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Reliable LangGraph agents need different responses to different failures: retry transient service errors, time-bound asynchronous work that can hang, route exhausted failures deliberately, and persist thread state with a durable checkpointer. These are separate controls—not one “retry everything” switch. The Python timeout and node error-handler APIs described here require langgraph>=1.2; check your installed version and dependency lock before using them.
Start by deciding what should happen when a node fails
Build reliability around the operation each node performs. A temporary network or service failure may clear if retried. A bad input, type mismatch, or programming bug usually will not. Retrying both alike wastes time and can repeat side effects.
Make nodes small enough to isolate work that has a distinct failure strategy. For example, separating an external API call from unrelated model or transformation work lets you retry and inspect the call independently. LangGraph resumes from the start of the node that failed, so a smaller node can reduce repeated work when a later step fails. More boundaries also mean more checkpoints; choose them according to recovery needs, observability, and the cost of repeating work. See the LangGraph node design guidance.
Configure retries for transient failures
Attach a retry policy to the node that makes the transiently failing call. This Python example uses the documented API:
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from langgraph.types import RetryPolicy
builder.add_node(
"call_api",
call_api,
retry_policy=RetryPolicy(max_attempts=3),
)max_attempts counts the first try, so this setting allows three attempts total. The documented defaults are an initial interval of 0.5 seconds, a backoff multiplier of 2.0, a maximum interval of 128 seconds, and jitter. These are framework defaults, not a recommended production policy for every service. Verify the behavior for your installed release in the Python fault-tolerance guide.
The default exception filter is not a blanket retry rule. The guide lists exclusions including ValueError, TypeError, RuntimeError, and OSError. It also says that for common HTTP libraries such as requests and httpx, retries are limited to 5xx responses. If your upstream service uses different status codes or exception types to signal transient failures, define retry_on as an exception class or callable that matches your service’s semantics.
- Retry errors that could plausibly recover, such as a temporary upstream outage.
- Do not retry deterministic validation or programming failures without a specific reason.
- Before repeating a call, consider whether it has side effects and whether the operation is safe to repeat. A retry policy does not make an external operation idempotent.
For attempt-sensitive behavior, the runtime exposes runtime.execution_info.node_attempt, a 1-indexed attempt number. A node can use it to choose a fallback after an initial attempt, but fallback logic alone does not make a repeated external call safe.
Set timeouts for asynchronous nodes that can hang
In Python, node timeouts require langgraph>=1.2 and currently apply only to async nodes. A number or timedelta specifies a wall-clock run limit; TimeoutPolicy can set separate run and idle limits. The values below are illustrative, not universal recommendations:
from langgraph.types import RetryPolicy, TimeoutPolicy
builder.add_node(
"call_model",
call_model,
timeout=TimeoutPolicy(run_timeout=120, idle_timeout=30),
retry_policy=RetryPolicy(max_attempts=3),
)A run_timeout caps the total wall-clock time for one attempt and does not reset when the node makes progress. An idle_timeout limits time without observable progress. Under the default refresh_on="auto", progress refreshes the idle timer; for long-running work without natural progress signals, emit explicit heartbeats as shown in the timeout documentation.
When a timeout is reached, LangGraph raises NodeTimeoutError, which is retryable by default. Writes made by the failed attempt are cleared before a retry. That protects graph state from partial writes, but it is not a transactional rollback of external effects: a request may already have reached a service before the timeout. Set timeouts and retries together only after weighing the cost and side effects of repeating the operation.
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Synchronous nodes with timeout settings are rejected at compile time. If a blocking operation needs a timeout, consider placing it in an async node and running the blocking I/O with asyncio.to_thread, where appropriate.
Define what happens after retries are exhausted
For Python langgraph>=1.2, a node’s error_handler can receive failure context after retries are exhausted, update graph state, or return a Command that routes execution to a recovery node. Use that path for a defined action—such as recording a graceful failure or starting a compensation step—rather than treating it as another undifferentiated retry.
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Keep the two decisions separate: the retry condition answers whether the same operation might succeed if tried again; the error handler answers what the graph should do when it cannot continue normally. The Python API and behavior are covered in the fault-tolerance guide.
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An interrupt() is a human-in-the-loop pause, not a failure sent through the retry or error-handler path. For unexpected exceptions the application cannot handle, let them surface so they remain diagnosable; see the LangGraph design guidance on error handling.
Persist thread state with a checkpointer
A checkpointer saves graph-state snapshots for a particular thread. Compile the graph with one, then provide a stable thread_id when invoking it so later invocations can continue that thread. Checkpoints support conversation continuity, human review pauses, time travel, and recovery after failures. For example:
graph = builder.compile(checkpointer=checkpointer)
result = graph.invoke(
inputs,
config={"configurable": {"thread_id": "customer-123"}},
)A store solves a different problem: it holds application-defined data across threads, such as shared facts or preferences. Use a checkpointer for thread-scoped graph state and a store when the application needs cross-thread data; an application may use both. The Python persistence guide explains their scope and setup.
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Choose storage that matches the environment
InMemorySaverorMemorySaverkeeps checkpoints in process memory; those checkpoints disappear when the process restarts.- The guide identifies
SqliteSaveras local file storage for development andPostgresSaveras a persistent option.
For production, use persistent checkpoint storage rather than relying on process memory. Checkpoint accumulation can increase latency and storage costs, so set an appropriate retention or pruning approach.
Separate Agent Server behavior from graph retry policies
For LangSmith Agent Server deployments, PostgreSQL is the default checkpoint backend and remains required even when MongoDB is configured for checkpoint data. The server’s data-plane documentation also describes a separate retry mechanism for certain transient PostgreSQL errors, limited to three attempts per run. That platform-level behavior is distinct from a node’s RetryPolicy; see the Agent Server data-plane documentation.
Make failures observable and recovery practical
Separate nodes when doing so makes it clear which step failed and allows that step to have its own timeout or retry rule. Keep useful raw state and execution metadata available for debugging and recovery, and format prompts when they are needed rather than obscuring the intermediate state. The LangGraph design guidance discusses intermediate visibility as a reason to distinguish classification and external-service steps.
Quick Recap
- Give each node a clear purpose and a failure strategy matched to its operation.
- Make retry filters reflect the actual transient errors of the dependency.
- Use timeouts only on supported async nodes, and treat timeout retries as potentially repeated external work.
- Route exhausted failures to a deliberate recovery or graceful-failure path.
- Use a persistent checkpointer when thread state must survive process restarts, and manage checkpoint growth.
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