The Tool Desk
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The Python fault-tolerance APIs for node timeouts and exhausted-retry error handlers require langgraph>=1.2. Check the version pinned in your project before using them; the examples below follow the current Python documentation.
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How should you divide a workflow for recovery?
Give each node a clear responsibility, especially around operations that fail independently. For example, keep an external API call separate from unrelated model or transformation work when that makes the call easier to retry, inspect, or recover. If a later step fails, LangGraph resumes from the start of the node where execution stopped, so smaller nodes can reduce the amount of work repeated.
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There is a trade-off: more node boundaries mean more checkpoints. Choose boundaries that isolate likely failures and make progress observable without splitting work so finely that checkpoint overhead outweighs the benefit. LangGraph’s design guide discusses node separation and visibility at its agentic design guide.
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Which failures should a node retry?
Retry errors that could plausibly clear on another attempt, such as a transient upstream service failure. Avoid repeating deterministic input, type, or programming errors: another attempt usually produces the same failure and can consume time or duplicate side effects.
Attach the policy to the node that performs the failure-prone operation:
from langgraph.types import RetryPolicy
builder.add_node(
"call_api",
call_api,
retry_policy=RetryPolicy(max_attempts=3),
)In the documented Python API, max_attempts includes the first attempt. The current defaults are shown below; they are framework defaults, not universal production recommendations. Confirm the behavior for your installed release.
| Setting | Documented default | What it controls |
|---|---|---|
max_attempts |
3, including the first attempt | Total executions allowed for the node |
initial_interval |
0.5 seconds | Initial wait before a retry |
backoff_factor |
2.0 | Exponential growth of the wait between retries |
max_interval |
128.0 seconds | Maximum retry interval |
jitter |
True |
Adds variation to retry timing |
The default exception filter is not simply “retry every error.” The fault-tolerance guide excludes several exception families, including ValueError, TypeError, RuntimeError, and OSError. It also says common HTTP libraries such as requests and httpx are retried only for 5xx responses. If your service uses different status or exception semantics, specify a narrower or otherwise appropriate retry_on exception class or callable. See the Python fault-tolerance guide for the current API and default filter.
When behavior should change after an attempt, the guide documents runtime.execution_info.node_attempt as a 1-indexed attempt number. You can use it to select a fallback path, but fallback logic does not make a repeated external operation idempotent. Consider whether the service supports idempotency keys or another safeguard before retrying a call that can create or modify data.
When should you set node timeouts?
A timeout is useful for an async node that could wait indefinitely on network or other asynchronous work. Node timeouts currently apply only to async nodes and require langgraph>=1.2. A numeric value or timedelta sets a wall-clock limit; TimeoutPolicy can set separate run and idle limits.
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- Run timeout: caps the total wall-clock time for one attempt. Progress does not reset it.
- Idle timeout: caps time without observable progress. With the default
refresh_on="auto", progress emitted by the node refreshes the idle timer.
For a long-running task without natural progress signals, the documentation shows explicit heartbeats. Synchronous nodes with timeout settings are rejected at compile time. If appropriate for the work, wrap blocking I/O in asyncio.to_thread inside an async node rather than applying the timeout directly to a synchronous node.
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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),
)The 120-second run limit, 30-second idle limit, and three-attempt policy in this example are illustrative settings, not recommended values for every workload. A timeout raises NodeTimeoutError, which is retryable by default. LangGraph clears writes from a timed-out attempt before retrying, but that is not a transactional rollback of external effects. Assess the cost and side effects of repeating the operation before combining a timeout with retries.
What happens when retries are exhausted?
Decide how the graph should proceed when an error cannot be retried or all attempts fail. In 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 to a recovery node. Use that route for a defined graceful-failure or compensation path rather than hiding an error the application cannot handle.
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Keep retry eligibility separate from recovery behavior: retry when another attempt could plausibly succeed; use a handler for the remaining failure path. An interrupt() is a human-in-the-loop pause, not an error sent through the retry or error-handler path. The fault-tolerance documentation describes the Python APIs; the general principle of letting unexpected errors surface for debugging also appears in the JavaScript design guide.
How should you persist graph state?
A checkpointer and a store solve different persistence problems. A checkpointer saves snapshots of graph state for one thread; a store holds application-defined information across threads, such as shared preferences or facts. Use a checkpointer for continuity, human-review pauses, time travel, and recovery. Add a store when your application needs cross-thread data.
Compile the graph with a checkpointer, then pass a stable thread_id when invoking it so LangGraph can associate execution with the right thread. The Python persistence guide explains checkpointers, stores, and thread configuration.
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| Mechanism | Scope and use | Durability consideration |
|---|---|---|
In-memory checkpointer (InMemorySaver or MemorySaver) |
Thread-scoped graph snapshots; convenient for development | RAM-held checkpoints disappear when the process restarts |
SqliteSaver |
Checkpoint persistence in a local file | Listed by the guide as a local-storage option for development |
PostgresSaver |
Persistent checkpoint storage | Listed by the guide as a persistent option |
| Store | Application-defined information shared across threads | Choose and configure storage appropriate to the application; it is not a substitute for a thread checkpointer |
For production, select persistent checkpoint storage rather than assuming an in-memory saver is durable. Checkpoint accumulation can also increase latency and storage costs, so plan retention or pruning for the application’s recovery and audit needs.
How does LangSmith Agent Server change the setup?
Graph-level retry policy is separate from LangSmith Agent Server’s platform behavior. According to the Agent Server data-plane documentation, PostgreSQL is the default checkpoint backend and remains required even when MongoDB is configured for checkpoint data. The data-plane docs also describe a server-level retry for certain transient PostgreSQL errors, currently limited to three attempts per run. That deployment mechanism does not replace a node’s RetryPolicy for failures in your graph’s work.
What makes reliability behavior easier to debug?
Make failures attributable to a step. Separate nodes where doing so reveals which operation failed and permits a different retry strategy. Preserve useful raw state and execution metadata needed for debugging and recovery, and format prompts at the point of use rather than obscuring intermediate values.
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- Give external calls policies based on their actual transient-error behavior.
- Set async run or idle limits when a node can hang, and ensure its progress signals are meaningful.
- Define what the graph does after retries are exhausted.
- Use a stable thread identifier and durable checkpoint storage when state must survive process restarts.
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