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To require human approval before a LangChain agent takes a sensitive action, pause the workflow immediately before the side-effecting tool, present the proposed action to a reviewer, and resume the saved LangGraph execution only after a decision. Use a checkpointer and a stable thread_id so the workflow can resume. For a durable account of who approved what, when, and with what result, also write an approval event to an application-controlled audit store: checkpoints support resumability and LangSmith traces support observability, but the official documentation does not describe either as a complete, immutable approval ledger.
The key design choice is not just whether to interrupt an agent, but where the interruption happens and what evidence you preserve. A reviewer should see the concrete action and arguments that may be executed, while the workflow should remain safe if it pauses, resumes, or has to be restarted. The mechanisms below cover different parts of that design: approval, persistence, observability, and the application-owned decision record.
Choose where approval belongs
Put the review gate immediately before the operation that can change something outside the agent’s reasoning process. Typical candidates include deleting a file, sending an external email, charging a payment method, deploying software, or writing to a database. Reviewing earlier planning steps is usually less useful: the reviewer needs to see the concrete action and arguments that will be executed.
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Think of the boundary as the last point at which the proposed action can be inspected before it causes an external effect. For example, a workflow can prepare an email and show its recipient and content for review, then send it only after approval. A deployment review should likewise occur before the deployment operation, not merely before the agent begins planning. The examples are application design choices; the important principle is to place the gate immediately ahead of the action that matters.
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LangGraph lets you place an interrupt() in a workflow node. This gives you control over the review boundary and the request shown to the human. Deep Agents offers a more tool-specific option: configure selected tools with interrupt_on and define which reviewer decisions are allowed. The first approach fits custom workflows; the second can be convenient when approval behavior should be configured per tool.
| Approach | Approval granularity | Reviewer choices | Best fit |
|---|---|---|---|
LangGraph interrupt() |
A workflow-defined pause, commonly placed before a side effect | Determined by the application’s resume logic | Custom approval flows and review interfaces |
Deep Agents interrupt_on |
Selected tools can be configured individually | Can be constrained to documented choices such as approve, edit, and reject | Tool-by-tool approvals in a Deep Agents harness |
The choice is about control as well as convenience. A workflow-level interrupt lets the application design the point of review and its surrounding logic. Per-tool configuration makes it possible to select which tools require interruptions and constrain the available decisions. Neither choice, by itself, creates a durable business record of what a reviewer decided.
Whichever boundary you choose, show the reviewer enough to make an informed decision: the proposed tool, its sanitized arguments, relevant user context, and the consequence of approval. Avoid exposing credentials, secrets, or personal data that is not needed for the decision. The framework’s ability to surface action names and arguments does not itself enforce data minimization; your application must decide what to display.
Make the approval request specific. “Approve this action?” is less useful than a structured description of the tool, the arguments that will be passed, the relevant context for the request, and what approval will cause. Show the exact edited arguments too, if editing is permitted. Keep the displayed information limited to what the reviewer needs; the framework does not decide which contextual data is appropriate to disclose.
Pause and resume a LangGraph workflow
LangGraph’s interrupt() pauses execution and surfaces a JSON-serializable request to the caller. To resume, invoke the graph with a Command carrying the reviewer’s response and the same thread_id. A checkpointer is required. The LangChain documentation describes the thread identifier as “effectively your persistent cursor.” In practice, treat it as the identifier connecting the paused execution to the later approval response, rather than creating a fresh thread for that response.
- Define the review request. In the node immediately before the sensitive tool, call
interrupt()with a structured, serializable payload. Include only information needed to identify and evaluate the proposed action. A useful payload can identify the action and present its sanitized arguments and relevant context; the details of the reviewer interface are application-specific. - Compile with a checkpointer. The graph must have a checkpointer so it can save execution state at the pause. For local experimentation, an in-memory saver may be sufficient; for workflows that must survive a process restart, select a persistent backend. The checkpointer is part of making the workflow resumable, not a substitute for an application approval record.
- Start or continue the run with a stable thread. Pass the same
thread_idin the configurable runtime settings for the initial run and its later resumption. Treat it as the identifier for this paused workflow, rather than creating a new one for the approval response. The identifier is the link between the saved state and the continuation; losing or changing that link can prevent the intended run from being resumed as planned. - Inspect the interruption. When the invocation pauses, read the returned
__interrupt__data and render the proposal for a reviewer. Do not execute the sensitive tool simply because the graph reached the approval node. The interruption is a request to obtain a decision, not an implicit approval. - Collect and record the decision. Validate the reviewer’s response, preserve it in your application’s approval record if a durable audit history is required, and decide how to handle edits or rejection. Keep the decision and any changed arguments sufficiently clear that the later execution outcome can be associated with what was authorized.
- Resume the same workflow. Invoke the graph again with
new Command({ resume: decision })and the same thread configuration. Continue only according to the validated decision. The response should reflect an actual allowed outcome, not arbitrary data from a caller.
The exact UI for collecting a decision is application-specific. The important runtime contract is the interrupt, the saved checkpoint, and a resume command associated with the same thread. A JSON-serializable interruption payload also means the request needs to be represented in a form the caller can handle; it does not define a review UI, reviewer identity system, or retention policy.
Do not conflate the checkpoint with the review event. The checkpoint allows the graph to continue from saved execution state. The application’s audit event should separately capture the reviewer’s decision and the action it applied to. Those records can be correlated, but they answer different questions.
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Make pause and resume safe to repeat
A node containing interrupt() starts again from its beginning when execution resumes. Any work in that node before the interrupt may therefore run again. This matters if that work has side effects: a repeated write, message, or payment can create duplicate or unintended outcomes.
- Place external side effects after the approval gate, not before it.
- Keep any required pre-approval work safe to repeat, or protect it with explicit idempotency controls.
- Do not treat a pause as a transaction rollback. The interrupt suspends graph execution; it does not undo work already performed.
- Make the final tool execution conditional on the validated approval result, and capture its outcome for the audit event.
The restart behavior is an easy pitfall to miss when a node does more than construct the review request. If it performs a write, sends a message, or otherwise changes external state before calling interrupt(), resumption can run that preceding work again. Keep the pre-interrupt portion limited to work that can safely repeat, or protect it with explicit idempotency controls. Then place the action that actually needs authorization after the pause.
Approval is also not a transaction boundary. An interrupt does not reverse any work that has already happened in the graph. If a workflow has multiple actions, identify which action is gated and avoid implying that approval rolls back or atomically covers unrelated work. Check the decision immediately before the protected execution and record whether the action ran, was skipped, or failed.
For production runs that need to survive a restart, do not rely on an in-memory saver. The LangGraph persistence guide says MemorySaver and InMemorySaver store checkpoints in RAM and are lost when the process restarts. It identifies persistent options such as PostgresSaver. Choose and operate the backend according to your durability, access-control, and retention requirements.
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Constrain approval, edits, and rejection
Deep Agents’ interrupt_on configuration can enable or disable interruptions per tool and specify allowed decisions. Its documented choices include approval of the original arguments, editing arguments, and rejection with feedback. That provides a useful range of reviewer control: an application can allow only the decisions appropriate to a particular tool rather than presenting every option everywhere.
- Approve: allow the proposed tool call to run with its original arguments.
- Edit: change the arguments before execution. Show the reviewer the revised values and make clear what will actually be run.
- Reject: skip the tool execution and provide feedback about what should happen next.
These choices have different operational meanings. Approving the original arguments authorizes the proposal as shown. Editing changes the action being considered, so the reviewer should be able to inspect the revised values before execution. Rejecting means the protected tool call is skipped and feedback is provided; it should not be treated as a successful execution.
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Define rejection behavior deliberately. Depending on the task, the agent may stop, ask the user a follow-up question, or pursue a safer alternative. The official Deep Agents guide warns that substantial edits to tool arguments can prompt the model to reconsider its approach and potentially make additional tool calls. If a review is intended to authorize only one specific operation, validate the post-review action and do not assume that an edited proposal guarantees the rest of the agent’s plan remains unchanged.
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For example, if a reviewer changes a tool call substantially, the agent may reconsider what to do rather than merely executing the edited call in isolation. The guide’s warning means that approval interfaces and downstream logic should be clear about the scope of a decision. Do not imply that approving or editing one proposal automatically authorizes any additional tool calls the agent may make. If the desired result is to stop on rejection, encode that behavior; if a safer alternative or a follow-up question is appropriate, make that path explicit.
Create an application-owned approval audit trail
A checkpoint records workflow state for resumption, and a trace helps make execution observable. Neither is established by the cited documentation as a complete immutable record of a human approval. If you need to answer later who approved an action, what they saw, what they changed, and what happened afterward, write that decision to a durable audit event store controlled by your application.
A practical event can include the following fields, as appropriate to your policy and privacy requirements:
- Workflow or thread identifier and a run correlation identifier.
- The proposed action and sanitized arguments presented for review.
- Reviewer identity, decision, and any argument edits.
- Decision timestamp and the relevant policy or prompt version.
- Whether execution followed, was rejected, or failed, plus the resulting outcome.
- A trace or run identifier that lets an authorized investigator connect the approval event to execution details.
This is implementation guidance, not a canonical LangChain schema. The official documentation does not prescribe a universal set of fields or say that checkpoints or traces alone fulfill the audit need. Select the fields according to what your organization needs to reconstruct: what was proposed, what the reviewer decided, what changed, and whether the protected operation subsequently succeeded, was rejected, or failed.
Preserve the approval event separately from mutable graph state so later workflow updates do not erase the record of what was authorized. Apply access controls and retention rules appropriate to your organization. Do not call a trace tamper-proof or a compliance record unless the deployed system’s controls have been independently verified; the framework documentation does not establish regulatory sufficiency or a universal audit schema.
In particular, distinguish the action presented to the reviewer from the action that ultimately ran if edits are allowed. Retaining the sanitized proposal, the reviewer’s decision, and any revised arguments makes that distinction visible. Correlation identifiers can connect the approval event to execution details without treating the trace itself as the decision record. Keep sensitive information to the minimum necessary in the event store as well as in the review interface.
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Make the store durable and controlled by the application, with access and retention set to match the organization’s requirements. The official documentation does not establish specific retention periods, external retention controls for every deployment, a universal audit schema, or regulatory sufficiency. Those are deployment-specific questions. Avoid claims that a system is immutable, tamper-proof, or compliant unless you have independently verified the actual controls in the deployed system.
Use LangSmith traces for visibility, not as the approval ledger
LangSmith documentation describes tracing and online evaluation of production runs. Traces can help teams inspect execution; online evaluators can monitor runs and filter around particular tool calls or trace metadata. These capabilities complement a human approval process, but they serve a different purpose from recording the business decision itself. An evaluator may help identify behavior to inspect, but it does not replace an application record of reviewer identity, decision, timestamp, edits, and outcome.
Deployment configuration affects where tracing data goes. LangSmith’s deployment documentation says Cloud requires tracing to LangSmith SaaS, while Hybrid and Self-Hosted options can disable tracing or select destinations. Check the current configuration, data handling, and retention behavior for the deployment you operate before sending traces containing sensitive context. The available destination choices vary by deployment option, so do not assume that a setting from one model applies to another.
There is also a retention and cost trade-off for continuous evaluation: LangSmith’s online-evaluator documentation says runs evaluated online are automatically upgraded to extended data retention, which affects trace pricing. Account for that when enabling evaluators on production runs. The source establishes the extended-retention and pricing implication, but it does not provide a universal cost figure; check the current pricing and configuration for the deployment rather than estimating a price from the fact of evaluation alone.
Use tracing and evaluation as operational visibility controls, not as a shortcut for deciding what your approval record must contain. Tracing helps inspect a run and online evaluators can focus on tool calls or metadata. The audit event documents the human decision and its outcome. Keeping those responsibilities separate makes it clearer which system answers operational questions and which one preserves the organization’s account of an approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the right combination
There is no single mechanism in this set that covers every requirement. Choose according to the review boundary, the reviewer’s permitted choices, whether execution must survive a restart, and whether the objective is inspection or a durable approval history.
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|---|---|---|
| Pause at a chosen point in a custom workflow | LangGraph interrupt() |
A structured interruption that the caller can present for review |
| Require review on selected tools | Deep Agents interrupt_on |
Per-tool interruption configuration and constrained reviewer decisions |
| Resume after a pause | LangGraph checkpointer, stable thread_id, and Command |
Saved workflow state and a way to continue the same execution |
| Survive process restarts | Persistent checkpointer, such as the documented PostgresSaver option |
Checkpoint persistence beyond the running process’s RAM |
| Inspect execution and monitor quality | LangSmith tracing and online evaluation | Execution visibility and evaluation workflows, with deployment and retention considerations |
| Prove who approved what and the outcome | Application-controlled audit event store | A durable approval decision record designed around your organization’s requirements |
The distinction between in-memory and persistent checkpointing is about durability: the former keeps state in RAM and is lost on process restart, while a persistent option is appropriate when a paused workflow must survive a restart. The distinction between a checkpoint and a trace is about function: one supports resumption, the other supports inspection and evaluation. Neither distinction changes the need for an application-owned event when your requirement is a durable account of a reviewer decision.
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Guidance in this article reflects the official documentation accessed on October 13, 2026. The reviewed sources do not establish publication dates for the referenced pages, benchmark figures, adoption statistics, or a universal compliance guarantee. Verify current product configuration and data-handling behavior for your deployment before relying on a particular tracing destination or retention setup.
Frequently Asked Questions
Does LangGraph’s interrupt() require a checkpointer?
Yes. A checkpointer is required to save the execution state at the pause. On resumption, pass a Command with the reviewer’s response and use the same thread_id as the original run.
Can an in-memory checkpointer preserve an approval pause across a process restart?
No. The LangGraph persistence guide says MemorySaver and InMemorySaver store checkpoints in RAM and are lost when the process restarts. Use a persistent checkpointer, such as the documented PostgresSaver option, when production workflows must survive a restart.
What happens to a node after its interrupt is resumed?
The node containing interrupt() starts again from its beginning. Keep external side effects after the approval gate and make any work before the interrupt safe to repeat or explicitly protected against replay.
Can a reviewer edit or reject a proposed tool call?
Deep Agents’ interrupt_on can constrain decisions to documented choices such as approve, edit, and reject. Approval runs the original arguments, editing changes them, and rejection skips execution and provides feedback. The application should make the behavior after rejection explicit.
Are LangGraph checkpoints or LangSmith traces a complete approval audit trail?
The official documentation does not describe either as a complete immutable approval ledger. Checkpoints support resumability and traces support observability. For a durable record of reviewer, action, decision, time, edits, and outcome, write an approval event to an application-controlled audit store.
Does LangSmith online evaluation affect retention or cost?
The online-evaluator documentation says runs evaluated online are automatically upgraded to extended data retention, which affects trace pricing. Check current pricing and deployment configuration before enabling continuous evaluation.
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The Bottom Line
Put approval immediately before the sensitive side effect, resume the same saved LangGraph thread only after validating the reviewer’s decision, and make the flow safe to replay. Use a persistent checkpointer when a pause must survive a restart. Treat checkpoints as resumable state and LangSmith traces as visibility—not as the approval ledger—and preserve the decision and execution outcome in a durable, application-owned audit event store.
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