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To add human review to an asynchronous LangGraph workflow, pause the graph with interrupt(), save its state with a checkpointer, and resume it later using the same thread_id and the reviewer’s response. The queue coordinates when a worker runs; the checkpointer preserves the graph state needed to continue. A worker does not need to sit idle while a person reviews a request.
What LangGraph contributes—and what the queue must do
LangGraph provides the pause-and-resume contract: an interrupt exposes a value to the caller, graph state is saved through the persistence layer, and a later invocation can continue the same thread with external input. Your application or runtime must handle the surrounding work: record the job-to-thread relationship, alert a reviewer, collect a decision, and schedule the resume invocation.
This distinction matters when designing an async system. A queue is for coordinating work and retries; a checkpointer is for restoring graph state. Neither one replaces the other, and LangGraph’s interrupt documentation does not prescribe a particular broker or custom queue architecture.
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Compile the graph with a checkpointer, provide a stable thread identifier in configurable.thread_id, and call interrupt() where the graph needs input from outside. The interrupt payload is returned to the caller, so the application can present it for review. When a worker resumes the graph with Command({ resume: value }), that value becomes the return value of the paused interrupt() call. The payload must be JSON-serializable. LangGraph’s JavaScript interrupts guide documents this behavior.
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The thread ID is how the persistence layer locates the saved thread state. Reuse it to continue that paused execution; a different ID creates a different thread rather than resuming the original one. Use a durable checkpointer for production work that must survive process restarts.
A queue-based review lifecycle
A custom queue can coordinate the waiting period without keeping a worker occupied. One possible design is:
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- Start the graph job. The application creates a job record and a durable mapping to its LangGraph
thread_id, then queues work for a graph worker. - Pause for review. The worker invokes the graph. When
interrupt()is reached, the graph saves its state and returns the interrupt payload. The application records the waiting status and makes the payload available to the reviewer. - Collect a decision. The reviewer approves, rejects, or supplies the requested edit. The application stores that response alongside the job record.
- Queue a resume job. After review, enqueue a resume task containing the same
thread_idand the human response. - Continue the graph. A worker invokes the graph with the resume command. It continues from the saved thread, subject to the replay behavior described below.
This is design guidance for a custom system, not a built-in LangGraph queue feature. The job mapping and review status need durable storage of their own: a checkpoint tells LangGraph how to restore graph state, but your application still needs to know which job is waiting, who should review it, and what response to resume with.
Protect code before the interrupt from replay
When a graph resumes, the node containing the interrupt starts again from its beginning. Statements that ran before interrupt() therefore run again. If those statements send a payment, publish a message, or perform another non-idempotent action, a resume could repeat the effect.
Put the interrupt before side effects where practical. Where an effect must happen before the pause or cannot be moved, protect it with an idempotency key, an outbox pattern, or another application-level safeguard. These are engineering strategies for handling the documented replay behavior; LangGraph does not prescribe one universal side-effect solution. The interrupts guide explains that the interrupted node restarts on resume.
Keep checkpoint storage distinct from application data
LangGraph’s checkpointer stores thread-scoped graph snapshots. A LangGraph store serves a different purpose: it holds application-defined data that can be accessed across threads. Neither should be confused with the queue’s job records or reviewer-facing status. The checkpointer guide describes checkpointing, while the persistence guide covers persistence concepts.
In-memory saver examples are useful for experiments, but the JavaScript persistence guide says in-memory checkpoints disappear after a process restart. For production, choose a persistent checkpointer and decide how long checkpoint and job data should be retained, who can access it, and how it will be cleaned up. A checkpoint can include workflow data that should not be kept indefinitely.
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Choose checkpoint durability for the recovery point you need
The JavaScript checkpointer documentation describes three durability modes. The choice affects when checkpoint writes occur and what state may be available after a failure; it is a trade-off, not a blanket reliability guarantee.
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| Mode | Documented behavior | Trade-off to consider |
|---|---|---|
exit |
Persists when execution exits; it does not save intermediate state for recovery from a mid-execution process crash. | Consider whether losing intermediate progress after a crash is acceptable. |
async |
Writes while the next step runs, balancing performance and durability with a small crash window. | Account for the possibility that a crash occurs before an in-progress write is complete. |
sync |
Writes before the next step begins. | It increases durability at some performance cost. |
These descriptions are from the JavaScript checkpointer guide. Select a mode against the application’s acceptable recovery point and latency needs, then test the failure cases that matter to your deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Retries, duplicates, timeouts, and cancellation
Checkpoint recovery helps restore graph state, but it does not by itself settle every queue or business-process failure. Define how the application handles these cases:
- Duplicate queue deliveries: Make job and resume handling idempotent so a repeated delivery does not apply the same human response or external effect twice.
- Worker retries: Use retry policies for transient errors, while allowing unexpected errors to surface for diagnosis. LangGraph’s “Thinking in LangGraph” guide discusses this distinction.
- Reviewer timeouts: Decide whether a request expires, escalates, remains pending, or is cancelled; record the outcome so it cannot be mistaken for an approval.
- Rejection and edits: Define the values the graph expects for approval, rejection, or corrected input, and validate the reviewer response before resuming.
- Cancellation: Make cancellation state visible to both the application and any queued work so a stale resume does not continue a cancelled job.
Smaller graph nodes can improve observability and reduce repeated work after failures, according to the same LangGraph design guide. Caching and safeguards for external side effects remain application-level decisions.
What LangSmith Agent Server documents
LangSmith Agent Server provides one managed runtime example, not a required blueprint for a custom broker. Its data-plane documentation describes PostgreSQL as the default checkpoint backend and as storage for server resources such as threads and runs. MongoDB can be configured for checkpoint storage, while PostgreSQL remains required for other server resources. Redis supports server-worker communication and ephemeral metadata rather than user or run data; in the documented flow, a Redis list wakes a worker with a sentinel, and the worker retrieves run information from PostgreSQL. Redis communication also supports cancellation and streaming. See the Agent Server data-plane documentation for its scope and details.
The documented Agent Server runs execute in background worker pools. Its autoscaling description says queue workers scale on pending run count, while API servers scale on CPU and memory. Those details apply to Agent Server deployments; they do not mean a custom LangGraph application must use Redis and PostgreSQL in the same arrangement.
Quick Recap
Implementation checklist
- Compile the graph with a persistent checkpointer appropriate to the deployment.
- Give each workflow thread a stable
thread_idand persist the mapping from the application’s job identifier. - Make interrupt payloads JSON-serializable and define the expected reviewer response.
- On resume, use the original thread ID and pass the response through
Command({ resume: value }). - Review every statement before an interrupt for replay safety; protect necessary side effects against duplication.
- Design queue retries, duplicate delivery handling, reviewer timeouts, rejection, cancellation, and checkpoint retention explicitly.
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