guidance addresses graph execution that has not reached a stop condition within its configured limit.
Separate two patterns that can look alike from outside the run. If the same graph node or edge is repeated, the immediate question is whether state and routing can advance toward the graph’s end. If the model is called repeatedly and emits the same tool request, inspect the model’s decision and the observation it received from the prior tool call. Both can be happening in one run, so trace the complete sequence before deciding which layer to change.
If graph nodes or edges repeat
- Follow the repeated path and inspect the state each node receives and returns.
- Check whether the condition that should end the graph can ever become true with those state values.
- Inspect error handling and fallback routes: an error branch that sends execution back to the same model or tool can form a cycle.
- Define an explicit stop condition and keep a bounded step limit as a safety guard.
A recursion-limit error means graph execution has continued without reaching a stop condition within its configured bound. Increasing that bound only raises the ceiling; it does not correct faulty routing. Change it only when the trace shows legitimate progress and the intended workflow is known to need more steps. Limits and runtime configuration depend on the installed LangGraph version, so use documentation for that version.
Before adjusting the bound, write down what the expected terminal state or condition is and identify which state update should make it reachable. Then compare that expectation with the values shown in the repeated steps. If the state is unchanged, or the routing condition keeps selecting the same path, the underlying issue is progression or routing, not a need for more iterations. If the workflow is making legitimate progress through a known sequence but the configured bound is too low, a version-appropriate limit change may be justified. Retain a finite bound so a future unexpected cycle cannot run indefinitely.
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If the model repeats the same tool call
Inspect the tool result or error the model received after the previous call. Verify that a successful result is appended to the conversation or graph state and associated with the matching tool call, so the next model turn can use it. If the observation is missing, mismatched, or unhelpful, the model may have no reason to choose a different action.
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Use the trace to compare the first call with the next one. Did the tool actually run? Did it return a useful result or an error? Did the result reach the model on the next turn and correspond to the correct call? If the model received the same empty, ambiguous, or failed observation, repeated selection may be a consequence of what the model can see. If it did receive a meaningful result and still repeats the action, inspect the prompt, tool definitions, and model compatibility rather than assuming the executor is at fault.
When a LangChain agent times out
A timeout is not by itself evidence of a loop. It can result from a slow model response, one slow external request, repeated work, or retries that outlast the application’s deadline. Use trace timing to locate the slow span, then configure a timeout at the layer that owns that request: the model provider’s client, the external-service client, or the tool implementation. LangChain integrations do not share one universal timeout setting.
Provider-specific configuration illustrates why the setting must be checked for the chosen integration. For example, the ChatAmazonNova integration documentation exposes timeout and max_retries parameters. These are integration options, not framework-wide defaults; consult the documentation for your actual provider and installed version.
Set deadlines and retries together
Choose a finite timeout that fits the operation and the application’s overall deadline. If retries are enabled, account for the time spent on each attempt and any backoff; otherwise retries can keep a request alive beyond the time the caller can wait. Retry transient network or rate-limit errors with a bounded policy. Do not automatically retry every failure: a parsing or tool error may need to be returned to the model for correction, missing user information needs clarification, and unexpected failures should remain visible.
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When you locate the slow span, identify who owns it before changing a timeout. A model call belongs to the chosen model integration; an external API call belongs to its client or the tool wrapping it. Setting a deadline at the wrong layer may leave the slow operation untouched. Check the corresponding integration or client documentation for supported timeout and retry options; LangChain integrations do not establish a universal default or a universally suitable timeout value.
Retries and deadlines should be considered together rather than tuned independently. A finite retry policy can still exceed the caller’s deadline if several attempts and their backoff fit inside no overall budget. Conversely, a short deadline with repeated retries may generate a chain of failures without allowing a transient request to complete. The trace can show whether the run is spending time on repeated attempts, one long attempt, or many normal agent steps. Apply a bounded policy to the failure type and preserve enough error context to tell which case occurred.
After changing a timeout or retry policy, run the same case again and compare the trace. Check that the relevant span now finishes within the intended deadline and that the change has not merely hidden repeated graph execution.
When the agent calls the wrong tool
Inspect the actual tool_calls data in the model response before changing the executor. Tool calls are structured requests with a name and arguments; checking the response separates a model-selection problem from a dispatch or result-handling problem. See the ChatAmazonNova integration documentation for an example of structured calls and provider-specific tool-choice behavior, and consult the documentation for your selected integration.
- Confirm the intended tool was supplied. Check the tool list passed to the model for this run, not just the tools defined elsewhere in the application.
- Make tools distinguishable. Give each tool a clear name and description that explain its purpose and when to use it.
- Validate the schema. Ensure argument names, types, and required fields match the task and the implementation. Look for arguments that are ambiguous or easy to confuse with another tool.
- Check model and integration support. Tool calling depends on a compatible model and integration. Verify support in the chosen provider’s documentation.
- Verify dispatch and response handling. Confirm the returned tool name maps to the intended implementation and that its result is sent back with the matching call identifier.
These checks distinguish several different failures hidden by the phrase “wrong tool.” The model may have chosen a different tool than expected; the intended tool may not have been included in this run; or the model may have returned the expected name but the application dispatched it incorrectly. A correct dispatch followed by a result associated with the wrong call can also confuse subsequent model turns. Compare the structured request, the implementation actually executed, and the result returned to the model in that order.
Tool-choice controls are provider dependent. The ChatAmazonNova integration describes auto, required, and none, and notes that the provider does not support every possible tool-choice form. Do not assume a setting or behavior from one integration works the same way with another.
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If you change a name, description, schema, prompt instruction, or tool-choice setting, change one relevant part at a time and rerun the same input. The trace should show whether the model emitted a different structured request and whether dispatch then invoked the intended implementation. A setting that exists in one provider integration is not evidence that another provider accepts the same choice or interprets it identically.
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Retries are useful only when they match the error. LangChain’s agent guidance demonstrates ModelRetryMiddleware and ToolRetryMiddleware; the LangGraph error-handling guide distinguishes transient errors, errors a model may recover from, user-fixable errors, and unexpected failures.
| Failure type | Response to consider |
|---|---|
| Transient network or rate-limit error | Retry with a finite attempt limit and suitable backoff. |
| Tool or parsing error the model can correct | Return a useful error to the model so it can adjust its action. |
| Missing information that only the user can provide | Ask for clarification rather than retrying the same request. |
| Unexpected failure | Keep it visible and record enough context to diagnose it. |
Cap recovery attempts and make terminal behavior observable; otherwise error handling can create another unbounded loop. Where it improves diagnosis, isolate a slow external operation in its own graph node. LangGraph notes that smaller nodes provide intermediate visibility and can isolate failures from external services.
That distinction matters when a recovery path involves the model. A recoverable tool or parsing error can be exposed so the model may adjust its action, but repeating the identical failed request without new information is not useful recovery. A missing user detail is different again: the model cannot reliably supply information that only the user knows, so ask for clarification. An unexpected exception should not disappear into a retry loop, because that removes the evidence needed to understand it.
For a slow external operation, a separate graph node can make the operation and its outcome easier to see in the trace and allow its retry policy to be considered separately. Smaller nodes are not a universal requirement, but they can improve intermediate visibility and help isolate external-service failures. Keep error records useful enough to connect the failure to its step, without treating a retry as proof that the original operation succeeded.
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A repeatable diagnostic workflow
- Reproduce the issue with the same input and runtime configuration, and capture a trace.
- Identify the repeated or slowest step: model call, tool call, retry, or graph route.
- If tool selection is wrong, inspect emitted names and arguments, supplied tool definitions, schemas, model compatibility, dispatch, and returned observations.
- If graph execution repeats, inspect state transitions and termination before changing a recursion limit.
- If a request is slow, set a timeout at the slow component’s own client or tool layer; retry only transient failures, with a bound.
- Run the same case again and compare traces. Confirm the loop ended, the intended tool ran, or the slow span improved before calling the issue resolved.
When asking for help, include the framework and integration versions, a minimal reproduction, the relevant error, and the trace. That context makes it possible to distinguish version-specific behavior from a prompt, graph, tool, or provider problem.
Keep the comparison controlled: use the same input and runtime configuration for the before-and-after run, and note the specific change made. If multiple settings change together, a better result does not reveal which one addressed the cause. Likewise, a run that finishes once does not establish that an intermittent problem is fixed; retain the trace and compare the behavior of subsequent reproductions as appropriate to the issue.
The final check should match the original symptom. For a loop, confirm the repeated sequence ends through the intended stop condition rather than merely reaching a larger ceiling. For wrong tool selection, confirm the structured call names the intended tool, dispatch invokes that implementation, and the matching result returns. For a timeout, confirm the slow span improved or is now bounded at the responsible layer. This is stronger evidence than a changed error message alone.
Frequently Asked Questions
What should I inspect first when a LangChain agent keeps looping?
Capture a trace and identify whether the repeated unit is a model decision, a tool call, or a graph node or edge. For repeated graph steps, inspect state updates, routing, and whether the stop condition can become true. For repeated tool calls, check what result or error the model received after the previous call.
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Does a LangGraph recursion-limit error mean I should raise the limit?
Not by itself. It means graph execution did not reach a stop condition within the configured bound. Inspect routing and state transitions first. Raise the limit only when the trace shows legitimate progress and the intended workflow is known to require more steps; configuration is version- and graph-specific.
How do I find why a LangChain agent timed out?
Use trace timing to locate the slow span and determine whether it is model generation, an external tool/API call, retries, or repeated graph work. Set the timeout at the layer that owns the slow request, and consult the selected integration’s documentation because there is no universal timeout setting established across integrations.
Why does an agent call the wrong tool?
Inspect the model response’s structured tool-call name and arguments, then check which tools were supplied, whether names and descriptions are distinct, whether the schema fits the task, and whether the model/integration supports tool calling. Also verify dispatch and that the result returns with the matching call identifier.
Should I retry every failed tool call?
No. Bounded retries can help transient network or rate-limit failures. A tool or parsing error may be returned to the model for correction; missing information calls for user clarification; unexpected failures should remain visible for debugging. Unbounded recovery can create another loop.
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Are tool-choice settings such as auto or required portable between providers?
Do not assume they are. Tool-choice behavior and supported forms depend on the provider integration. For example, the ChatAmazonNova documentation describes auto, required, and none but notes it does not support every possible tool-choice form. Check the documentation for the integration you use.
The Bottom Line
Trace first, classify what repeats or runs slowly, then change the responsible layer. Inspect state and termination before raising a recursion limit; inspect structured tool calls, definitions, compatibility, and dispatch before blaming the executor; and apply timeouts and bounded retries where the slow operation or failure type belongs. Re-run the same case and compare traces before calling the issue fixed.
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