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To pause a LangChain agent before a sensitive tool runs, add a human-review gate immediately before the side effect, persist the workflow with a LangGraph checkpointer, and resume it with a decision on the same thread. For a durable record of who approved what and what happened next, also write an application-owned audit event: checkpoints support resumption and LangSmith traces support observability, but the official documentation does not describe either as a complete, immutable approval ledger.
Choose what must be approved
Place approval immediately before a tool that can cause a consequential or hard-to-reverse change: for example, deleting files, sending external email, making a payment, deploying software, or writing to a database. The reviewer should see the proposed tool, its arguments, enough context to understand the request, and the consequence of approving it. Avoid exposing secrets or personal information the reviewer does not need.
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Start by listing the tools available to the agent and identifying which ones can change something outside the conversation. A useful approval boundary is specific: it identifies the action to be reviewed, the point at which the action is about to happen, and the information a reviewer needs to make a decision. A broad instruction such as “review important actions” is harder to test than a rule that a particular email-sending or database-writing operation must stop for approval before it executes.
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There are two useful approval boundaries. A LangGraph interrupt() gives a workflow-level pause that you can place in a graph node. Deep Agents can instead configure interruption on selected tools through interrupt_on. The first gives you control over the workflow location and review context; the second is a tool-specific configuration. See the LangGraph interrupts guide and the Deep Agents human-in-the-loop guide.
Choose based on the control you need. A workflow-level interrupt suits a flow that needs to assemble context, apply business rules, or route a proposed action to a particular review experience before proceeding. A per-tool configuration suits selected tools that should consistently pause when called. These are different approval granularities, not evidence that one approach is universally safer. Whichever you choose, check that every sensitive route to the side effect passes through the gate.
Implement a LangGraph approval gate
LangGraph pauses execution with interrupt() and surfaces a JSON-serializable request to the caller. A checkpointer is required. After a person decides, resume with Command and the same thread_id used to start the run.
- Define the boundary. Put the interrupt in a node immediately before the sensitive tool call. Keep the tool’s side effect after the gate.
- Compile with a checkpointer. The checkpointer saves graph state so a paused execution can be resumed. In production, choose a persistent backend if the workflow must survive process restarts.
- Start the run with a stable thread identifier. Supply the identifier through the configurable runtime settings. The official guide notes, “The
thread_idyou choose is effectively your persistent cursor.” See LangGraph interrupts documentation. - Inspect the pause. When the graph returns an interrupt, read its
__interrupt__data and present the relevant proposed action and context to the reviewer. - Collect and record the decision. Capture the reviewer’s identity, decision, timestamp, and any edits in your application’s audit store before resuming.
- Resume the same thread. Invoke the graph again with
new Command({ resume: decision })and the same thread configuration. Handle the resulting execution or rejection explicitly.
This sequence separates three events that are easy to confuse: the agent proposed an action, a person made a decision, and the workflow subsequently executed or skipped the action. A proposal is not approval, and an approval is not proof that the operation completed. Keep those states distinguishable in the interface and in any application records. The interrupt data is JSON-serializable, which makes it suitable to pass to a caller for review, but your application still decides how to display, validate, and persist the request and response.
The node containing interrupt() begins again from its start when the graph resumes. Therefore, any work before the interrupt may run again. Keep non-idempotent side effects after the approval gate; make preceding work safe to repeat or protect it against replay. This behavior is documented in the LangGraph interrupts guide.
That restart behavior affects how you design the node, not just how you handle failures. Before the interrupt, prefer work that can safely be repeated, such as preparing a review request from existing state. Do not send the email, commit the payment, or perform the write while preparing the request and then assume the interrupt prevents it. The consequential call belongs after the reviewer’s decision. If any preceding operation cannot safely repeat, explicitly protect it against replay or move it behind the gate.
On resume, use the same thread configuration as the paused run. The thread identifier connects the continuation to the saved execution state; starting a separate thread would not be a continuation of that checkpointed pause. Treat the decision as input to the resumed workflow, and make the post-review behavior explicit for both approval and rejection. This is especially important for edited arguments: the reviewer is authorizing the final proposed action, not an earlier draft that the agent has since changed.
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Choose a checkpoint that survives the pause
A checkpoint is saved workflow state, not a permanent approval record. The LangGraph persistence guide states that MemorySaver and InMemorySaver keep checkpoints in RAM, so they are lost if the process restarts. Use a persistent checkpointer for workflows that may remain paused across a restart; the guide identifies PostgresSaver as one option. Consult the LangGraph persistence guide for backend setup and current details.
The practical distinction is durability. An in-memory checkpoint can support a pause while the process remains alive, but it cannot provide the saved state after that process restarts. If reviewers may take time to respond, or a deployment may restart while a workflow is waiting, persistence needs to match that reality. A production workflow that must resume after a restart needs a persistent checkpointer, not just a checkpoint mechanism in name.
Persistent workflow state still has a limited purpose: it lets the graph resume from saved state. It does not, by itself, establish who submitted the resume decision, whether the decision was later changed, or whether the downstream tool completed. The persistence guide’s statement that memory savers store checkpoints in RAM is a useful reminder to check the actual backend rather than infer durability from the fact that a graph can pause.
Even with persistent checkpoints, plan for recovery: the service may restart while waiting for a reviewer, the reviewer may respond more than once, or a resume request may be retried. The approval record and the action’s execution status should let your application distinguish a pending review, an approved action not yet completed, a completed action, and a rejected action. Design writes and retries so a repeated resume cannot silently perform the same external operation twice.
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Set reviewer choices and rejection behavior
Deep Agents’ interrupt_on configuration can enable or disable interruption per tool and restrict the decisions available to a reviewer. Its documented choices support three distinct outcomes:
- Approve: run the proposed action with its original arguments.
- Edit: change the arguments before the action runs.
- Reject: skip the action and provide feedback.
These choices let you make reviewer authority explicit. An approve-only design offers less reviewer control than one that permits rejection; an edit option adds a third path but also means the final arguments can differ from the agent’s original proposal. Configure the allowed choices to match the action and the review policy. The documentation supports enabling or disabling interruption per tool and restricting decisions; it does not imply that every tool must offer all three choices.
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Show exactly what an approval will authorize, including the final arguments if edits are allowed. Define what rejection means in your application: stop the workflow, ask the user a follow-up question, or proceed along a safer alternative. Do not treat rejection as implicit permission to try a different sensitive tool. The Deep Agents guide also warns that substantial argument edits can lead the model to reconsider its approach and potentially make additional tool calls. If that could create new risk, require another approval for the new action.
For example, if a reviewer changes a proposed action substantially, do not assume the original approval covers every possible next step the agent may take. The changed arguments may cause the model to reconsider its approach and make additional tool calls. Decide whether the edited call can proceed as shown, whether new calls need their own review, or whether the workflow should return to a safer path. The key is to make the reviewer’s decision and its scope clear.
Rejection also needs an explicit outcome. “Reject” means skip the proposed operation and provide feedback; it does not decide whether the agent should stop, ask the user a follow-up question, or continue by another method. Choose and test that behavior. In particular, do not let a rejected action become a silent instruction to attempt a different sensitive action without a fresh review where one is required.
Record approval in an application-owned audit store
A checkpoint captures execution state, and a trace captures run activity; neither alone necessarily answers the business question, “Who approved this exact action, under which policy, and what happened?” The official LangChain documentation reviewed here does not define checkpoints or traces as a complete immutable approval ledger, nor does it prescribe a canonical audit-event schema. If you need a durable account of a human decision, create and retain that record in a system your application controls.
A practical approval event can include:
- Workflow or thread identifier and a run or trace correlation ID.
- The proposed action and sanitized arguments shown to the reviewer.
- Reviewer identity, decision, and any approved edits.
- Decision timestamp and relevant policy or prompt version.
- Execution outcome, or the fact that the action was rejected or remains pending.
Use the record to connect what the reviewer saw to what the workflow did. The action name and sanitized arguments help identify the proposal; the reviewer identity, decision, and timestamp capture the human response; and the outcome distinguishes a rejection from an approved action that later completed or remained pending. A policy or prompt version provides context for the rules active when the review occurred. These are practical fields for an application record, not a schema mandated by LangChain.
Record edits as well as the decision label. If a reviewer changes an argument and approves the edited action, preserving only “approved” loses the difference between the agent’s proposal and the action authorized by the reviewer. Likewise, record whether the tool ultimately executed, was rejected, or remained pending. This helps avoid treating approval as synonymous with completion when a later failure, retry, or pause changes the operational outcome.
Keep the recorded decision separate from mutable graph state so later workflow updates do not overwrite what the reviewer authorized. Apply access controls and retention according to your organization’s requirements. Do not label a trace or application record tamper-proof, immutable, or compliant unless the deployed system’s protections have been independently verified.
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The audit event should be stored durably in an application-controlled system with access and retention appropriate to the organization’s needs. The reviewed LangChain sources do not establish a universal retention period, regulatory sufficiency, or a canonical event format. Those decisions depend on the use case and deployed controls. Preserve correlation identifiers so the approval event can be connected to the corresponding workflow and, where available, its execution trace without relying on a trace to stand in for the event.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use LangSmith traces and evaluation for visibility
LangSmith tracing and online evaluation can help teams inspect production runs and monitor agent behavior. The documentation describes filtering online evaluators by tool calls or trace metadata, which can help surface runs involving sensitive tools. This is an observability and monitoring layer, not a substitute for recording a reviewer’s identity and decision in the application’s approval record. See the LangSmith tracing guide and the online evaluations guide.
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Tracing can help answer questions about run activity and agent behavior; the checkpoint helps with execution resumption; and the application event records the human decision. These functions complement one another. A trace may show the tool call or metadata used to filter an online evaluator, but the documentation reviewed here does not define it as a complete, immutable record of reviewer identity, approved arguments, decision time, and outcome. Avoid promising that adding tracing alone creates an approval audit trail.
Tracing destinations depend on deployment. LangSmith’s deployment documentation says Cloud requires tracing to LangSmith SaaS, while Hybrid and Self-Hosted options can disable tracing or select destinations. Confirm the current deployment configuration, data handling, and retention settings before sending sensitive run content to a tracing service; minimize or redact data that is not needed for debugging or evaluation. Details are in the LangSmith deployment options documentation.
There is also a retention and cost trade-off for online evaluation: the LangSmith online-evaluator documentation says runs evaluated online are automatically upgraded to extended data retention, which affects trace pricing. Account for that behavior before enabling continuous evaluation, particularly where the data is sensitive or retention requirements are strict. Check the online evaluations documentation for current terms.
The deployment choice is therefore both an operational and a data-handling decision. Cloud, Hybrid, and Self-Hosted do not have identical tracing behavior according to the deployment documentation: Cloud requires tracing to LangSmith SaaS, while Hybrid and Self-Hosted can disable tracing or select destinations. Verify the current configuration and retention details for the deployment in use rather than assuming that a tracing destination can be changed in every option.
Continuous evaluation is useful for monitoring production behavior, but factor extended data retention into the decision. The online-evaluator documentation says runs evaluated online are automatically upgraded to extended retention, with an effect on trace pricing. That is a concrete cost and retention implication, not a price estimate; consult current product terms and confirm that the resulting data handling fits the workflow before turning on online evaluators.
How the pieces differ
| Mechanism | Primary role | What it does not establish by itself |
|---|---|---|
LangGraph interrupt() |
Pause a workflow at a chosen point, surface a request, and resume with a decision. | A durable, immutable business record of the reviewer and approval outcome. |
Deep Agents interrupt_on |
Configure human approval for selected tools and constrain choices such as approve, edit, or reject. | A universal application audit schema or a guarantee that every downstream action is recorded. |
| LangGraph checkpointer | Save workflow state for resumption; durability depends on the configured backend. | Proof of who made a decision or a record that cannot be changed. |
| LangSmith tracing and online evaluation | Inspect and monitor run behavior, including tool calls and metadata. | A replacement for the application’s approval decision record or an automatic compliance guarantee. |
| Application-owned audit event store | Preserve the decision, reviewer, timestamp, approved action, and outcome according to application requirements. | Automatic immutability or regulatory sufficiency; those depend on the deployed controls and applicable requirements. |
Use the comparison to assign each requirement to the right mechanism. If the need is to stop and resume a graph, use the interrupt and a checkpointer. If the need is to apply approval rules to selected Deep Agents tools, configure interrupt_on as appropriate. If the need is to inspect runs and evaluate behavior, use tracing and evaluation after reviewing deployment data handling and retention. If the need is a durable account of a human decision, write an application-owned event. No one of these functions should be treated as a substitute for the others.
The choice also depends on reviewer control and persistence. A graph interrupt gives workflow-level placement; a Deep Agents tool configuration gives per-tool approval controls and can restrict choices. A checkpoint held in memory is lost on process restart; a persistent backend is the relevant option when the pause must survive one. LangSmith provides operational visibility, but online evaluation brings an extended-retention and pricing consideration. These distinctions help avoid choosing a mechanism because it sounds like “audit” when its documented role is different.
Before enabling the workflow
- Verify that every sensitive tool is behind the intended approval boundary.
- Confirm the reviewer sees the final action and arguments that will execute.
- Test approve, edit, reject, process restart, and repeated-resume paths.
- Check that replaying the interrupt node cannot repeat an unprotected side effect.
- Verify the selected checkpointer persists across the pauses and restarts your workflow must handle.
- Confirm that the audit event is written durably and linked to the corresponding workflow and trace.
- Review trace destination, data exposure, retention, and online-evaluation pricing for your deployment.
Also verify that the human decision is captured before the workflow resumes and that the event reflects any edits the reviewer made. Confirm that rejection has the intended result, and that substantial edits or newly proposed calls do not bypass a gate that should apply to them. Finally, distinguish approved-but-not-yet-executed from completed: the event and workflow state should not imply a successful side effect merely because the reviewer said yes.
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Does a LangGraph interrupt require a checkpointer?
Yes. The interrupt flow requires a checkpointer so the graph can save state while paused and continue later. Resume it with Command and the same thread_id used for the initial run.
Will an in-memory checkpointer survive a restart?
No. The LangGraph persistence guide says MemorySaver and InMemorySaver store checkpoints in RAM, which are lost when the process restarts. Use a persistent checkpointer, such as the guide’s PostgresSaver option, if the workflow must survive a restart.
Is a checkpoint or LangSmith trace an audit trail for approvals?
Not by itself. Checkpoints support execution resumption, and LangSmith traces and evaluations support observability. The official documentation reviewed here does not describe either as a complete immutable approval ledger. Record reviewer, action, decision, time, and outcome in an application-owned audit event store when you need that durable account.
What should happen after a reviewer rejects an action?
Make the behavior explicit in the application. A rejection can stop the workflow, prompt a follow-up question, or route it to a safer alternative, but rejection should not silently authorize a different sensitive action. Deep Agents’ documented reject choice skips the action and provides feedback.
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Deep Agents supports an edit decision when enabled in its approval configuration. Show the final arguments that will run and record the edits. The guide warns that substantial changes can cause the model to reconsider its approach and potentially make additional tool calls, so decide whether those calls need fresh approval.
What is the LangSmith online-evaluation retention trade-off?
The online-evaluator documentation says runs evaluated online are automatically upgraded to extended data retention, which affects trace pricing. Check current deployment settings and terms before enabling continuous evaluation, especially when sensitive data or strict retention requirements are involved.
The Bottom Line
Use a gate to control execution, a persistent checkpointer to resume safely, and an application-owned audit event to preserve the human decision. Treat LangSmith traces and online evaluation as complementary visibility tools, and verify their data-retention and pricing implications for your deployment.
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