Run Cancellation Causes Loss of Streamed State Not Yet Persisted as a Checkpoint

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This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
I added a clear and detailed title that summarizes the issue.
I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.

Example Code

# Minimal Example

# Start a streaming LangGraph run
for chunk in graph.stream(input, config, stream_mode="values"):
    process_streamed_value(chunk)
    if should_cancel():
        cancel_run()

# After cancel, send new message and sync UI:
# => streamed data between last persisted checkpoint and cancel is lost

# Full minimal code can be provided if desired (let me know)

Error Message and Stack Trace (if applicable)

Description

Issue Summary

(I am using langgraph platform/ langgraph api)

When canceling a LangGraph run during streaming, the most recent "in-progress" streamed state is not persisted to the backend checkpoint database. As a result, when a new message is sent (triggering the front end to sync with the backend), any streamed data—in some cases, even several seconds or more of streamed output—is lost. The backend reflects only the last completed+persisted checkpoint, not the full state the user saw up to the moment of cancellation.

Steps to Reproduce

Start a LangGraph run with streaming output (e.g., a chat session, a long node, or token-by-token streaming).
Observe the front end as streaming updates/tokens arrive and are rendered.
Cancel/abort the run before it naturally completes or reaches a checkpoint.
Send a new message or re-sync the UI, causing the front-end to fetch authoritative state from the backend.
Notice that all the streamed data since the last checkpoint is missing.

Expected Behavior

On cancellation, the in-progress streamed state (what the user saw up to the abort/cancel) should be persisted to the backend checkpoint database, at least as a "partial"/"aborted" state.
Resuming the conversation (or rendering the thread after a cancel) should match what the user last saw, not rollback to the last completed checkpoint.

Actual Behavior

The backend does not persist the streamed/in-memory state at the moment of cancellation.
The front-end loses all non-checkpointed streamed data on sync, since it relies on the backend as the source of truth after a cancel.
Practically, this means that users can see output for several seconds (or longer), but after canceling and sending a new message, that output disappears.

Why This Matters

Users see and may even start reading output as it’s streamed, but lose it entirely if they cancel—even if a majority of the output was streamed.
Creates an inconsistent, surprising user experience for chat applications or any app with incremental streaming.
Makes it difficult to build features that require exact “what you saw is what is saved” continuity.

Technical Insight

Currently, checkpointing only happens at full run/task completion, not on cancel/abort/stream end events.
There is no mechanism for a "flush/persist to checkpoint right now" on cancel/disconnect.
Cancelling a run always discards the difference between the last checkpointed state and what was streamed to the client before cancellation.
This is especially problematic for apps that adopt optimistic/real-time UI updates based on the stream.

Feature Proposal

Add an option (or enable by default) to persist the latest/active state as a special checkpoint when a run is canceled or the streaming connection is closed. This could be a "partial," "aborted," or "interrupted" checkpoint.
Optionally, provide a callback or hook for custom flush/final persistence on abort.

Additional context

If you need more information or a deeper technical dive/code pointers, I’m happy to collaborate or prototype a solution!

System Info

OS: macOS 14.5 (ARM64, Apple M1)
LangGraph version: 0.3.31
Runtime: FastAPI, Python 3.11
Deployment: both dev and cloud, similar results

Ibrahim (@ibbybuilds)

I am running into same issue and was able to pinpoint what was the error , at least in our case. For some reason, the next message sent my user created a new branch. To get the messages, frontend SDK starts a BFS search from the history and looks for a state with no parent i.e. state.parent_checkpoint == null.

However, because it becomes a branch off, the parent checkpoint is not null and it leads to not parsing any messages. This code is written in stream.js in method getBranchSequence.

I confirmed this behaviour by modifying code for a static checkpoint and then it worked.

Ibrahim (@ibbybuilds) - Can you confirm if your frontend is getting the state history by checking the network tab? That's how I reached here.

Someone from Langgraph, can you probably confirm if this is a server issue or client SDK issue? I believe the branching should never have happened at first place. And even if it did, why is the parent checkpoint not null for the first message?

Here is the link to run in Langsmith:
https://smith.langchain.com/public/78163885-e653-4f9f-84c3-e05f4c65ab57/r

FYI, I can see the same issue in Studio. This thread does not render anything in Langgraph Studio.

function getBranchSequence(history) {
    console.log("Ishmeet: getBranchSequence: history", history);
    const childrenMap = {};
    // Short circuit if there's only a singular one state
    // TODO: I think we can make this more generalizable for all `fetchStateHistory` values.
    if (history.length <= 1) {
        return {
            rootSequence: {
                type: "sequence",
                items: history.map((value) => ({ type: "node", value, path: [] })),
            },
            paths: [],
        };
    }
    // First pass - collect nodes for each checkpoint
    history.forEach((state) => {
        if (state.parent_checkpoint_id == "1f079219-9ab4-6305-800e-8e4713fc3cb4") {
            console.log("Ishmeet: state", state);
            state.parent_checkpoint_id = null;
            state.parent_checkpoint = null;
        }
        const checkpointId = state.parent_checkpoint?.checkpoint_id ?? "$";
        childrenMap[checkpointId] ??= [];
        childrenMap[checkpointId].push(state);
    });
    const rootSequence = { type: "sequence", items: [] };
    const queue = [{ id: "$", sequence: rootSequence, path: [] }];
    const paths = [];
    const visited = new Set();
    while (queue.length > 0) {
        const task = queue.shift();
        if (visited.has(task.id))
            continue;
        visited.add(task.id);
        const children = childrenMap[task.id];
        if (children == null || children.length === 0)
            continue;
        // If we've encountered a fork (2+ children), push the fork
        // to the sequence and add a new sequence for each child
        let fork;
        if (children.length > 1) {
            fork = { type: "fork", items: [] };
            task.sequence.items.push(fork);
        }
        for (const value of children) {
            const id = value.checkpoint?.checkpoint_id;
            if (id == null)
                continue;
            let { sequence } = task;
            let { path } = task;
            if (fork != null) {
                sequence = { type: "sequence", items: [] };
                fork.items.unshift(sequence);
                path = path.slice();
                path.push(id);
                paths.push(path);
            }
            sequence.items.push({ type: "node", value, path });
            queue.push({ id, sequence, path });
        }
    }
    console.log("Ishmeet: getBranchSequence: rootSequence", rootSequence);
    console.log("Ishmeet: getBranchSequence: paths", paths);
    return { rootSequence, paths };
}

Ishmeet Singh Sethi (@sethi-ishmeet)

Ishmeet Singh Sethi (@sethi-ishmeet) I think this is mainly a backend issue. When a run is canceled, the current in-progress streamed state (tokens/messages that the user has already seen in the UI) is never written to the checkpoint database. When the next message is sent, the backend starts from the last saved checkpoint — which could be a human message or an intermediate AI message — instead of the full state the user saw before cancel.

The result is that the backend “history” is missing streamed-but-not-saved output. The frontend may optimistically display that output in real time, but it’s not persisted. On the next message, the UI syncs with backend history and effectively “rolls back” to the last checkpoint, losing several seconds or more of visible output.

Flow of the issue:

User starts streaming run
   |
   v
[ Frontend ]  ------------------------->  [ Backend ]
 Receives streamed AI tokens/messages      Saves checkpoints only at full run completion
 Displays them to user                     (No save on cancel)
   |
   v
User cancels run
   |
   v
[ Frontend ]  — still has the latest streamed messages in memory  
[ Backend ]   — last checkpoint = older state (missing streamed content)  

Later...
User sends new message
   |
   v
Frontend requests history from backend
   |
   v
Backend returns only what it has saved (missing partial streamed messages)
   |
   v
Frontend now shows a "rollback" to last checkpoint
(User loses several seconds or more of streamed output)

Workaround I used on the frontend:

Keep the streamed state in memory until the next user message is sent.
Fetch the latest state history from the backend (starting from its last checkpoint).
Compare it with the local streamed state.
Identify missing AI messages.
Send a combined sequence where those missing messages appear before the new user message, for example:
[missing_ai_1, missing_ai_2, new_user_message].

Ibrahim (@ibbybuilds)

Ibrahim (@ibbybuilds) Will your workaround work on page reload? I don't think so... Harrison Chase (@hwchase17) Can you please review this? Brace Sproul (@bracesproul) Nuno Campos (@nfcampos)

@ofried-acutis yes it won't work on page reload but you can use local storage to work around that or any prefered persistant storage.

Ibrahim (@ibbybuilds)

Ibrahim (@ibbybuilds) Ok gotcha, thanks for the quick reply. This would mean basically persisting to storage every token streamed as soon as it's streamed... ok thanks!

Hi all, I ran into this recently as well.

TLDR is that Pregel.astream() persists state using loop.after_tick() and intermediate checkpointer aput_writes() calls scheduled via AsyncBackgroundExecutor, which are normally drained on a clean exit. When a run is cancelled, AsyncPregelLoop.__aexit__ creates exit_task = asyncio.create_task(self.stack.__aexit__(...)) and awaits it. If that await raises CancelledError, it re-raises and only attaches exit_task to the exception args. Since exit_task is an asyncio.Task, it can continue running after the caller is cancelled, but if the caller does not await it and the event loop/process shuts down before it finishes draining background persistence tasks, pending aput/aput_writes may not flush.

One possible fix is to shield and await the exit cleanup for a small bounded time before re-raising CancelledError, so already-enqueued writes are more likely to commit. But I’m not sure if we want this as default behaviour (with a small timeout) or opt-in using a flag. I have some changes made locally and can draft a PR once confirmed.

any updates on this????

any updates on this????

I don’t think so, I haven’t seen much public activity from the maintainers in the past couple of months.

GAUTAM V DATLA (@gautamvarmadatla)

GAUTAM V DATLA (@gautamvarmadatla) we are actively looking into this as we are rethinking some of our streaming primitives. Stay tuned.

just don't use langchain / langgraph. Models are good enough now to just write all the code with no external dependencies (like langgraph)

damn langgraph has so many bugs..
in anthropic this is even worse experience it will cause corrupted thread, tool_use_id wont match tool_result

For agent reliability, cancellation should probably be modeled as a state transition with a bounded persistence contract, not only as an asyncio control-flow event.

The key operational question is: after cancellation, which writes are guaranteed durable and which writes were abandoned?

A useful event shape could be:

cancellation_requested
writes_enqueued_count
writes_flushed_count
flush_timeout_ms
checkpoint_status: durable | partial | abandoned
resume_recommendation: safe | inspect_state | restart_from_previous_checkpoint

That would make the failure mode visible to operators and to any recovery UI. Without that distinction, a cancelled streamed run can look recoverable while its latest state never actually reached the checkpointer.

test(langgraph): assert resume re-runs only the cancelled task

LIUwenhao (@LB623)

Thanks — and your invariant is a better statement of the bug than mine was. "State survived ≠ termination evidence survived" is exactly it: the durable values were never wrong, what disappeared was the occurrence-level fact that p2 ran at all.

I ran your formulation as an executable check against both trees, clause by clause, using precisely the negative control you described.

Unmodified main (644815f):

durability=exit
   [PASS] p1 outcome remains committed
   [FAIL] p2 cancellation remains observable
   [PASS] p1 not misreported as cancelled
   [PASS] resume re-runs only p2
durability=async    all 4 PASS
durability=sync     all 4 PASS

INVARIANT VIOLATED

With the fix branch:

durability=exit     all 4 PASS
durability=async    all 4 PASS
durability=sync     all 4 PASS

INVARIANT HOLDS IN ALL THREE MODES

Worth noting what the failure isolates to: exactly one clause, in exactly one durability mode. The other three clauses hold on main even in the broken case — p1's work stays committed, p1 is not mislabelled, and resume still re-runs only p2. So the defect is precisely the loss of termination evidence and nothing else, which is a good sign the fix is not papering over a wider correctness problem.

The regression test on the branch already asserts three of your four clauses (p2 error present, p1 error absent, p1's value committed) parametrized over all three modes. The one it does not assert is "resume re-runs only p2". That is worth adding on your framing — it is the clause that connects the durable marker to the behaviour a user actually cares about (not paying twice for work that already completed), and it is the one most likely to regress silently if someone later changes how pending writes are replayed. Added in b336f72 on the branch: the test now tracks which nodes execute, clears that record after the cancel, resumes, and asserts only the cancelled node re-ran. Still mutation-checked, and the suite is 313 passed vs 310 on main.

Agreed on the two-boundary split, and I'd put it the same way:

core: cancellation occurrence → durable checkpoint evidence (this fix; now has an executable invariant)
platform: durable checkpoint evidence → externally published run status (still open, still not inspectable from OSS)

The useful property of that split is that the platform boundary becomes testable only after core can prove the first half. Before this fix, an Agent Server integration test asserting "interrupted is not published before the cancellation is durable" could not have distinguished a Platform ordering bug from core never persisting the fact at all — under durability="exit" there was nothing durable to wait for.

Status: the PR (#8634) was auto-closed by the require-issue-link bot because I'm not assigned to this issue. The branch and verification stand; it reopens automatically once a maintainer assigns it. LIUwenhao (@LB623) and EMAN YOUSAF (@Eman-Yousaf) both asked for assignment before I did, so I'm still happy to hand this to either of them rather than duplicate.

Ayush Baluni (@aayushbaluni)

Ayush Baluni (@aayushbaluni) I don't have a PR in flight — please go ahead. Thanks for the thorough write-up and for checking first.

LIUwenhao (@LB623)

LIUwenhao (@LB623) Thanks — appreciated, and noted. I'll carry it.

One mechanical note for whoever assigns this, because it affects what a reviewer would actually see.

I pushed b336f72 (the fourth invariant clause Aleksey Safonov (@safal207) proposed — asserting a resume re-runs only the cancelled node) after the bot closed #8634. GitHub does not sync a closed PR's head, so the PR still records only 7045d72:

commits GitHub currently sees on PR #8634:
   7045d72  fix(langgraph): persist cancellation record under durability='exit'

fork branch tip:
   b336f72  test(langgraph): assert resume re-runs only the cancelled task

The branch is correct; the closed PR's view of it is one commit behind.

Reading reopen_on_assignment.yml, the reopen itself is unaffected — it matches on label:missing-issue-link plus a Fixes #5672 reference in the body, and #8634 still has both. The workflow's own comment flags exactly this case for the check re-run:

if the contributor pushed new commits while the PR was closed, head.sha won't match the SHA of the original failed run [...] any push after reopen triggers a fresh require_issue_link run against the new SHA

So on assignment it should reopen, pick up b336f72, and re-run cleanly. If for any reason it comes back showing a single commit, ping me and I'll push an empty commit to force the sync rather than leaving a reviewer looking at the older diff.

Current state of the branch, for the record:

_loop.py +30, test_pregel_async.py +73 across the two commits
4/4 clauses of the invariant hold in all three durability modes; on unmodified main exactly one clause fails in exactly one mode
mutation-checked: removing only _stage_cancelled_task_writes(exc_value) fails the exit case while async/sync stay green
tests/test_pregel_async.py: 313 passed vs 310 on main; full suite 2012 vs 2009, 0 failed
ruff check / ruff format clean

Branch: https://github.com/aayushbaluni/langgraph/tree/fix/5672-persist-cancellation-exit-durability

Ayush Baluni (@aayushbaluni)

Ayush Baluni (@aayushbaluni) — this is exactly the kind of localization I was hoping the invariant would force.

The strongest result here is not just that the fix gets 4/4. It is that unmodified main fails one clause, in one durability mode, while the mutation removes exactly the staging call and reproduces that same failure. That makes the defect much narrower than “cancellation persistence is flaky”: the durable state is intact, replay selection is intact, and the missing fact is specifically the cancellation occurrence evidence under durability="exit".

b336f72 is important because it closes the behavioral side of the contract too. I would keep that fourth clause permanently: resume re-runs only the cancelled node is where a persistence bug turns into duplicate work / duplicate cost for the user, and it is exactly the sort of property that can silently regress while the checkpoint contents still look plausible.

Now that core can prove its half, I think the platform boundary can be stated as a separate executable invariant when that surface is testable:

published termination status
    ⇒ corresponding durable termination evidence already exists

with the useful negative control:

no durable cancellation evidence
    ⇒ do not derive/publish cancellation from a transient occurrence alone

That gives the full chain a clean proof split:

cancellation occurrence
    → durable checkpoint evidence        [core]
    → externally published run status    [platform]

Before this fix, the second test could not tell whether platform ordering was wrong or core had simply dropped the fact. After this fix, those two failure classes become distinguishable.

And yes — no objection from me to you carrying #8634. The branch evidence is already unusually strong; once assignment reopens/syncs the PR, the reviewer should have a very crisp regression story to inspect.

Agreed — carrying the frontier kind explicitly is cleaner. Freezing the model here and moving to the executable crash/recovery fixture sounds like the right next step. Thanks for pushing the distinction to a precise boundary.

scout: the finder kept offering a lead the box would always refuse

One follow-up from the checkpoint/recovery work in this thread: after testing the same explicit-evidence projection contract across several agent runtimes, I found that a single PASS/FAIL score hides an important distinction.

For LangGraph, the source-pinned checkpoint/state boundary can host the complete witness sequence and replay it deterministically, but that does not automatically make the underlying evidence append-only or make every LangGraph application conformant. Those are separate claims supplied at different boundaries.

I wrote the comparison up here:

https://safal207.github.io/RESONANCE/conformance-is-a-vector-not-a-verdict.ru.html

The compact rule is:

projection conformance ≠ persistence ≠ append-only evidence ≠ framework-wide safety

I think this is directly relevant to #5672 because the cancellation problem is exactly where those layers separate: what was visible, what became durable, what can be replayed, and what the host is allowed to treat as authoritative are not the same fact.

The LangGraph row in the article is intentionally bounded to a pinned checkpoint/state surface; it is not a framework ranking or a claim that the issue here is solved.

I ran a local follow-up specifically on the state boundary behind this behavior using Python 3.11.15, LangGraph 1.2.9, and AsyncSqliteSaver (no model / no external API).

What I observed across 16 deterministic stream cases:

A long-running node emitted 3 custom stream updates that reached the consumer, but when the consumer stopped before the node returned, graph state restored 0/3 of those visible updates across all durability inputs I tested.
Modeling the same progress as completed graph steps restored 3/3 visible updates.
durability=sync changes write timing for completed graph-state changes; it does not turn unfinished node-local streamed progress into a state update.
interrupt() is a different boundary: prior completed state is persisted, but code before the interrupt can run again when the node resumes.

So for applications that promise the user that already-rendered partial output will survive Stop/cancel, I would separate three things: UI stream, an application-owned event ledger for promised partial output, and LangGraph checkpoints for completed workflow state.

Important scope note: this is not a reproduction of the exact LangGraph Platform/API cancellation implementation originally reported here on 0.3.31. It reproduces the local semantic boundary that can produce the same rollback symptom.

Detailed matrix and test shape: https://www.xbstack.com/en/ai/langgraph-cancel-run-streaming-checkpoint-state-loss/?utm_source=github&utm_medium=referral&utm_campaign=langgraph_cancel_state_loss&utm_content=issue_comment

This is useful evidence, and I think it both confirms the semantic boundary and narrows the original Platform/API claim in an important way.

The 0/3 result makes the core distinction concrete: a custom stream update can be observable by the consumer without ever becoming a graph-state transition that the checkpointer is entitled to restore. durability="sync" therefore cannot solve that class of loss; it only changes persistence timing for state changes that have actually completed.

That suggests the cancellation contract should not be phrased as “flush the current checkpoint” alone. It needs to reconcile two different frontiers:

visible_frontier = highest stream event delivered to the consumer
state_frontier   = highest completed graph-state transition durably checkpointed

If visible_frontier > state_frontier at explicit cancellation, the runtime/application needs durable evidence for that gap (event ledger / cancellation receipt / equivalent) before later treating the older checkpoint as the full authoritative history.

Your interrupt() observation adds another useful constraint: because code before the interrupt may execute again on resume, any application-owned event ledger should give emitted events an occurrence/sequence identity and make replay/idempotency explicit. Otherwise recovery can preserve the user-visible prefix but accidentally duplicate it on resume.

So I would now state the issue more narrowly as:

the local tests reproduce the visibility-vs-durability boundary that can produce the rollback symptom;
they do not by themselves prove the exact Platform/API cancellation path from the original 0.3.31 report;
but they do rule out “just use synchronous durability” as a general fix for unfinished node-local streamed output.

That seems like a materially stronger and more falsifiable formulation than the original blanket claim that cancellation simply fails to checkpoint the latest streamed state.

Agreed. Adding run-generation / attempt identity closes an ambiguity that a plain C < P < T ordering still leaves open: recovery should never be able to satisfy a later terminal transition with a receipt from an earlier run occurrence.

For the local visibility-vs-durability fixture I would carry the same identity into any application-owned event ledger as well: run_id + run_generation + stream_seq. That gives replay an explicit idempotency key and prevents a resumed node from duplicating already-visible partial output.

I would also keep transport interruption on a separate causal branch, as you described. My local test still only demonstrates the semantic boundary behind the rollback symptom; it does not independently prove the exact LangGraph Platform/API cancellation path from the original report, so I will keep that scope distinction explicit.

The visible-vs-durable frontier in this thread seems related to a broader execution-evidence question I’m trying to validate.

Would an application benefit from a framework-neutral execution ledger that records a stable logical action/run identity and separately tracks “observable”, “durably recorded”, and “outcome unknown” states across cancel/resume boundaries?

The ledger would not try to replace LangGraph checkpoint semantics or infer recovery automatically; it would only preserve provenance so the application can reconcile later.

Would that be useful production infrastructure, or would you consider it redundant because this state should remain entirely application/LangGraph-owned?

I think there is a useful boundary here, but I would make the ledger earn its existence by keeping its claim deliberately smaller than another source of truth.

For this issue, the interesting case is a fact that is already observable to the application but is not yet represented by a durable LangGraph state transition.

So I would require the ledger to preserve distinctions such as:

OBSERVED != DURABLY_RECORDED
DURABLY_RECORDED != OUTCOME_CONFIRMED
UNKNOWN remains UNKNOWN until new evidence resolves it

with stable occurrence identity across cancel/resume — e.g. logical action/run + attempt/generation + event sequence — while leaving checkpoint semantics, retry, and reconciliation to LangGraph/the application.

A useful negative control would be:

event becomes observable
-> cancel before corresponding state is durably checkpointed
-> resume

The ledger should still be able to say “observed, not proven durable/outcome unknown”; it must not silently upgrade that fact to durable or succeeded simply because execution later continues.

If a framework-neutral layer cannot preserve that distinction more reliably than ordinary application state, then I would consider it redundant. If it can, that seems like a real execution-evidence boundary rather than a second checkpoint system.

Thank you both. The visible/durable/confirmed separation is the right boundary for my Agent Session Bridge work. I’ll keep the record append-only and identify each occurrence by logical run/action, attempt or generation, and event sequence; after cancellation before checkpoint, it should preserve “observed, not proven durable, outcome unknown” and leave checkpoint and recovery semantics to LangGraph/the application. I’ll add this as a concrete LangGraph negative-control case and report back with a minimal repro or results.

I added a runnable local negative control in TFB PR #42. It uses LangGraph 1.2.12 with AsyncSqliteSaver 3.1.1 and durability="sync", with no model or hosted API.

The consumer received a synthetic custom stream event; I cancelled before the node returned; then SQLite checkpoint readback still contained only the prior state, without the partial event. The recorded classification is OBSERVED / NOT_PROVEN / UNKNOWN, and the observation timestamp is left absent. The result and reproduction are in fixtures/runtime/langgraph-cancel-negative-control.result.json and experiments/langgraph_cancel_negative_control/repro.py.

Scope: this demonstrates the local stream-versus-checkpoint evidence boundary. It does not reproduce the hosted LangGraph Platform/API cancellation path reported here, establish a platform defect, or characterize other versions/checkpointers.

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Run Cancellation Causes Loss of Streamed State Not Yet Persisted as a Checkpoint #5672

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Run Cancellation Causes Loss of Streamed State Not Yet Persisted as a Checkpoint #5672

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sethi-ishmeet commented on Aug 14, 2025

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aayushbaluni commented on Aug 18, 2026

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