LibreChat/api/server/controllers/agents/client.js
Danny Avila 7bb6651883
🛑 feat: Preemptive Steer - Backend Interrupt & Steer (#14518)
* 🛑 feat: Preemptive Steer — server half (Interrupt & Steer, PR 2 of 3)

Lets the steer route ask the generating replica to seal its live model
stream at the next provider-safe boundary instead of waiting for a tool
step. The run is never aborted, job status never changes, the partial
answer is kept, and generation resumes in the same assistant message
after the injected steer. Consumes the SDK seam in @librechat/agents
(danny-avila/agents#335, #346).

Transport: IEventTransport gains a fenced emitPreempt/onPreempt pair
beside abort. RedisEventTransport fans PREEMPT out on the SAME events
channel and subscription (no new connection, key, or subscribe call);
onPreempt returns a registration-scoped unsubscribe with the same
replacement-safe state-identity guard onAbort uses. InMemory implements
neither — single-process preempt lives entirely in the runtime set.

Runtime state: RuntimeJobState carries the per-generation request set,
createdAt-fenced and capped at STEER_QUEUE_MAX_DEPTH, plus a bounded
`cleared` tombstone so a late cross-replica arm cannot resurrect a
request whose steer already drained. registerPreemptSubscription
mirrors the abort registration's double fence (runtime identity +
generation createdAt); releaseAbortSubscription retires BOTH listeners
and the armed set, so every terminal path drops preempt state for free.
Public surface: requestPreempt (arm + fenced publish, never a rejection
surface, never touches job status), isPreemptRequested (O(1)
level-triggered poll), noteSteersRemoved (drain/cancel bookkeeping +
fenced clear), clearPreemptRequests (empty-boundary disarm).

One drain body, two boundaries: createSteerDrainHook (PostToolBatch)
and createSteerPreemptBoundaryHook (PreemptBoundary) share
drainAndBuildInjections, so the two injection sites cannot drift — the
SDK's provider-safety argument rests on identical HumanMessage shapes.
The shared body builds injections incrementally under a swallow-all
catch (a mid-loop throw still injects what was applied — those parts
are already persisted), clears preempt requests in finally, and
disarms the generation when a boundary drains nothing.

Request path: POST /chat/steer accepts preempt: true. The guard ladder
is unchanged in order and in every status code. A preempt request is
NEVER a rejection reason — without the capability the steer still
enqueues and the 202 echoes preempt: false. Armed strictly after a
successful enqueue; cancel disarms. The capability is read from the
OWNING replica's recorded `preemptCapable` rather than the route
replica's own SDK probe, so a rolling deploy cannot label a steer
"interrupting" that the older owner will only inject at a tool step.

Durable label: SteerQueueItem.preempt → TPendingSteer.preempt, so a
parked/claimed/replayed chip keeps its wording.

Run wiring: createRun registers the PreemptBoundary hook and threads
RunConfig.preemption, both gated on isSteerPreemptSupported() — a
separate probe from isSteeringSupported(), so the client affordance can
never arm against an SDK that only injects at tool boundaries.
buildSteerWiring builds both hooks from one shared closures object, so
preemption survives HITL pause/resume for free.

Honest finalization: an empty preempt boundary persists and emits with
unfinished: true — the same contract an abort gets — re-marked
explicitly because BaseClient has already saved the row as
unfinished: false by that point.

Not changed: no new job status, store method, Lua, SSE event type,
endpoint, or authorization surface. abortJob, completeJob,
transitionStatus, closeAndDrainSteers, getResumeState, emitChunk,
applySteerPart and the whole abort path are untouched.

Tests: 120 packages/api steering specs (preempt lifecycle, tombstone,
fences, caps, terminal release, both-boundary drain parity,
level-triggered poll, request/cancel arming, owner-capability
degradation) plus 5 in api for buildSteerWiring gating, and 2
Redis-gated cross-replica transport specs.

* 🔒 fix: Codex round 2 — evict tombstones, scope the empty-boundary disarm, honest resumes

All four server findings were fresh consequences of the round-1 fixes,
which is the review doing exactly what it should.

- Tombstone cap refused new entries instead of evicting. Every drained
  or cancelled steer is tombstoned, not just preempting ones, so a
  generation that processed 20 steers exhausted the set and the
  late-arm race resurfaced silently. Now evicts oldest-first (Set
  iteration is insertion-ordered), with the budget named
  PREEMPT_TOMBSTONE_MAX rather than an inline expression.

- The empty-boundary disarm I added in round 1 wiped the generation's
  ENTIRE armed set. A second steer can enqueue and arm between the
  atomic drain returning empty and the disarm running — that arm is
  backed by a live, uninjected queue item and must survive. The drain
  now snapshots the armed ids BEFORE draining
  (getArmedPreemptIds) and clearPreemptRequests takes an explicit id
  list instead of clearing everything.

- HITL resume finalized with a hardcoded unfinished: false. The
  boundary hook is re-registered on resume via buildSteerWiring, so a
  resumed segment can end on an empty preempt boundary exactly like a
  fresh one; finalizeResumedTurn now reads getPreemptStats() and the
  halt reason, matching the normal request path.

- Ownership moves on resume, so the job's recorded preemptCapable must
  describe the replica that will actually generate. Refreshed before
  resumeCompletion; a job created on a capable replica that resumes on
  an older one during a rolling deploy no longer acknowledges steers as
  interrupting.

Tests: +3 (scoped disarm sparing a post-snapshot arm, oldest-first
tombstone eviction, id-list disarm). 122 packages/api steering specs
green.

* 🚨 fix: Codex round 3 — deserialize preemptCapable from Redis (feature was dead under Redis)

The P1 here is the most consequential defect in the whole feature, and
it was introduced by round 1's own capability fix.

- `RedisJobStore.serializeJob` writes booleans generically, so
  `preemptCapable` reached Redis — but `deserializeJob` is an EXPLICIT
  field map and had no line for it. Every `getJob()` therefore dropped
  the flag, `job.metadata.preemptCapable` was always undefined, and
  `handleSteerRequest` computed `preemptArmed: false` unconditionally.
  Interrupt & steer would have silently degraded to ordinary
  tool-boundary steering in EVERY Redis deployment — i.e. the feature
  shipping as a no-op in production while passing every in-memory test.
  Now deserialized, with a round-trip assertion in the metadata spec
  that fails (`Received: undefined`) against the unfixed store.

- The resume capability refresh moved from just-before
  `resumeCompletion` to immediately after `approvals.resolve` claims
  the run. That call already flips the job back to `running`, so the
  steer route accepts requests from that instant; leaving the refresh
  135 lines later (across the whole client reconstruction) left a real
  window where a steer read the PREVIOUS owner's capability. Not the
  fully atomic transition Codex suggested — that reaches into the
  approvals Lua — but it shrinks the window from seconds to one await,
  which is proportionate for a label-accuracy issue.

Refuted: "avoid triggering preemption inside subagents". The premise —
that the run-wide poll can seal a subagent stream — does not hold
against the shipped SDK. Child graphs are constructed with
`subagentScope: true` (SubagentExecutor) and `preemption` is NOT
propagated into child inputs, while `canClaimPreemptSeal()` requires
`!subagentScope && preemption != null`. Both conditions fail
independently, so a subagent can never claim a seal and the boundary
cannot fire with `agentId` set. The `input.agentId != null` guard in
the hook is defensive depth, not the thing standing between us and the
described failure.

140 packages/api specs green.

* 🔁 fix: Codex round 4 — re-arm durable interrupt steers when resume moves owners

- An arm lives only in the owning replica's runtime plus a transient
  pub/sub message, while the steer's `preempt` flag is durable on the
  queue item. A HITL resume landing on a different replica therefore
  started with an empty armed set and a poll stuck false, so an
  interrupt the user had already been ACKed for silently waited for an
  ordinary tool boundary. New `GenerationJobManager.rearmQueuedPreempts`
  rebuilds the armed set by peeking the durable queue (fenced on the
  generation) and re-arming every item flagged `preempt`; resume calls
  it right after claiming. Safe by construction: every item peeked is
  still queued, so no drained steer can be resurrected.

- Capability-refresh failure now logs at error rather than warn, but
  deliberately does NOT fail the resume — see the reply on that thread.

Tests: +2 (rebuild from queue arms only the flagged item and reports
the count; a stale generation arms nothing). 124 packages/api steering
specs green.

* 📡 fix: Codex round 5 — acknowledge only what was actually armed

- A cross-replica arm was fire-and-forget: `emitPreempt` logged its own
  publish failure and `requestPreempt` returned void, so the route
  answered `preempt: true` even when the owner never armed a poll. The
  steer still injected at the next tool boundary, but the chip claimed
  an interrupt that could not happen — and unlike HITL resume, an
  ordinary running generation had no durable reconciliation to recover
  it.

  `emitPreempt` now resolves to the subscriber count and rejects on
  failure; `requestPreempt` is async and returns whether the arm truly
  landed (owned locally, or delivered to at least one subscriber). The
  202 reports THAT rather than what was asked for, so the chip relabels
  to ordinary steering exactly as it does for a capability-degraded
  deployment. Errors are swallowed into `false` — an unarmed interrupt
  is a downgrade, never a failed steer.

- The owner capability is re-read immediately before enqueue rather
  than reused from the top of the guard ladder. `checkAgentAccess` and
  file resolution are awaits, so a request can span an entire HITL
  pause/resume that moves ownership to a replica with different
  capability and rewrites that very flag. Only paid for by requests
  that actually asked to interrupt.

Tests: +3 (not-armed when the publish reaches nobody; armed when this
replica owns the generation; a throwing publish downgrades instead of
propagating). 127 packages/api steering specs green.

* 🎯 fix: Codex round 6 — real ownership, confirmed disarms, and a CI regression of my own

Three review findings plus three CI failures the round-5 commit caused.

Review:
- Ownership came from `runtimeState`, which a cross-replica `getJob`
  populates with a FACADE runtime on any replica that merely read the
  job. Matching `createdAt` therefore proved only "we looked at this
  job", so a non-owner could arm nothing and report success. Ownership
  now comes from `ownedJobs`, the actual owner map.
- `armPreemptIds` returns how many ids it accepted, and a local arm is
  only reported as armed when one was. A tombstoned id (its steer
  drained at an ordinary boundary mid-request) no longer answers
  `preempt: true` for an interrupt that cannot happen.
- The cancel disarm is awaited. A dropped clear is worse than a dropped
  arm: the owner keeps a level-triggered request for a steer that no
  longer exists, seals its next chunk and truncates an unrelated
  answer. The boundary drain's own call stays non-blocking — there the
  owner is local, so the disarm is already effective and awaiting the
  informational publish would only delay injection.
- Subscriber count is NOT read as proof of owner receipt: the count
  includes this replica's own facade subscription. A successful publish
  reports armed, a rejected one does not. Documented rather than
  papered over — see the acknowledgement-semantics note on the PR.

CI regressions from round 5, all mine:
- `registerPreemptSubscription` was AWAITED at both runtime-init sites,
  so job creation blocked on a second Redis channel subscription and
  hung when that subscribe was slow. Abort is awaited because a missed
  abort strands a run; a missed preempt only degrades that steer to the
  next tool boundary, so it now registers without gating createJob.
- Two api specs mocked `@librechat/api` without the newly imported
  `isSteerPreemptSupported`, so the call threw before createJob; and one
  exact-match assertion needed the new `preemptCapable` metadata field.
- My own Redis integration spec asserted arm-before-clear ordering,
  which two publishes carry no guarantee of — the receiving tombstone
  exists precisely because of that. Now asserts delivery and payload
  fidelity, order-independent.

158 packages/api specs, 27 api specs green.

* 🧭 fix: Codex round 7 — settle the acknowledgement semantics (Option A)

Round 7's second finding is the incoherence I flagged on the PR: the
route persisted `preempt: true` on the durable queue item while
returning `preempt: false` when delivery could not be confirmed. Those
two then disagreed, and `rearmQueuedPreempts` trusts the DURABLE one —
so a resumed owner would honour an interrupt the client had explicitly
been told degraded to ordinary steering.

Rather than patch the disagreement, this settles the meaning:

`preempt` in the 202 means "queued as an interrupt request", NOT "a
seal is guaranteed". It mirrors `SteerQueueItem.preempt` exactly, so
the response, the durable record, and the resume-time re-arm can never
disagree. The gates that ARE knowable stay — the owner's recorded
capability and a successful enqueue. Everything past that degrades to
the documented fallback of injecting at the next tool boundary.

A route cannot synchronously know whether another replica will seal:
proving it needs a correlated request/response over pub-sub, and even
that only proves the owner heard, not that it is still streaming when
the arm lands. Four rounds of tightening this boolean each surfaced a
narrower case; the sequence does not converge, so the invariant is now
"the flag describes the durable decision" and an unconfirmed arm logs a
warning instead of rewriting the answer.

Also from this round: a failed disarm publish is retried once and its
outcome reported. `handleSteerCancel` keeps `removed: true` — the steer
really did leave the queue, and saying otherwise would make the client
re-show a chip for a steer that can never arrive — and adds
`disarmed: false` so the residual risk is visible rather than swallowed.
Damage stays bounded regardless: the empty-boundary self-clear disarms
the generation after a single seal.

Tests: +1 pinning the response/durable-flag invariant. 159
packages/api specs green.

* 🧹 fix: Codex round 8 — remove the unverifiable disarm signal

Round 8 found the same over-promise on the disarm side that round 7
corrected on the arm side, so this applies the same answer rather than
patching around it.

The `disarmed: false` field added in round 7 was both unreliable and
unused: a resolved publish is not proof the owner heard it (the
delivery count includes this replica's own facade subscription), and it
was never threaded into `CancelSteerResponse` or read by any client. A
signal that claims a certainty the transport cannot provide is worse
than no signal — it invites callers to trust it.

Removed from the response. The retry stays, because it genuinely
reduces the failure rate, and `noteSteersRemoved` still returns whether
the publish succeeded FOR LOGGING, now documented explicitly as
"published without error", not "the owner disarmed".

Disarm is best effort with a bounded, self-healing failure: if the
clear is lost the owner seals once, the empty-boundary self-clear
disarms the generation, and the turn is persisted `unfinished: true`
rather than silently truncated. Tightening that further needs a
correlated request/response over pub-sub with a timeout — noted on the
PR as the deliberate boundary of this design rather than an oversight.

130 packages/api steering specs green.

* 🧽 fix: Codex round 9 — spend snapshot arms on nonempty drains too

The round-6 scoping fix only cleared the pre-drain snapshot when the
drain came back EMPTY. On a nonempty drain the `finally` cleared just
the drained ids, so a stale arm — typically a cancel whose
cross-replica clear was lost — survived the boundary. It would then
immediately seal the continuation meant to answer the steer that had
just been injected, and land on an empty boundary as
`preempt_incomplete`: the interrupt appears to work, and the answer to
it is truncated.

A boundary that runs has spent its seal, so everything armed at
snapshot time is spent whether or not it came back from the drain. The
`finally` now clears the union of the snapshot and the drained ids.
Arms that land AFTER the snapshot are still spared — their queue items
are live and uninjected, which is the property round 6 added.

Also fixes an api-workspace CI failure of mine: `resume.spec.js` stubs
`GenerationJobManager` wholesale, and the round-3/4 resume work added
two calls (`updateMetadata`, `rearmQueuedPreempts`) the stub did not
define, so 34 specs threw. Stub extended.

Tests: +2 (a nonempty drain clears a stale snapshot arm; a nonempty
drain spares an arm that landed mid-drain). Counterfactually verified —
the stale-arm spec fails against the unfixed drain. 132 packages/api
specs, 60 resume specs green.

* fix: never let a failed preempt subscription reject into the void

registerPreemptSubscription is called detached at both sites, so a
rejected Redis SUBSCRIBE became an unhandled rejection — process-fatal
under Node's default --unhandled-rejections=throw. The comment already
promised this path merely degrades steering; it now does.

Swallowed and logged inside the registration rather than at each call
site, so a future third caller cannot reintroduce the trap. Losing the
channel costs this generation's cross-replica preempts, not the server:
same-replica arming is runtime state and still works, and remote arms
fall back to the next tool boundary.

Verified counterfactually — the new spec surfaces SUBSCRIBE failed as an
unhandled rejection against the unfixed registration.

* docs: state the real blast radius of a failed preempt subscription

LibreChat's own entrypoints install a global unhandledRejection handler
that logs and keeps serving, so the escaping rejection this guards was
never fatal to this server — only to another consumer of @librechat/api
that installs no handler. The fix stands either way; the comment just
should not overstate what it prevents.

* test: cover the cross-replica preempt hop with two manager instances

Every other preempt test runs against a single manager, so the hop that
actually carries an interrupt in production had no coverage: the steer POST
lands on whichever replica the balancer picks, which is usually not the one
generating. Non-owner publishes, owner arms, owner's level-triggered poll
flips — none of that was exercised end to end.

Two GenerationJobManagerClass instances are a faithful replica pair here.
runtimeState and ownedJobs are private instance fields, there is no
module-level mutable state between them, and createStreamServices duplicates
a dedicated subscriber connection per call, so separate OS processes would
exercise the same objects over the same Redis.

Both assertions verified counterfactually against real Redis:
- Deleting the preemptCapable deserialization in RedisJobStore fails this
  with 'Expected: true, Received: undefined' — the exact P1 that shipped past
  every in-memory test and would have made the feature a silent no-op on
  every Redis deployment.
- Dropping the non-owner arm publish fails it with 'Received: false'.

* test: remove the fixed sleeps and vacuity from the cross-replica preempt test

Codex round 11, both findings, both on the test I added last commit.

P2 — the 300ms waits were load-bearing. Redis pub/sub never replays and the
owner's SUBSCRIBE is detached, so on a slow CI worker the publish could land
before anyone was listening and the test would fail against correct code.
Now it republishes until the owner's state converges, which is safe because
arms and clears are idempotent set writes keyed by steerId. Side effect: the
tests got ~10x faster (85ms/57ms vs 929ms/606ms) since they finish on
delivery rather than on a timer.

P3 — afterEach destroyed only the transports, leaving each manager alive in
its own cleanup-interval closure, still working against a dead transport.
Now tracks the managers and awaits destroy(), which disposes the job store
and its timer too. Matches how the rest of this file cleans up.

Fixing the sleeps exposed a third problem codex did not flag: the stale-arm
test could pass vacuously, because an undelivered arm and a fenced one look
identical. It now brackets the stale publish between two control arms — the
first proves the owner is listening before the stale one is sent, the second
proves it has had its chance to arrive.

Verified counterfactually against real Redis, and stable over 5 runs:
- dropping the preemptCapable deserialization fails with 'Received: undefined'
- dropping the non-owner arm publish times out both tests
- removing the generation fence fails the stale test with
  ["control-before", "steer-stale", "control-after"] — which also confirms
  the bracketing orders as intended rather than by luck

* fix: gate interrupt on the OWNER's capability alone, not the route's

Codex round 12. The comment above this gate already said 'the OWNER's
recorded capability, not this replica's probe' — and then the code ANDed in
isSteerPreemptSupported(), which is exactly this replica's probe. The
contradiction dates to the original commit; round 6 made the gate
owner-scoped and wrote that comment without removing the local conjunct.

The route never seals. It enqueues and publishes an arm, neither of which
touches the SDK, so during a rolling deploy a steer landing on an
un-upgraded replica silently lost its interrupt even though the owner could
seal. When the route IS the owner the probe is redundant anyway: the flag it
would consult is the one this process wrote at createJob.

The real degradation path is unchanged and still tested — an owner that
recorded no capability relabels to an ordinary steer. The test that pinned
the local probe asserted an impossible same-replica state (capable metadata
plus an incapable local SDK, when the metadata is written from that probe);
it now pins the mixed-SDK direction instead, and fails with
'Expected: true, Received: false' if the probe is put back.

* fix: reconcile arms at handover, and stop holding the 202 on a publish

Codex round 13, two of three findings.

P2 — rearmQueuedPreempts only ever ADDED. A replica that merely read the job
still installs a facade runtime and subscribes, so it can accept an arm and
then miss the best-effort clear that follows the drain. HITL resume promotes
that facade to owner, the union keeps the orphan, and the first resumed
stream seals on a steer no longer in the queue, drains nothing, and
truncates the resumed answer as preempt_incomplete. acquireResumedJobOwnership
only sets ownedJobs, so nothing else was clearing it. The durable queue is
the sole authority at a handover: arms it does not back are now disarmed and
tombstoned, so an in-flight publish cannot revive them either.

Worth recording that my own independent review raised this and my verifier
refuted it. Codex found it separately; two reviewers converging should have
outweighed one refutation.

P2 — the route awaited the arm publish before answering. The 202 reports
capability, not delivery, so the await could not change the response; it only
exposed the caller to Redis latency after the queue item was already durable.
A client that times out and retries mints a second steer while the first
stays queued, injecting the same instruction twice, whereas a lost publish
merely takes the tool-boundary fallback. Detached, with both outcomes logged.

All three tests verified counterfactually: union-only rearm fails the two new
handover specs, and re-awaiting the publish hangs the stalled-publish spec
until jest kills it.

* fix: snapshot arms before reading the queue at handover

Codex round 14 — a regression from my own round-13 fix, and a worse failure
than the one it corrected.

Round 13 read the durable queue first, then tombstoned any armed id the
snapshot did not back. But approvals.resolve reopens steering before
reconciliation runs, so another replica can commit a preempt steer and
publish its arm while the peek is in flight. That arm is then present locally
but absent from a snapshot taken before the steer existed, so a LIVE
interrupt the route already acknowledged got dropped — and tombstoned, which
blocks the re-arm, making it unrecoverable rather than merely late.

Fixed by inverting the two reads rather than by locking or paying a second
round trip. A steer is durably enqueued BEFORE its arm is published, so any
id in an arms-first snapshot was already queued when it was armed, and the
later peek must observe it unless it has since drained — which is exactly the
orphan this reconciliation exists to drop. Arms landing after the snapshot
are simply not candidates.

Also re-checks runtime identity across the await, since the generation can be
replaced while the queue read is in flight.

New spec injects a steer + arm during the peek and verifies it survives;
against the round-13 ordering it fails with Received array: [].

* fix: bound the cancel disarm wait and fence enqueue to its generation

Codex round 15.

P2 — the cancel awaited its disarm publish unbounded. ioredis queues
commands during an outage rather than rejecting, so that await could hang for
the length of the outage with the steer ALREADY durably cancelled; a client
that gives up then treats the cancel as failed and restores a chip for a
steer that can never produce an applied event. Every successful cancel
publishes, so ordinary steers were exposed too, not only preemptive ones.
Now bounded at 1s, with the publish continuing behind it — its retry and
logging are unchanged, it is just no longer in front of the response. This is
the sibling of round 13's arm-publish finding; I fixed one path and left this
one.

P3 — enqueue was not fenced to the generation the capability decision was
made against. The access checks, file resolution and owner re-read are all
awaits, so the run can be replaced before the enqueue: the item then lands on
the REPLACEMENT queue while the durable preempt flag and the arm still name
the previous epoch, the arm is fenced out at the owner, and the 202 promises
an interrupt that cannot happen. enqueueSteer now takes an expected
generation, mirroring drain/peek, and the Redis path enforces it inside
STEER_ENQUEUE_LUA so the check is atomic with the push rather than racing it.

All three new specs verified counterfactually, including the Lua guard
against real Redis (removing it returns 1 where -1 is required).

* fix: fence the steer to its authorized generation, bound resume setup, keep preempt when Redis parks

Codex round 16, all three findings.

P2 — round 15 fenced the enqueue to owner.createdAt, the RE-READ job. Every
guard above it (ownership, tenant, paused-state, agent ACL) ran against the
job read at the top, so if the run was replaced during those awaits the fence
happily accepted the steer into a generation the request was never authorized
against, carrying the wrong agent's metadata. Now rejects on any mismatch
between the validated job and the re-read.

P2 — resume awaited its steering bookkeeping unbounded, after
approvals.resolve had consumed the action and flipped the job to running, and
outside the resume lifecycle's own try/finally. .catch does not fire on a
promise that never settles, which is what ioredis produces during an outage,
so the client times out, its retry gets a 409 for a spent action, and no
cleanup runs. Bounded at 1s with the writes finishing in the background.

P3 — Redis parks leftover steers inside its terminal-transition Lua, which
projects item fields one by one, so preempt was silently dropped and a steer
recovered from /chat/status lost its interrupting label. Added to both
projections.

All three verified counterfactually, two against real Redis. Worth recording
that my first version of the generation-mismatch test was VACUOUS — it faked
a createdAt matching no live job, so the round-15 enqueue fence rejected it
for the wrong reason and the test passed with the guard removed. Rewritten to
replace the run for real; it now fails with 'Expected 404, Received 202'.
2026-07-30 12:36:13 -04:00

3325 lines
133 KiB
JavaScript

require('events').EventEmitter.defaultMaxListeners = 100;
const { logger } = require('@librechat/data-schemas');
const { getBufferString, HumanMessage } = require('@librechat/agents/langchain/messages');
const {
createRun,
isEnabled,
checkAccess,
buildToolSet,
logToolError,
sanitizeTitle,
payloadParser,
createSafeUser,
initializeAgent,
resolveConfigHeaders,
countTokens,
getBalanceConfig,
omitTitleOptions,
getProviderConfig,
memoryInstructions,
createTokenCounter,
applyContextToAgent,
isMemoryAgentEnabled,
recordCollectedUsage,
sendEvent,
computeUsageCostUSD,
aggregateEmittedUsage,
resolveAgentTokenConfig,
buildPersistedContextUsage,
computeSummaryUsedTokens,
priorRunOutputTokens,
createSubagentUsageSink,
anyAgentReplaysReasoningContent,
GenerationJobManager,
getTransactionsConfig,
resolveRecursionLimit,
buildPendingAction,
toClientPendingAction,
computeAgentRequestFingerprint,
extractDiscoveredToolsFromHistory,
captureResumeModelParameters,
pickResumeContext,
getApprovalTtlMs,
isHITLEnabled,
deleteAgentCheckpoint,
agentRequestsAskUserQuestion,
attachAskUserQuestionArgs,
hydrateResumeRunSteps,
createContentIndexOffsetHandlers,
createSteerIndexOffsetHandlers,
createSteerDrainHook,
createSteerPreemptBoundaryHook,
createSteerPreemptPoll,
isSteeringSupported,
isSteerPreemptSupported,
buildSteerMedia,
stampSteerPartMedia,
createActivityLabelWiring,
resolveActivityConfig,
getCustomEndpointConfig,
mapCollectedMetadataToUsage,
resolveActivityLabelModel,
settlePendingLabelFills,
stripActivityLabelParts,
getRequestMemories,
getMemoryAgentId,
createMemoryProcessor,
agentHasInlineMemoryTools,
loadAgent: loadAgentFn,
createMultiAgentMapper,
filterMalformedContentParts,
countFormattedMessageTokens,
prependFileContext,
prependQuotes,
hydrateMissingIndexTokenCounts,
injectSkillPrimes,
collectFreshSkillPrimeNames,
isSkillPrimeMessage,
collectFileIds,
processTextWithTokenLimit,
buildAgentScopedContext,
buildSkillPrimeContentParts,
buildInitialToolSessions,
hasUrlContextTool,
hasYouTubeVideoParts,
appendYouTubeVideoParts,
resolveGoogleVideoError,
resolveYouTubeInjectionConfig,
decrementPendingRequest,
maybePrewarmCodeSandbox,
} = require('@librechat/api');
const {
Run,
Callback,
Providers,
TitleMethod,
formatMessage,
formatAgentMessages,
createMetadataAggregator,
} = require('@librechat/agents');
const {
Constants,
SteerEvents,
ActivityLabelEvents,
UsageEvents,
Permissions,
VisionModes,
ContentTypes,
ApprovalEvents,
EModelEndpoint,
PermissionTypes,
AgentCapabilities,
isAgentsEndpoint,
isEphemeralAgentId,
removeNullishValues,
DEFAULT_MEMORY_MAX_INPUT_TOKENS,
} = require('librechat-data-provider');
const { filterFilesByAgentAccess } = require('~/server/services/Files/permissions');
const { encodeAndFormat } = require('~/server/services/Files/images/encode');
const { createContextHandlers } = require('~/app/clients/prompts');
const { resolveConfigServers } = require('~/server/services/MCP');
const { getMCPServerTools } = require('~/server/services/Config');
const BaseClient = require('~/app/clients/BaseClient');
const { getMCPManager } = require('~/config');
const db = require('~/models');
const loadAgent = (params) => loadAgentFn(params, { getAgent: db.getAgent, getMCPServerTools });
const MEMORY_INPUT_CHARS_PER_TOKEN = 8;
class AgentClient extends BaseClient {
constructor(options = {}) {
super(null, options);
/** The current client class
* @type {string} */
this.clientName = EModelEndpoint.agents;
/** @deprecated @type {true} - Is a Chat Completion Request */
this.isChatCompletion = true;
/** @type {number | undefined} */
this.jobCreatedAt = options.jobCreatedAt;
/** @type {AgentRun} */
this.run;
/** Resolves with the agent run once `chatCompletion` initializes it (or
* `null` if initialization fails), letting immediate-mode title generation
* await the run instead of throwing when fired before the run exists.
* @type {Promise<AgentRun | null> | null} */
this._runReady = null;
/** @type {((run: AgentRun | null) => void) | null} */
this._resolveRun = null;
const {
agentConfigs,
contentParts,
stepMap,
collectedUsage,
collectedThoughtSignatures,
artifactPromises,
maxContextTokens,
subagentAggregatorsByToolCallId,
contextUsageSink,
usageEmitSink,
toolInputValidationErrors,
...clientOptions
} = options;
this.agentConfigs = agentConfigs;
this.maxContextTokens = maxContextTokens;
/** Latest visible context snapshot for this response, captured live by the
* ON_CONTEXT_USAGE handler; persisted on `metadata.contextUsage`.
* @type {{ latest: import('librechat-data-provider').TContextUsageEvent | null } | undefined} */
this.contextUsageSink = contextUsageSink;
/** Every emitted `on_token_usage` payload for this response (primary,
* summarization, sequential, and subagent); aggregated into the rollup
* persisted on `metadata.usage`.
* @type {Array<import('librechat-data-provider').TTokenUsageEvent> | undefined} */
this.usageEmitSink = usageEmitSink;
/** Schema-validation exceptions keyed by tool-call ID. The completion
* handler consumes these to distinguish execution failures from tool
* output that merely contains similar text.
* @type {Map<string, import('@librechat/api').ToolInputValidationError> | undefined} */
this.toolInputValidationErrors = toolInputValidationErrors;
/** @type {MessageContentComplex[]} */
this.contentParts = contentParts;
/** Original run-step identity used by the content aggregator to attach
* completion events to their rendered content indices.
* @type {Map<string, import('@librechat/agents').RunStep | undefined> | undefined} */
this.stepMap = stepMap;
/** @type {Array<UsageMetadata>} */
this.collectedUsage = collectedUsage;
/** Vertex Gemini 3 thought signatures captured during the run, keyed by
* `tool_call_id`. Persisted on `responseMessage.metadata.thoughtSignatures`
* and restored as `additional_kwargs.signatures` on subsequent turns to
* keep tool round-trips valid across DB reconstruction.
* @type {Record<string, string> | undefined} */
this.collectedThoughtSignatures = collectedThoughtSignatures;
/** @type {ArtifactPromises} */
this.artifactPromises = artifactPromises;
/** Per-request map of `createContentAggregator` instances keyed by
* the parent's `tool_call_id`. `ON_SUBAGENT_UPDATE` events stream
* into each aggregator as they arrive; `finalizeSubagentContent`
* harvests `contentParts` onto the matching `subagent` tool_call
* so the child's full activity survives a page refresh. */
this.subagentAggregatorsByToolCallId = subagentAggregatorsByToolCallId ?? new Map();
/** In-flight `on_token_usage` emits from subagent child runs. The sink
* fires the emitter without awaiting, so chatCompletion's finally flushes
* these before returning — otherwise job cleanup can race the persist.
* @type {Promise<void>[]} */
this.pendingSubagentEmits = [];
/** @type {AgentClientOptions} */
this.options = Object.assign({ endpoint: options.endpoint }, clientOptions);
/** @type {string} */
this.model = this.options.agent.model_parameters.model;
/** The key for the usage object's input tokens
* @type {string} */
this.inputTokensKey = 'input_tokens';
/** The key for the usage object's output tokens
* @type {string} */
this.outputTokensKey = 'output_tokens';
/** @type {UsageMetadata} */
this.usage;
/** @type {Record<string, number>} */
this.indexTokenCountMap = {};
/** @type {Array<Record<string, unknown>> | null} */
this.memoryPayload = null;
/** Mutable content-index shift shared with the steer offset handlers.
* Incremented each time a steer part is spliced into `contentParts`, so
* SDK-emitted indices that arrive after an injection land past it.
* @type {import('@librechat/api').SteerOffsetState} */
this.steerOffsetState = { offset: 0 };
/** @type {(messages: BaseMessage[]) => Promise<void>} */
this.processMemory;
}
/**
* Returns the aggregated content parts for the current run.
* @returns {MessageContentComplex[]} */
getContentParts() {
return this.contentParts;
}
/**
* Harvest the `contentParts` from each per-subagent `createContentAggregator`
* instance and attach them onto the matching parent `subagent` tool_call
* as `subagent_content`. Runs once per message save (from
* `sendCompletion`'s `finally`) so the child's full reasoning / tool
* calls / final text survive a page refresh — the client-side Recoil
* atom is session-only. Aggregators keyed by a tool_call_id that never
* appeared in `contentParts` are discarded (no home to attach to).
*/
finalizeSubagentContent() {
const buffer = this.subagentAggregatorsByToolCallId;
if (!buffer || buffer.size === 0 || !Array.isArray(this.contentParts)) {
return;
}
for (const part of this.contentParts) {
if (part?.type !== ContentTypes.TOOL_CALL) continue;
const toolCall = part[ContentTypes.TOOL_CALL];
if (!toolCall || toolCall.name !== Constants.SUBAGENT || !toolCall.id) continue;
const aggregator = buffer.get(toolCall.id);
if (!aggregator) continue;
try {
/** `createContentAggregator` returns a sparse array (undefined
* slots for indices that never received content). Strip those
* so the persisted shape is a clean `TMessageContentParts[]`. */
const parts = Array.isArray(aggregator.contentParts)
? aggregator.contentParts.filter((p) => p != null)
: [];
if (parts.length > 0) {
toolCall.subagent_content = parts;
}
} catch (err) {
logger.warn(
`[AgentClient] Failed to attach subagent content for tool_call ${toolCall.id}: ${err?.message ?? err}`,
);
}
}
buffer.clear();
}
/**
* Apply one drained steer to host state: append the steer content part at
* the live content index, bump the shared index offset so subsequent SDK
* step indices land past it, and emit `on_steer_applied` so the live client
* replaces its pending chip with the inline part (the emitted chunk also
* reaches the Redis chunk log for reconnect reconstruction).
*
* Runs BEFORE the drain hook's media encode so an abort during the encode
* cannot lose the steer. File refs persist from the queue item (sanitized at
* enqueue); replay/token accounting re-fetch owner-scoped and re-encode per
* turn (stampSteerPartMedia), so unauthorized ids drop out there.
*
* @param {string} streamId
* @param {import('@librechat/api').SteerQueueItem} item
*/
async applySteerPart(streamId, item) {
const index = this.contentParts.length;
const part = {
type: ContentTypes.STEER,
[ContentTypes.STEER]: item.text,
steerId: item.steerId,
createdAt: item.createdAt,
...(item.files?.length && { files: item.files }),
};
this.contentParts.push(part);
this.steerOffsetState.offset += 1;
// durable: the chunk-log XADD is this event's recovery record — it must
// commit before the publish or a cross-replica reconnect that missed the
// pub/sub delivery reconstructs content without the steer part.
await GenerationJobManager.emitChunk(
streamId,
{
event: SteerEvents.ON_STEER_APPLIED,
data: {
steerId: item.steerId,
index,
part,
responseMessageId: this.responseMessageId,
conversationId: this.conversationId,
},
},
{ durable: true, expectedCreatedAt: this.jobCreatedAt },
);
}
/**
* The `steering` fragment for `createRun`: the run-scoped PostToolBatch
* drain hook — plus, when the SDK can seal mid-stream, the PreemptBoundary
* twin and the preempt poll built from the SAME drain closures, so both
* boundaries inject byte-identical shapes. `undefined` when there is no
* resumable job surface or the installed SDK cannot inject hook messages
* (draining would drop them).
*
* @param {string | undefined} streamId
*/
buildSteerWiring(streamId) {
if (!streamId || !isSteeringSupported()) {
return undefined;
}
const drainOptions = {
streamId,
jobCreatedAt: this.jobCreatedAt,
applySteer: (item) => this.applySteerPart(streamId, item),
buildMedia: (item) =>
buildSteerMedia({
client: this,
user: this.options.req?.user,
item,
getFiles: db.getFiles,
}),
};
return {
hook: createSteerDrainHook(drainOptions),
...(isSteerPreemptSupported() && {
preemptHook: createSteerPreemptBoundaryHook(drainOptions),
preemption: createSteerPreemptPoll(streamId),
}),
};
}
setOptions(_options) {}
/**
* Resolve provider + client options for the
* tool-batch summary model. Same resolution path as titleConvo minus the
* title-specific branches. Model precedence: the endpoint's
* `activityModel` > its `titleModel` > the agent's own model, on the
* endpoint named by `activityEndpoint` (default: the agent's).
*/
async resolveActivityLabelLLM() {
/** Memoized per response: resolution reads provider config and can hit the
* database for user keys, and nothing it depends on changes between
* batches of the same run — so re-resolving on every batch (twice, with
* usage accounting) is repeated credential work for an identical result.
* The promise is cached rather than the value so concurrent batches share
* one in-flight resolution. */
this.activityLabelLLMPromise =
this.activityLabelLLMPromise ??
resolveActivityLabelModel({
req: this.options.req,
agent: this.options.agent,
/** Same public-endpoint-first field resolution as the wiring gate. */
publicEndpoint: this.options.endpoint,
ids: {
messageId: this.responseMessageId,
conversationId: this.conversationId,
parentMessageId: this.parentMessageId,
},
db: { getUserKey: db.getUserKey, getUserKeyValues: db.getUserKeyValues },
}).catch((error) => {
/** Never cache a rejection: a transient credential read failure would
* otherwise disable labels for the rest of the response. */
this.activityLabelLLMPromise = null;
throw error;
});
return this.activityLabelLLMPromise;
}
/**
* Bills the label call and folds its usage into the response rollup with
* an `activity-label` tag (subagent precedent) so `metadata.usage` and the
* live cost gauge reflect it. Tagged, so it is not a PRIMARY usage event
* and cannot disturb the context-snapshot pairing in buildResponseMetadata.
*/
async recordActivityLabelUsage(
collectedMetadata,
model,
endpointTokenConfig,
sameEndpoint,
/** Optional suppression gate, defaulting open. The hook-driven paths
* deliberately pass nothing: they invoke accounting ONLY for a
* COMMITTED fill, and a committed (visible) label must bill even when
* its scope closed during the durable emit — the commit flag, not the
* scope, is the billing authority. */
scopeOpen = () => true,
/** The LABEL endpoint's provider — cost math needs it to know whether
* cache tokens are folded into `input_tokens` (additive providers like
* Bedrock keep them separate). */
provider = undefined,
/** Lazy `() => ({ promptText, completionText })` fallback. When the
* provider omits usage metadata entirely, labels bill by ESTIMATE —
* the title convention — from locally counted text rather than going
* unbilled. Invoked only when no entry carries a real token count. */
estimate = undefined,
) {
const appConfig = this.options.req?.config;
/** Provider ON EVERY ENTRY, not just the streamed event: `splitUsage`
* keys additive-vs-subset cache math on `usage.provider`, and an
* unknown provider takes the additive branch — for Anthropic/OpenAI
* (cache already inside `input_tokens`) that re-adds cache_read and
* cache_creation on top, double-charging the balance while the
* streamed cost (which carries the provider) disagrees. */
let collectedUsage = mapCollectedMetadataToUsage(collectedMetadata).map((usage) =>
provider != null ? { ...usage, provider } : usage,
);
const hasRealUsage = collectedUsage.some(
(usage) => usage.input_tokens != null || usage.output_tokens != null,
);
if (!hasRealUsage && typeof estimate === 'function') {
try {
const { promptText = '', completionText = '' } = estimate() ?? {};
const [input_tokens, output_tokens] = await Promise.all([
countTokens(promptText),
countTokens(completionText),
]);
collectedUsage = [
provider != null
? { input_tokens, output_tokens, provider }
: { input_tokens, output_tokens },
];
} catch (err) {
logger.warn(
`[AgentClient] Failed to estimate activity-label usage: ${err?.message ?? err}`,
);
}
}
if (
collectedUsage.length === 0 ||
!collectedUsage.some((usage) => usage.input_tokens != null || usage.output_tokens != null)
) {
return;
}
if (!scopeOpen()) {
return;
}
const streamId = this.options.req?._resumableStreamId || null;
const includeCost = this.options.req?.config?.interfaceConfig?.contextCost === true;
/** Cross-endpoint labels (`activityEndpoint`) price with THEIR endpoint's
* rates. `undefined` is a MEANINGFUL result for a built-in label endpoint
* (built-ins price from the shared table, not a per-endpoint map), so it
* must not fall through to the agent's custom rates — a custom primary
* pointing `activityEndpoint` at a built-in would bill the label at its
* own rates. Only inherit when the label actually runs on the agent's
* endpoint. */
const labelTokenConfig = sameEndpoint
? (endpointTokenConfig ?? this.options.endpointTokenConfig)
: endpointTokenConfig;
for (const usage of collectedUsage) {
/** `seq` is normally a position in `collectedUsage` (each emitter
* pushes, then emits with the new length). Label usage is billed
* separately and never appended there, so it has no position: any
* positive value eventually collides with a real one, and the client
* dedupes on `runId:seq`. Labels therefore occupy a NEGATIVE seq
* namespace that positional sequences can never reach. The key is
* only used for Set membership, so the sign is inert. */
this.activityLabelUsageSeq = (this.activityLabelUsageSeq ?? 0) + 1;
const data = {
input_tokens: usage.input_tokens,
output_tokens: usage.output_tokens,
/** Cache tokens ride along (subagent-event shape) so display and
* aggregation price cached label calls at cache rates. */
...(usage.input_token_details != null && {
input_token_details: usage.input_token_details,
}),
...(provider != null && { provider }),
model,
usage_type: 'activity-label',
/**
* Scoped to the GENERATION, not just the response. Editing one
* assistant response reuses its `responseMessageId` while each fresh
* server generation restarts `activityLabelUsageSeq`, so a second
* edit re-emitted `<responseId>:-1` and the client — which dedupes on
* exactly `runId:seq` — discarded the newer usage even though its
* balance transaction was still written. `jobCreatedAt` is the run's
* own epoch: stable across reconnects and HITL resumes of one
* generation, distinct between generations.
*/
runId:
this.jobCreatedAt != null
? `${this.responseMessageId}:${this.jobCreatedAt}`
: this.responseMessageId,
seq: -this.activityLabelUsageSeq,
/** Cost coverage is all-or-nothing in `aggregateEmittedUsage`: an
* event without `cost` suppresses the whole response's cost when
* `interface.contextCost` is on. */
cost: includeCost
? computeUsageCostUSD(
{ ...usage, model, provider },
{ getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
labelTokenConfig,
)
: undefined,
};
/** Fold into the response rollup synchronously, then stream it like
* primary/subagent usage so the live session gauge stays honest.
* Retained and flushed with the subagent emits so job cleanup cannot
* race the persist. */
this.usageEmitSink?.push(data);
if (streamId) {
const emit = GenerationJobManager.emitChunk(
streamId,
{
event: UsageEvents.ON_TOKEN_USAGE,
data,
},
/** Same epoch scoping as the label event: this usage is recorded
* from a detached generation and must not bill against whichever
* generation replaced it. */
{ expectedCreatedAt: this.jobCreatedAt },
).catch((err) => {
logger.warn(`[AgentClient] Failed to emit activity-label usage: ${err?.message ?? err}`);
});
this.pendingSubagentEmits.push(emit);
}
}
await this.recordCollectedUsage({
collectedUsage,
context: 'activity-label',
model,
endpointTokenConfig: labelTokenConfig,
/** The label ran elsewhere, so its config governs even when undefined. */
crossEndpoint: sameEndpoint === false,
balance: getBalanceConfig(appConfig),
transactions: getTransactionsConfig(appConfig),
messageId: this.responseMessageId,
/** Billed, but NOT the response's stream usage — see the parameter. */
updateStreamUsage: false,
}).catch((err) => {
logger.error(
'[api/server/controllers/agents/client.js #recordActivityLabelUsage] Error recording usage',
err,
);
});
}
/**
* Bridges label generation to the SDK's `run.generateActivityLabel()` so
* the fast-model call is Langfuse-traced under the conversation's session
* (thread_id) with its own tags — never as an orphan trace. Returns null
* when the label could not be generated.
*/
async generateActivityLabelViaRun({
entries,
context,
previousLabels,
traceSeed,
signal,
charLimit,
prompt,
executingAgentId,
deferUsage,
}) {
/** Version gating happens at wiring time via the `sdkCapable` prototype
* probe, so this only catches a run that is missing or not yet built.
* Resolve `undefined` (not `null`) so the hook reads it as "this path
* cannot serve the request" and falls back to the direct model call;
* `null` would mean "ran, produced no label" and would leave the slot
* permanently empty. */
if (typeof this.run?.generateActivityLabel !== 'function') {
return undefined;
}
const { provider, clientOptions, endpointTokenConfig, sameEndpoint } =
await this.resolveActivityLabelLLM();
const { handleLLMEnd, collected: collectedMetadata } = createMetadataAggregator();
/**
* NO scope gate here: the hook invokes this ONLY for a COMMITTED fill,
* and the commit flag is the single billing authority. A scope that
* closes while the fill's durable emit is in flight does not un-commit
* the label — it is persisted and visible — so gating on the scope here
* turned that race into a completed provider call escaping both the
* label charge and the primary abort accounting. The reverse direction
* (billed but never shown) is enforced by the commit gate itself: a
* dropped fill never reaches this callback.
*/
/**
* The PROMPT THE SDK ACTUALLY SENT, captured at chain start. The hook's
* estimate thunk carries this module's locally built prompt — same
* entries and instruction but different framing — so estimated billing
* on this path would count a prompt that was never sent. When capture
* succeeded, it replaces the thunk's promptText.
*/
let sdkPromptText;
const capturePrompt = {
handleLLMStart: (_llm, prompts) => {
sdkPromptText = Array.isArray(prompts) ? prompts.join('\n') : undefined;
},
handleChatModelStart: (_llm, messages) => {
try {
sdkPromptText = (messages ?? [])
.flat()
.map((message) =>
typeof message?.content === 'string'
? message.content
: JSON.stringify(message?.content ?? ''),
)
.join('\n');
} catch {
/** Estimation falls back to the local approximation. */
}
},
};
const recordUsage = async (estimate) => {
const refined =
typeof estimate === 'function'
? () => {
const base = estimate() ?? {};
return sdkPromptText != null && sdkPromptText.length > 0
? { ...base, promptText: sdkPromptText }
: base;
}
: estimate;
await this.recordActivityLabelUsage(
collectedMetadata,
clientOptions.model,
endpointTokenConfig,
sameEndpoint,
undefined,
provider,
refined,
);
};
/**
* Accounting is DEFERRED to the hook, which runs it only after the slot
* commit settles. Awaiting it here (pre-fill) let the settlement window
* expire during the balance write: the charge landed, then the fill was
* dropped as out-of-scope — billed, never shown. Registered before the
* call so a mid-call throw still bills whatever metadata the provider
* returned, exactly like the old `finally` did.
*/
let usageDeferred = false;
if (typeof deferUsage === 'function') {
usageDeferred = true;
deferUsage(recordUsage);
}
try {
const { label } = await this.run.generateActivityLabel({
provider,
clientOptions,
entries: entries.map(({ toolName, toolInput, toolOutput, error, status }) => ({
toolName,
toolInput,
toolOutput,
error,
status,
})),
thinkingExcerpts: context.thinkingExcerpts,
lastAssistantText: context.lastAssistantText,
...(previousLabels != null && { previousLabels }),
traceSeed,
charLimit,
/** Selects the EXECUTING agent's Langfuse metadata and, more
* importantly, its tool-output redaction policy. Omitting it lets a
* handoff's activity be traced and redacted under the default
* agent's configuration, bypassing a stricter per-agent policy. */
...(executingAgentId != null && { agentId: executingAgentId }),
/** The wiring always supplies one (the yaml `activityPrompt` when
* set, else this repo's instruction). Falling through to the SDK's
* built-in prompt would silently use a different register. */
...((prompt ?? this.activityLabelPrompt) != null && {
prompt: prompt ?? this.activityLabelPrompt,
}),
chainOptions: {
signal,
callbacks: [{ handleLLMEnd, ...capturePrompt }],
configurable: {
thread_id: this.conversationId,
user_id: this.user ?? this.options.req?.user?.id,
},
},
});
return label ?? null;
} finally {
/** Safety net for a caller that did not defer (none in-tree): the old
* inline accounting, still scope-gated. */
if (!usageDeferred) {
await recordUsage();
}
}
}
/** Bounded settle for in-flight label fills before finalization. On
* timeout the label scope is closed and its abort controller fired, so a
* straggler cannot mutate the saved response or emit into a dead job. */
async settleActivityLabels(timeoutMs = 3000) {
/** Detached even when nothing settled: the wiring attaches its abort
* listener at BUILD time, and a segment can end without a single claim
* (text-only, or handoff batches, which skip labels) — the early
* return below would otherwise leave that listener accumulating across
* HITL approval cycles on the shared job signal. Idempotent. */
const detachScopeListeners = () => {
for (const scope of this.activityLabelScopes ?? []) {
scope.detach?.();
}
};
const pending = this.pendingActivityLabelFills;
if (!pending || pending.length === 0) {
detachScopeListeners();
return;
}
this.pendingActivityLabelFills = [];
await settlePendingLabelFills(pending, timeoutMs, () => {
/** Close EVERY generation's scope: a pre-pause wiring's straggler must
* stay closed even though a resume built a newer one. */
for (const scope of this.activityLabelScopes ?? []) {
scope.closed = true;
scope.abort.abort();
}
});
detachScopeListeners();
}
/**
* Activity-label wiring. At each batch boundary the hook synchronously
* claims a live content slot (steering's index-offset pattern: push
* placeholder with deterministic counts, bump the shared offset so
* subsequent SDK indices land past it) and fills it when the fast-model
* label resolves. Both states reach the live client via the dedicated
* `on_activity_label` event; failures leave the counts-only part.
* @param {string | undefined} streamId
*/
buildActivityLabelWiring(streamId, abortSignal) {
if (!streamId) {
return undefined;
}
/** Per-endpoint opt-in via `activityLabel: true` in librechat.yaml,
* resolved the same way the title options are (endpoints.all > named
* endpoint > custom endpoint config). Custom endpoints live in the
* `endpoints.custom` ARRAY, so their settings are only visible through
* the matched entry — without it every custom endpoint reads as
* disabled. */
const agentEndpoint = this.options.agent?.endpoint ?? '';
const appConfigForActivity = this.options.req?.config;
let customEndpointConfig;
try {
customEndpointConfig = getCustomEndpointConfig({
endpoint: agentEndpoint,
appConfig: appConfigForActivity,
});
} catch {
customEndpointConfig = undefined;
}
const activityConfig = resolveActivityConfig(
appConfigForActivity,
agentEndpoint,
customEndpointConfig,
/** The PUBLIC endpoint (`agents`): `initializeAgent` rewrites
* `agent.endpoint` to the backing provider, so without this an
* admin's `endpoints.agents.activityLabel: true` reads the
* provider's block instead and the feature stays off. */
this.options.endpoint,
);
if (!activityConfig.enabled) {
return undefined;
}
this.activityLabelPrompt = activityConfig.prompt;
/**
* Mark the job so a resume can reconcile label gaps without probing
* content. Retried rather than fire-and-forget: this flag GATES that
* reconciliation, and it is a separate write from the durable label
* append — so a single lost write silently drops a label that the label
* content itself recorded perfectly well. One retry costs nothing at run
* setup and removes the only realistic way the gate goes stale.
*/
/** Retained (not fire-and-forget): the RUN START awaits this persist
* (chatCompletion/resumeCompletion, before processStream/resume), so
* the flag is durable before any batch can claim a label — closing the
* immediate-reconnect race WITHOUT delaying the claim-time reservation
* emit, whose ordering against shifted SDK indices is load-bearing.
* The chain settles on failure (warned retry), so a lost write can
* never wedge run startup. */
this.activityLabelsMarkedPromise = GenerationJobManager.markActivityLabels(streamId).catch(() =>
GenerationJobManager.markActivityLabels(streamId).catch(() => {
logger.warn(
`[AgentClient] Could not flag activity labels for ${streamId}; a label resolving during a resume gap may not be reconciled.`,
);
}),
);
/** SDK support probe (steering-style): the Run method and the formatter
* replay skip ship together, so method presence is the capability. */
const sdkCapable = typeof Run?.prototype?.generateActivityLabel === 'function';
/** Label-scoped abort: fired when settle times out so a straggling
* generation stops burning provider time for a finalized response.
* Chained to the run signal so a user abort still cancels labels. */
/** Close state is PER WIRING, not per client: a HITL resume rebuilds the
* wiring, and resetting a shared instance flag would re-open closures
* from the pre-pause segment whose provider call ignored the abort.
* Scopes are retained so settle closes every generation, past included. */
const labelScope = { closed: false, abort: new AbortController() };
this.activityLabelScopes = this.activityLabelScopes ?? [];
this.activityLabelScopes.push(labelScope);
/** Seed the usage sequence past the labels already on this response.
* `runId` is the response message id, so a HITL resume — which builds a
* NEW client for the SAME response — would otherwise restart at -1 and
* the client's `runId:seq` deduper would discard the post-approval
* label's usage as already counted. Each label generation is a single
* non-streaming invoke, so one existing label part == one consumed seq. */
this.activityLabelUsageSeq =
this.activityLabelUsageSeq ??
(this.contentParts ?? []).filter((part) => part?.type === ContentTypes.ACTIVITY_LABEL).length;
this.activityLabelAbort = labelScope.abort;
/** An abort CLOSES the scope, not just cancels the call. The rejected
* generation still runs its catch and calls `fill(null)`; with the scope
* merely aborted that fill would emit — and by then the next generation
* may already own the stream, so the event would land an index from the
* abandoned response onto the new one. */
const closeOnAbort = () => {
labelScope.closed = true;
labelScope.abort.abort();
};
if (abortSignal != null) {
if (abortSignal.aborted) {
closeOnAbort();
} else {
abortSignal.addEventListener('abort', closeOnAbort, { once: true });
/** Detached once this segment settles: HITL runs rebuild a wiring
* per approval cycle on the SAME job signal, and `once` only
* removes the listener if an abort actually fires — long
* multi-approval runs would otherwise accumulate obsolete
* closures toward the listener-limit warning. */
labelScope.detach = () => abortSignal.removeEventListener('abort', closeOnAbort);
}
}
/** Thin wrapper: slot claiming, lane stamping, emit ordering, and settle
* tracking live in `createActivityLabelWiring` (packages/api, TS). */
return createActivityLabelWiring({
maxPerRun: activityConfig.maxPerRun,
charLimit: activityConfig.charLimit,
prompt: activityConfig.prompt,
abortSignal: labelScope.abort.signal,
isClosed: () => labelScope.closed,
getContentParts: () => this.contentParts,
bumpIndexOffset: () => {
this.steerOffsetState.offset += 1;
},
/** Emits IMMEDIATELY — never sequenced behind the flag persist. The
* claim has already bumped the shared index offset, so delaying the
* reservation while shifted SDK chunks persist would let a
* cross-instance reconnect reconstruct a hole, compact it, and have
* the late label event overwrite the part that moved into its index.
* Flag ordering is guaranteed upstream instead: run start awaits the
* persist, so the flag is durable before any batch can claim. */
emitLabelEvent: (index, part) =>
GenerationJobManager.emitChunk(
streamId,
{
event: ActivityLabelEvents.ON_ACTIVITY_LABEL,
data: {
index,
part,
responseMessageId: this.responseMessageId,
conversationId: this.conversationId,
},
},
/** Label generation is detached and can outlive its generation, so
* the emit is scoped to the epoch that claimed the index. Without
* it a straggler from a replaced generation lands its old index on
* the new response — invisibly, since an empty label renders
* nothing — overwriting whatever occupies that slot. */
{ durable: true, expectedCreatedAt: this.jobCreatedAt },
),
trackPendingFill: (fillDone) => {
this.pendingActivityLabelFills = this.pendingActivityLabelFills ?? [];
this.pendingActivityLabelFills.push(fillDone);
},
resolveLLM: () => this.resolveActivityLabelLLM(),
/** Per-generation usage accounting for the direct fallback path;
* the SDK bridge records its own via chainOptions callbacks. */
getInvokeCallbacks: () => {
const { handleLLMEnd, collected } = createMetadataAggregator();
return {
callbacks: [{ handleLLMEnd }],
collect: async (estimate) => {
const { provider, clientOptions, endpointTokenConfig, sameEndpoint } =
await this.resolveActivityLabelLLM();
await this.recordActivityLabelUsage(
collected,
clientOptions.model,
endpointTokenConfig,
sameEndpoint,
/** No scope gate — the hook invokes collect ONLY for a
* COMMITTED fill (the billing authority), and a scope that
* closes during the fill's durable emit must not let a
* visible label escape its charge. Dropped fills never
* reach this callback. */
undefined,
provider,
estimate,
);
},
};
},
...(sdkCapable && {
generateLabel: (payload) => this.generateActivityLabelViaRun(payload),
}),
});
}
/**
* `AgentClient` is not opinionated about vision requests, so we don't do anything here
* @param {MongoFile[]} attachments
*/
checkVisionRequest() {}
getSaveOptions() {
let runOptions = {};
try {
runOptions = payloadParser(this.options) ?? {};
} catch (error) {
logger.error(
'[api/server/controllers/agents/client.js #getSaveOptions] Error parsing options',
error,
);
}
return removeNullishValues(
Object.assign(
{
spec: this.options.spec,
iconURL: this.options.iconURL,
chatProjectId: this.options.chatProjectId,
endpoint: this.options.endpoint,
agent_id: this.options.agent.id,
modelLabel: this.options.modelLabel,
resendFiles: this.options.resendFiles,
imageDetail: this.options.imageDetail,
maxContextTokens: this.maxContextTokens,
},
// TODO: PARSE OPTIONS BY PROVIDER, MAY CONTAIN SENSITIVE DATA
runOptions,
),
);
}
/**
* Returns build message options. For AgentClient, agent-specific instructions
* are retrieved directly from agent objects in buildMessages, so this returns empty.
* @returns {Object} Empty options object
*/
getBuildMessagesOptions() {
return {};
}
/**
*
* @param {TMessage} message
* @param {Array<MongoFile>} attachments
* @returns {Promise<Array<Partial<MongoFile>>>}
*/
async addImageURLs(message, attachments) {
const { files, image_urls } = await encodeAndFormat(
this.options.req,
attachments,
{
provider: this.options.agent.provider,
endpoint: this.options.endpoint,
},
VisionModes.agents,
);
message.image_urls = image_urls.length ? image_urls : undefined;
return files;
}
async buildMessages(messages, parentMessageId, _buildOptions, opts) {
/** Always pass mapMethod; getMessagesForConversation applies it only to messages with addedConvo flag */
const orderedMessages = this.constructor.getMessagesForConversation({
messages,
parentMessageId,
summary: this.shouldSummarize,
mapMethod: createMultiAgentMapper(this.options.agent, this.agentConfigs),
mapCondition: (message) => message.addedConvo === true,
});
let payload;
/** @type {number | undefined} */
let promptTokens;
/** Normalize instruction fields before applying per-run context. */
const normalizeInstructions = (agent) => {
agent.instructions = agent.instructions?.trim() || undefined;
agent.additional_instructions = agent.additional_instructions?.trim() || undefined;
return agent;
};
/** Collect all agents for unified processing while preserving stable/dynamic instruction fields. */
const allAgents = [
{ agent: normalizeInstructions(this.options.agent), agentId: this.options.agent.id },
...(this.agentConfigs?.size > 0
? Array.from(this.agentConfigs.entries()).map(([agentId, agent]) => ({
agent: normalizeInstructions(agent),
agentId,
}))
: []),
];
/**
* Memory authorization/loading and MCP config resolution do not depend on
* attachment hydration or prompt formatting. Start them before that work,
* but keep the existing context-application barrier below.
*
* Attach a rejection observer immediately because these operations may
* settle while request attachments are still being prepared. Awaiting the
* original promise later still propagates either error.
*/
const earlySharedContextPromise = Promise.all([
this.useMemory(),
resolveConfigServers(this.options.req),
]);
void earlySharedContextPromise.catch(() => {});
const sharedRunAttachmentIds = new Set();
/** @type {ReturnType<typeof buildAgentScopedContext>} */
let agentScopedContextPromise;
const startAgentScopedContext = () => {
const contextPromise = buildAgentScopedContext({
agentIds: allAgents.map(({ agentId }) => agentId),
attachmentsByAgentId: this.options.agentContextAttachmentsByAgentId,
sharedRunAttachmentIds,
req: this.options.req,
tokenCountFn: (text) => countTokens(text),
});
void contextPromise.catch(() => {});
return contextPromise;
};
if (this.options.attachments) {
const attachments = await this.options.attachments;
const latestMessage = orderedMessages[orderedMessages.length - 1];
for (const fileId of collectFileIds(attachments)) {
sharedRunAttachmentIds.add(fileId);
}
/** Agent-scoped extraction only depends on the shared attachment IDs. */
agentScopedContextPromise = startAgentScopedContext();
if (this.message_file_map) {
this.message_file_map[latestMessage.messageId] = attachments;
} else {
this.message_file_map = {
[latestMessage.messageId]: attachments,
};
}
const [, files] = await Promise.all([
this.addFileContextToMessage(latestMessage, attachments),
this.processAttachments(latestMessage, attachments),
]);
this.options.attachments = files;
} else {
agentScopedContextPromise = startAgentScopedContext();
}
/** Note: Bedrock uses legacy RAG API handling */
if (this.message_file_map && !isAgentsEndpoint(this.options.endpoint)) {
this.contextHandlers = createContextHandlers(
this.options.req,
orderedMessages[orderedMessages.length - 1].text,
);
}
/** @type {Record<number, number>} */
const indexTokenCountMap = {};
/** @type {Record<string, number>} */
const tokenCountMap = {};
const memoryPayload = [];
let hasFileContext = false;
let promptTokenTotal = 0;
const encoding = this.getEncoding();
const formattedMessages = orderedMessages.map((message, i) => {
const formattedMessage = formatMessage({
message,
userName: this.options?.name,
assistantName: this.options?.modelLabel,
});
const memoryFormattedMessage = formatMessage({
message,
userName: this.options?.name,
assistantName: this.options?.modelLabel,
});
/**
* Bind file context to the message it belongs to. Historical attachments
* are resent inline, so the current turn's text attachment must be inline
* too instead of living only in the dynamic system tail.
*/
if (message.fileContext) {
hasFileContext = true;
prependFileContext(formattedMessage, message.fileContext);
}
/**
* Durably re-merge quoted excerpts into every user turn that carries them
* (current and historical) so the model receives the referenced context on
* every prompt and the token count matches what was persisted. Applied to
* the memory copy too so the canonical per-message count includes them.
*/
if (Array.isArray(message.quotes) && message.quotes.length > 0) {
prependQuotes(formattedMessage, message.quotes);
prependQuotes(memoryFormattedMessage, message.quotes);
}
memoryPayload.push(memoryFormattedMessage);
const dbTokenCount = Number(orderedMessages[i].tokenCount);
const hasDbTokenCount = Number.isFinite(dbTokenCount) && dbTokenCount > 0;
/**
* Force a recount when the message carries quotes: a plain text-only
* "Save" edit recomputes `tokenCount` from `text` alone while leaving
* `message.quotes` persisted, so the stored count would undercount the
* quote block this turn prepends. Recounting from the quote-merged memory
* copy keeps context accounting accurate (and self-heals stale counts).
*/
const needsCanonicalTokenCount =
!hasDbTokenCount ||
(this.isVisionModel && (message.image_urls || message.files)) ||
(Array.isArray(message.quotes) && message.quotes.length > 0);
let canonicalTokenCount = hasDbTokenCount ? dbTokenCount : 0;
if (needsCanonicalTokenCount) {
canonicalTokenCount = countFormattedMessageTokens(memoryFormattedMessage, encoding);
}
const promptMessageTokenCount = message.fileContext
? countFormattedMessageTokens(formattedMessage, encoding)
: canonicalTokenCount;
/* If message has files, calculate image token cost */
if (this.message_file_map && this.message_file_map[message.messageId]) {
const attachments = this.message_file_map[message.messageId];
for (const file of attachments) {
if (file.embedded) {
this.contextHandlers?.processFile(file);
continue;
}
if (file.metadata?.codeEnvRef) {
continue;
}
}
}
const normalizedCanonicalTokenCount =
Number.isFinite(canonicalTokenCount) && canonicalTokenCount > 0 ? canonicalTokenCount : 0;
const normalizedPromptTokenCount =
Number.isFinite(promptMessageTokenCount) && promptMessageTokenCount > 0
? promptMessageTokenCount
: 0;
orderedMessages[i].tokenCount = normalizedCanonicalTokenCount;
indexTokenCountMap[i] = normalizedPromptTokenCount;
promptTokenTotal += normalizedPromptTokenCount;
if (message.messageId) {
tokenCountMap[message.messageId] = normalizedCanonicalTokenCount;
}
if (isEnabled(process.env.AGENT_DEBUG_LOGGING)) {
const role = message.isCreatedByUser ? 'user' : 'assistant';
const hasSummary =
Array.isArray(message.content) && message.content.some((p) => p && p.type === 'summary');
const suffix = hasSummary ? '[S]' : '';
const id = (message.messageId ?? message.id ?? '').slice(-8);
const recalced = needsCanonicalTokenCount ? normalizedCanonicalTokenCount : null;
const promptRecalced = message.fileContext ? normalizedPromptTokenCount : null;
logger.debug(
`[AgentClient] msg[${i}] ${role}${suffix} id=…${id} db=${dbTokenCount} needsRecount=${needsCanonicalTokenCount} recalced=${recalced} promptRecalced=${promptRecalced} tokens=${normalizedPromptTokenCount}`,
);
}
return formattedMessage;
});
/**
* Native YouTube -> video understanding: when Google `url_context` is enabled
* (resolved to the native `urlContext` provider tool), inject any YouTube URLs
* from the latest user turn as Gemini `fileData` video parts. The URL Context
* tool cannot read YouTube, so this routes those links through the video path
* while other URLs still flow through `urlContext`. Done after token counting
* (video tokens are reported by the provider) and only on the LLM payload, so
* the memory copy and persisted message are untouched.
*/
const latestOrdered = orderedMessages[orderedMessages.length - 1];
const provider = this.options.agent?.provider;
if (
latestOrdered?.isCreatedByUser === true &&
(provider === Providers.GOOGLE || provider === Providers.VERTEXAI) &&
hasUrlContextTool(this.options.agent?.tools)
) {
const latestFormatted = formattedMessages[formattedMessages.length - 1];
/** Use the resolved run model (model_parameters override) rather than the saved base model. */
const resolvedModel =
this.options.agent?.model_parameters?.model ?? this.options.agent?.model;
const { max, mimeType } = resolveYouTubeInjectionConfig({
provider,
model: resolvedModel,
});
latestFormatted.content = appendYouTubeVideoParts({
enabled: true,
text: latestOrdered.text,
content: latestFormatted.content,
max,
mimeType,
});
/** Google rejects an unusable video with a generic `INVALID_ARGUMENT` that names no cause,
* so `#sendCompletion` can only attribute one by knowing this turn carried a video. */
this.injectedYouTubeVideo = hasYouTubeVideoParts(latestFormatted.content);
}
payload = formattedMessages;
if (this.options.resendFiles) {
/** Persisted steer parts of past turns replay with their attachments:
* one batched owner-scoped fetch, re-encoded per turn and stamped as a
* transient `media` array (same resend semantics as message files).
* The stamp lands after the loop above finalized its counts, so the
* re-encoded media (minus the text part the steer part already counted)
* is folded into the budget here — large steered attachments must
* shrink the window like any other resent media. */
const stamped = await stampSteerPartMedia({
client: this,
user: this.options.req?.user,
payload,
// addPreviousAttachments already fetched steer-part refs in its single
// per-turn historical-files query — no second round trip.
docsById: this.authorizedHistoricalFiles,
getFiles: db.getFiles,
});
for (const { index, media, steerText } of stamped) {
/** Count the FULL stamped content and subtract only the steer body
* (already counted inside the assistant message): extracted file
* context prepended into the text part must hit the budget too, or
* large steered documents bypass pruning. */
const fullTokens = countFormattedMessageTokens({ role: 'user', content: media }, encoding);
const bodyTokens = steerText
? countFormattedMessageTokens(
{ role: 'user', content: [{ type: ContentTypes.TEXT, text: steerText }] },
encoding,
)
: 0;
const mediaTokens = Math.max(0, (fullTokens ?? 0) - (bodyTokens ?? 0));
if (Number.isFinite(mediaTokens) && mediaTokens > 0) {
indexTokenCountMap[index] = (indexTokenCountMap[index] ?? 0) + mediaTokens;
promptTokenTotal += mediaTokens;
}
}
}
this.memoryPayload = hasFileContext ? memoryPayload : null;
messages = orderedMessages;
promptTokens = promptTokenTotal;
/**
* Build shared run context - applies to ALL agents in the run.
* Request attachment file context is already bound inline to the latest
* user message above; only side-channel context belongs here.
* Memory context is handled separately and applied per-agent based on config.
*/
const sharedRunContextParts = [];
const [augmentedPrompt, [memories, configServers], agentScopedContext] = await Promise.all([
this.contextHandlers?.createContext(),
earlySharedContextPromise,
agentScopedContextPromise,
]);
/** Augmented prompt from RAG/context handlers */
this.augmentedPrompt = augmentedPrompt;
if (this.augmentedPrompt) {
sharedRunContextParts.push(this.augmentedPrompt);
}
/** Memory context (user preferences/memories). Keyed context (with memory
* keys + token metadata) is reserved for agents that can call
* `delete_memory`; everyone else gets the unkeyed values only. */
/** Partition the loaded memories belong to (the primary agent's). */
const loadedMemoryAgentId = getMemoryAgentId(this.options.agent);
const buildMemoryContext = (text) =>
text ? `${memoryInstructions}\n\n# Existing memory about the user:\n${text}` : undefined;
/** Resolves formatted memories for an agent's own partition. A defined
* `memories` means the run-level gates (permission, opt-out, config)
* passed; agents on other partitions fetch through the request-scoped
* cache so repeated partitions share one query. */
const getAgentPartitionMemories = async (agent) => {
if (!memories) {
return undefined;
}
const agentPartition = getMemoryAgentId(agent);
if (agentPartition === loadedMemoryAgentId) {
return memories;
}
try {
return await getRequestMemories({
req: this.options.req,
userId: this.options.req.user.id + '',
agentId: agentPartition,
getFormattedMemories: db.getFormattedMemories,
});
} catch (error) {
logger.error('[AgentClient] Error loading partition memories', error);
return undefined;
}
};
const sharedRunContext = sharedRunContextParts.join('\n\n');
const memoryAgentEnabled = isMemoryAgentEnabled(this.options.req.config?.memory);
/** Preserve prompt token counts for graph formatting and pruning. */
this.indexTokenCountMap = indexTokenCountMap;
/** Extract contextMeta from the parent response (second-to-last in ordered chain;
* last is the current user message). Seeds the pruner's calibration EMA for this run. */
const parentResponse =
orderedMessages.length >= 2 ? orderedMessages[orderedMessages.length - 2] : undefined;
if (parentResponse?.contextMeta && !parentResponse.isCreatedByUser) {
this.contextMeta = parentResponse.contextMeta;
}
const result = {
prompt: payload,
tokenCountMap,
promptTokens,
messages,
};
if (promptTokens >= 0 && typeof opts?.getReqData === 'function') {
opts.getReqData({ promptTokens });
}
/**
* Apply context to all agents.
* Stable agent/MCP instructions stay on `instructions`; shared runtime context
* is appended to `additional_instructions` as the dynamic system tail.
*
* NOTE: This intentionally mutates agent objects in place. The agentConfigs Map
* holds references to config objects that will be passed to the graph runtime.
*/
const ephemeralAgent = this.options.req.body.ephemeralAgent;
const mcpManager = getMCPManager();
await Promise.all(
allAgents.map(async ({ agent, agentId }) => {
const agentRunContextParts = [sharedRunContext];
const agentHasMemory = agentHasInlineMemoryTools(agent);
if (agentId === this.options.agent.id || memoryAgentEnabled || agentHasMemory) {
const partitionMemories = await getAgentPartitionMemories(agent);
const agentMemoryContext = buildMemoryContext(
agentHasMemory ? partitionMemories?.withKeys : partitionMemories?.withoutKeys,
);
if (agentMemoryContext) {
agentRunContextParts.push(agentMemoryContext);
}
}
const scopedContext = agentScopedContext.get(agentId);
if (scopedContext) {
agentRunContextParts.push(scopedContext);
}
return applyContextToAgent({
agent,
agentId,
logger,
mcpManager,
configServers,
sharedRunContext: agentRunContextParts.filter(Boolean).join('\n\n'),
ephemeralAgent: agentId === this.options.agent.id ? ephemeralAgent : undefined,
});
}),
);
return result;
}
/**
* Creates a promise that resolves with the memory promise result or undefined after a timeout
* @param {Promise<(TAttachment | null)[] | undefined>} memoryPromise - The memory promise to await
* @param {number} timeoutMs - Timeout in milliseconds (default: 3000)
* @returns {Promise<(TAttachment | null)[] | undefined>}
*/
async awaitMemoryWithTimeout(memoryPromise, timeoutMs = 3000) {
if (!memoryPromise) {
return;
}
try {
const timeoutPromise = new Promise((_, reject) =>
setTimeout(() => reject(new Error('Memory processing timeout')), timeoutMs),
);
const attachments = await Promise.race([memoryPromise, timeoutPromise]);
return attachments;
} catch (error) {
if (error.message === 'Memory processing timeout') {
logger.warn('[AgentClient] Memory processing timed out after 3 seconds');
} else {
logger.error('[AgentClient] Error processing memory:', error);
}
return;
}
}
/**
* @returns {Promise<{ withKeys?: string; withoutKeys?: string } | undefined>}
*/
async useMemory() {
const user = this.options.req.user;
if (user.personalization?.memories === false) {
return;
}
const hasAccess = await checkAccess({
user,
permissionType: PermissionTypes.MEMORIES,
permissions: [Permissions.USE],
getRoleByName: db.getRoleByName,
});
if (!hasAccess) {
logger.debug(
`[api/server/controllers/agents/client.js #useMemory] User ${user.id} does not have USE permission for memories`,
);
return;
}
const appConfig = this.options.req.config;
const memoryConfig = appConfig.memory;
if (!memoryConfig || memoryConfig.disabled === true) {
return;
}
const userId = this.options.req.user.id + '';
/** Memory partition of the primary agent; undefined = shared personal pool */
const memoryAgentId = getMemoryAgentId(this.options.agent);
this.processMemory = undefined;
if (!isMemoryAgentEnabled(memoryConfig)) {
try {
const { withKeys, withoutKeys } = await getRequestMemories({
req: this.options.req,
userId,
agentId: memoryAgentId,
getFormattedMemories: db.getFormattedMemories,
});
return { withKeys, withoutKeys };
} catch (error) {
logger.error(
'[api/server/controllers/agents/client.js #useMemory] Error loading memories',
error,
);
return;
}
}
/** @type {Agent} */
let prelimAgent;
const allowedProviders = new Set(
appConfig?.endpoints?.[EModelEndpoint.agents]?.allowedProviders,
);
try {
if (memoryConfig.agent?.id != null && memoryConfig.agent.id !== this.options.agent.id) {
prelimAgent = await loadAgent({
req: this.options.req,
agent_id: memoryConfig.agent.id,
endpoint: EModelEndpoint.agents,
});
} else if (memoryConfig.agent?.id != null) {
prelimAgent = this.options.agent;
} else if (
memoryConfig.agent?.id == null &&
memoryConfig.agent?.model != null &&
memoryConfig.agent?.provider != null
) {
prelimAgent = { id: Constants.EPHEMERAL_AGENT_ID, ...memoryConfig.agent };
}
} catch (error) {
logger.error(
'[api/server/controllers/agents/client.js #useMemory] Error loading agent for memory',
error,
);
}
if (!prelimAgent) {
return;
}
/** Forward the same `execute_code` capability gate the chat flow uses —
* memory agents are unlikely to list `execute_code`, but if one does,
* Phase 8 relies on this flag to expand the string into
* `bash_tool` + `read_file` (pre-Phase 8 the legacy `execute_code`
* tool registered unconditionally; without this passthrough the
* memory path would silently lose code-execution tooling). */
const memoryCapabilities = new Set(appConfig?.endpoints?.[EModelEndpoint.agents]?.capabilities);
const agent = await initializeAgent(
{
req: this.options.req,
res: this.options.res,
agent: prelimAgent,
allowedProviders,
endpointOption: {
endpoint: !isEphemeralAgentId(prelimAgent.id)
? EModelEndpoint.agents
: memoryConfig.agent?.provider,
},
codeEnvAvailable: memoryCapabilities.has(AgentCapabilities.execute_code),
statefulSessionsAvailable: memoryCapabilities.has(AgentCapabilities.stateful_code_sessions),
},
{
getFiles: db.getFiles,
getUserKey: db.getUserKey,
getConvoFiles: db.getConvoFiles,
updateFilesUsage: db.updateFilesUsage,
getUserKeyValues: db.getUserKeyValues,
getToolFilesByIds: db.getToolFilesByIds,
getCodeGeneratedFiles: db.getCodeGeneratedFiles,
filterFilesByAgentAccess,
},
);
if (!agent) {
logger.warn(
'[api/server/controllers/agents/client.js #useMemory] No agent found for memory',
memoryConfig,
);
return;
}
const llmConfig = Object.assign(
{
provider: agent.provider,
model: agent.model,
},
agent.model_parameters,
);
/** @type {import('@librechat/api').MemoryConfig} */
const config = {
validKeys: memoryConfig.validKeys,
instructions: agent.instructions,
llmConfig,
tokenLimit: memoryConfig.tokenLimit,
};
const messageId = this.responseMessageId + '';
const conversationId = this.conversationId + '';
const streamId = this.options.req?._resumableStreamId || null;
const [withoutKeys, processMemory] = await createMemoryProcessor({
userId,
agentId: memoryAgentId,
config,
messageId,
streamId,
jobCreatedAt: this.jobCreatedAt,
conversationId,
memoryMethods: {
setMemory: db.setMemory,
deleteMemory: db.deleteMemory,
getFormattedMemories: db.getFormattedMemories,
},
res: this.options.res,
user: createSafeUser(this.options.req.user),
});
this.processMemory = processMemory;
let withKeys = withoutKeys;
try {
({ withKeys } = await getRequestMemories({
req: this.options.req,
userId,
agentId: memoryAgentId,
getFormattedMemories: db.getFormattedMemories,
}));
} catch (error) {
logger.error(
'[api/server/controllers/agents/client.js #useMemory] Error loading keyed memories',
error,
);
}
return { withKeys, withoutKeys };
}
/**
* Filters out image URLs from message content
* @param {BaseMessage} message - The message to filter
* @returns {BaseMessage} - A new message with image URLs removed
*/
filterImageUrls(message) {
if (!message.content || typeof message.content === 'string') {
return message;
}
if (Array.isArray(message.content)) {
const filteredContent = message.content.filter(
(part) => part.type !== ContentTypes.IMAGE_URL,
);
if (filteredContent.length === 1 && filteredContent[0].type === ContentTypes.TEXT) {
const MessageClass = message.constructor;
return new MessageClass({
content: filteredContent[0].text,
additional_kwargs: message.additional_kwargs,
});
}
const MessageClass = message.constructor;
return new MessageClass({
content: filteredContent,
additional_kwargs: message.additional_kwargs,
});
}
return message;
}
/**
* @param {BaseMessage[]} messages
* @returns {Promise<void | (TAttachment | null)[]>}
*/
async runMemory(messages) {
try {
if (this.processMemory == null) {
return;
}
const appConfig = this.options.req.config;
const memoryConfig = appConfig.memory;
const messageWindowSize = memoryConfig?.messageWindowSize ?? 5;
/**
* Strip skill-primed meta messages before memory extraction. The primes
* sit next to the latest user message and carry large SKILL.md bodies,
* so letting them into the window would crowd out real chat turns and
* pollute extracted memories with synthetic instruction content the
* user never typed.
*/
const chatMessages = messages.filter((m) => !isSkillPrimeMessage(m));
let messagesToProcess = [...chatMessages];
if (chatMessages.length > messageWindowSize) {
for (let i = chatMessages.length - messageWindowSize; i >= 0; i--) {
const potentialWindow = chatMessages.slice(i, i + messageWindowSize);
if (potentialWindow[0]?.role === 'user') {
messagesToProcess = [...potentialWindow];
break;
}
}
if (messagesToProcess.length === chatMessages.length) {
messagesToProcess = [...chatMessages.slice(-messageWindowSize)];
}
}
const filteredMessages = messagesToProcess.map((msg) => this.filterImageUrls(msg));
const bufferString = getBufferString(filteredMessages);
const configuredMaxInputTokens = Number.isFinite(memoryConfig?.maxInputTokens)
? Math.floor(memoryConfig.maxInputTokens)
: undefined;
const maxInputTokens =
configuredMaxInputTokens != null && configuredMaxInputTokens > 0
? configuredMaxInputTokens
: DEFAULT_MEMORY_MAX_INPUT_TOKENS;
const maxInputChars = maxInputTokens * MEMORY_INPUT_CHARS_PER_TOKEN;
const isCharTruncated = bufferString.length > maxInputChars;
const memoryInput = `# Current Chat:\n\n${
isCharTruncated
? `[Earlier chat content omitted due to memory input limit]\n\n${bufferString.slice(
-maxInputChars,
)}`
: bufferString
}`;
const {
text: limitedMemoryInput,
tokenCount,
wasTruncated,
} = await processTextWithTokenLimit({
text: memoryInput,
tokenLimit: maxInputTokens,
tokenCountFn: (text) => countTokens(text),
preserve: 'end',
});
if (isCharTruncated || wasTruncated) {
logger.warn('[MemoryAgent] Memory input truncated before processing', {
tokenCount,
messageId: this.responseMessageId,
conversationId: this.conversationId,
maxInputTokens,
wasTruncated,
maxInputChars,
originalLength: bufferString.length,
});
}
const bufferMessage = new HumanMessage(limitedMemoryInput);
return await this.processMemory([bufferMessage]);
} catch (error) {
logger.error('Memory Agent failed to process memory', error);
}
}
/** @type {sendCompletion} */
async sendCompletion(payload, opts = {}) {
await this.chatCompletion({
payload,
onProgress: opts.onProgress,
userMCPAuthMap: opts.userMCPAuthMap,
abortController: opts.abortController,
});
const completion = filterMalformedContentParts(this.contentParts);
const metadata = this.buildResponseMetadata();
return metadata ? { completion, metadata } : { completion };
}
/**
* Assembles the response message `metadata`: Vertex thought signatures plus
* the persisted context breakdown (Part A) and the usage/cost rollup (Part B),
* which rebuild the gauge breakdown and branch/total cost across reloads.
* Returns undefined when nothing was captured.
* @returns {{
* thoughtSignatures?: Record<string, string>,
* contextUsage?: import('librechat-data-provider').TContextUsageEvent,
* usage?: import('librechat-data-provider').TResponseUsage,
* } | undefined}
*/
buildResponseMetadata() {
/** @type {{
* thoughtSignatures?: Record<string, string>,
* contextUsage?: import('librechat-data-provider').TContextUsageEvent,
* usage?: import('librechat-data-provider').TResponseUsage,
* }} */
const metadata = {};
const signatures = this.collectedThoughtSignatures;
if (signatures && Object.keys(signatures).length > 0) {
metadata.thoughtSignatures = signatures;
}
const usageEvents = this.usageEmitSink ?? [];
/** Persist the breakdown only when the latest snapshot's OWN run completed —
* i.e. a PRIMARY usage event (usage_type == null) from that run's id arrived
* AFTER the snapshot. Matching by run id keeps `completedOutputTokens` a real
* post-snapshot delta even when parallel/direct runs interleave (A snapshot →
* B snapshot → A usage must NOT persist B's snapshot with A's output); an
* interrupted final call that emits no usage falls back to the per-message
* estimate. It still keeps the post-summary snapshot: the summarization detour
* emits an extra snapshot whose following primary usage shares that run's id,
* which the old snapshot-count guard miscounted and wrongly dropped. Events
* without a run id (older lib / resume) match any snapshot for back-compat. */
const latestSnapshot = this.contextUsageSink?.latest;
const latestSnapshotUsageIndex = this.contextUsageSink?.latestUsageIndex ?? 0;
const latestSnapshotRunId = latestSnapshot?.runId;
const hasPrimaryAfterSnapshot = usageEvents
.slice(latestSnapshotUsageIndex)
.some(
(event) =>
event.usage_type == null &&
(latestSnapshotRunId == null ||
event.runId == null ||
event.runId === latestSnapshotRunId),
);
if (latestSnapshot && hasPrimaryAfterSnapshot) {
metadata.contextUsage = buildPersistedContextUsage(latestSnapshot, usageEvents);
}
/** Lightweight summarization marker — persisted whenever this turn compacted
* the context, INDEPENDENT of the snapshot guard above. When the client has
* no usable snapshot on the branch and falls back to the per-message
* estimate, it caps the discarded pre-summary history at this baseline
* instead of re-summing it (the gauge otherwise reads 100% forever). Shared
* with the abort save path via `computeSummaryUsedTokens`. Subtract the
* response's earlier tool-loop outputs (the primaries that preceded the
* latest snapshot, same run): those tokens are inside the snapshot baseline
* AND in the response `tokenCount` the client estimate adds on top, so
* leaving them in the marker double-counts them on a multi-call turn. */
const priorOutputTokens = priorRunOutputTokens(
usageEvents,
latestSnapshotUsageIndex,
latestSnapshotRunId,
);
const summaryUsedTokens = computeSummaryUsedTokens(latestSnapshot, priorOutputTokens);
if (summaryUsedTokens != null) {
metadata.summaryUsedTokens = summaryUsedTokens;
}
const usage = aggregateEmittedUsage(usageEvents);
if (usage) {
metadata.usage = usage;
}
return Object.keys(metadata).length > 0 ? metadata : undefined;
}
/**
* Resolves the endpoint token config for a usage item by its producing agent
* (multi-endpoint graphs: connected agents + subagents). A known agent's
* config is authoritative — including `undefined`, which prices with built-in
* rates (e.g. a non-custom agent in a custom-primary graph). Only an
* untagged/unknown agent falls back to the primary config, so single-endpoint
* graphs are unchanged.
* @param {UsageMetadata} usage
* @returns {import('@librechat/api').EndpointTokenConfig | undefined}
*/
resolveAgentEndpointTokenConfig(usage) {
return resolveAgentTokenConfig({
agentId: usage?.agentId,
byAgentId: this.options.endpointTokenConfigByAgentId,
fallback: this.options.endpointTokenConfig,
});
}
/**
* @param {Object} params
* @param {string} [params.model]
* @param {string} [params.context='message']
* @param {AppConfig['balance']} [params.balance]
* @param {AppConfig['transactions']} [params.transactions]
* @param {UsageMetadata[]} [params.collectedUsage=this.collectedUsage]
*/
async recordCollectedUsage({
model,
balance,
transactions,
context = 'message',
collectedUsage = this.collectedUsage,
/**
* Rates for usage that did NOT run on the agent's endpoint — currently
* activity labels pointed at a different `activityEndpoint`. Without it
* the caller's config was dropped here and the balance transaction was
* written at the primary agent's rates while the UI cost was computed at
* the label's, so the two disagreed. `undefined` keeps the agent default.
*/
endpointTokenConfig,
/**
* True when this usage ran on a DIFFERENT endpoint than the agent, making
* `endpointTokenConfig` authoritative even when it is `undefined` (a
* built-in endpoint prices from the shared table). Presence of the value
* cannot express that, which is why the caller states it outright.
*/
crossEndpoint = false,
/**
* Whether this recording owns `getStreamUsage()`. Only the PRIMARY
* generation does. Secondary usage (activity labels) must still be
* billed, but writing it here would hand `BaseClient` the label's token
* counts as the assistant response's authoritative total — and because
* the primary call returns early when it collected nothing, the wrong
* value would never be replaced, suppressing the text-based token
* fallback and leaving the real generation unbilled.
*/
updateStreamUsage = true,
}) {
/** Per-agent resolution keys off the AGENT's config map, which cannot
* describe a label running on a different endpoint — so an explicit
* config wins outright rather than being second-guessed per usage row.
*
* Keyed on the caller's discriminator, NOT on `endpointTokenConfig !==
* undefined`: a built-in label endpoint prices from the shared table, so
* `undefined` is its meaningful value. Reading that as "no override" is
* what silently restored the primary's custom rates. */
const overrideTokenConfig = crossEndpoint === true;
const result = await recordCollectedUsage(
{
spendTokens: db.spendTokens,
spendStructuredTokens: db.spendStructuredTokens,
pricing: { getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
bulkWriteOps: { insertMany: db.bulkInsertTransactions, updateBalance: db.updateBalance },
},
{
user: this.user ?? this.options.req.user?.id,
conversationId: this.conversationId,
collectedUsage,
model: model ?? this.model ?? this.options.agent.model_parameters.model,
context,
messageId: this.responseMessageId,
balance,
transactions,
endpointTokenConfig: overrideTokenConfig
? endpointTokenConfig
: this.options.endpointTokenConfig,
...(overrideTokenConfig
? {}
: { resolveEndpointTokenConfig: (usage) => this.resolveAgentEndpointTokenConfig(usage) }),
},
);
if (result && updateStreamUsage) {
this.usage = result;
}
}
/**
* Get stream usage as returned by this client's API response.
* @returns {UsageMetadata} The stream usage object.
*/
getStreamUsage() {
return this.usage;
}
/**
* Builds the subagent usage emitter for {@link createSubagentUsageSink}.
* Streams each billed child-run usage to the client as an `on_token_usage`
* event tagged `subagent` (folds into session cost/totals, not the live
* gauge), with the authoritative cost when `interface.contextCost` is on.
* Returns undefined when there's no stream to write to.
* @param {AppConfig} [appConfig]
* @returns {((usage: UsageMetadata) => void) | undefined}
*/
buildSubagentUsageEmitter(appConfig) {
const res = this.options.res;
const streamId = this.options.req?._resumableStreamId || null;
if (!res && !streamId) {
return undefined;
}
const includeCost = appConfig?.interfaceConfig?.contextCost === true;
return (usage) => {
const data = {
input_tokens: usage.input_tokens,
output_tokens: usage.output_tokens,
total_tokens: usage.total_tokens,
input_token_details: this.subagentCacheDetails(usage),
model: usage.model,
provider: usage.provider,
usage_type: 'subagent',
runId: this.responseMessageId,
/** Unique per collected entry (post-push length) for resume dedupe */
seq: this.collectedUsage.length,
/** Price with the SUBAGENT's own endpoint token config (its endpoint may
* differ from the parent's); `usage.agentId` is tagged by the sink. */
cost: includeCost
? computeUsageCostUSD(
usage,
{ getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
this.resolveAgentEndpointTokenConfig(usage),
)
: undefined,
};
/** Fold into the response's usage rollup (synchronously, regardless of
* emit success) so the persisted total matches the live session, which
* also folds subagent usage into its cost/totals. */
if (this.usageEmitSink) {
this.usageEmitSink.push(data);
}
/** The sink fires this without awaiting, so retain the promise and flush
* it in chatCompletion's finally — emitChunk persists (HSET) before
* publishing, and job cleanup must not race that persist or resumed
* clients miss billed subagent usage. */
const emit = (async () => {
try {
if (streamId) {
await GenerationJobManager.emitChunk(
streamId,
{
event: UsageEvents.ON_TOKEN_USAGE,
data,
},
{ expectedCreatedAt: this.jobCreatedAt },
);
} else {
sendEvent(res, { event: UsageEvents.ON_TOKEN_USAGE, data });
}
} catch (err) {
logger.warn('[AgentClient] Failed to emit subagent usage', err);
}
})();
this.pendingSubagentEmits.push(emit);
return emit;
};
}
/** Normalizes a subagent usage event's cache token details for emission. */
subagentCacheDetails(usage) {
const cache_creation =
usage.input_token_details?.cache_creation ?? usage.cache_creation_input_tokens;
const cache_read = usage.input_token_details?.cache_read ?? usage.cache_read_input_tokens;
if (cache_creation == null && cache_read == null) {
return undefined;
}
return { cache_creation, cache_read };
}
/**
* @param {TMessage} responseMessage
* @returns {number}
*/
getTokenCountForResponse({ content }) {
return countFormattedMessageTokens({ role: 'assistant', content }, this.getEncoding());
}
/**
* @param {object} params
* @param {string | ChatCompletionMessageParam[]} params.payload
* @param {Record<string, Record<string, string>>} [params.userMCPAuthMap]
* @param {AbortController} [params.abortController]
*/
/**
* @deprecated Agent Chain — strip hidden intermediate sequential-agent content
* before persistence, keeping only the last part + tool_call parts. Mirrors the
* chat path so a HITL resume doesn't persist/emit intermediate outputs the
* agent's `hide_sequential_outputs` setting is meant to hide.
*/
applyHideSequentialOutputsFilter() {
if (!this.options.agent?.hide_sequential_outputs || !Array.isArray(this.contentParts)) {
return;
}
this.contentParts = this.contentParts.filter(
(part, index) =>
index >= this.contentParts.length - 1 ||
part.type === ContentTypes.TOOL_CALL ||
// Steer parts are user speech, not intermediate agent output — dropping
// one would erase the user's words from the persisted turn.
part.type === ContentTypes.STEER ||
// Activity labels summarize the hidden intermediate outputs — exactly
// the affordance hide_sequential_outputs wants to keep visible.
part.type === ContentTypes.ACTIVITY_LABEL ||
part.tool_call_ids,
);
}
/**
* Surface any human-in-the-loop interrupt the SDK captured during the most
* recent `processStream` / `resume`. When the run paused for tool approval (or
* an ask-user question), mark the job `requires_action`, persist the pending
* review record, and emit it to live clients — then set `this.pendingApproval`
* so the controller leaves the turn unfinalized for the resume route to continue.
*
* No-op when the run completed without an interrupt, or when the job was aborted
* between the interrupt firing and this mark (a late interrupt must not pause a
* dead job — the atomic `pause` transition returns false and we drop it).
*
* @param {AgentRun} run
* @param {string} [streamId]
*/
async handleRunInterrupt(run, streamId) {
if (!streamId || typeof run?.getInterrupt !== 'function') {
return;
}
const interrupt = run.getInterrupt();
if (!interrupt?.payload) {
return;
}
const appConfig = this.options.req?.config;
const checkpointerCfg = appConfig?.endpoints?.[EModelEndpoint.agents]?.checkpointer;
// Persist the generation params (temperature, max tokens, custom endpoint params, …)
// so an ephemeral-agent resume continues with the SAME settings the run paused on.
// The resume payload omits them and they aren't part of the fingerprint, so without
// this the rebuilt ephemeral run falls back to defaults. The paused request body is
// the primary source (UI-form, round-trips the compact-convo schema by construction);
// the resolved llmConfig fills gaps and is sanitized — it carries provider secrets
// (apiKey, credentials) and gateway config — resume re-resolves those server-side.
// (Saved agents source params from the DB record, so this is belt-and-suspenders.)
const resumeContext = pickResumeContext(this.options.req?.body);
const resumeModelParameters = captureResumeModelParameters(
this.options.req?.body,
this.options.agent?.model_parameters,
);
if (resumeModelParameters) {
resumeContext.model_parameters = resumeModelParameters;
}
// Persist the question onto the paused ask tool_call's args NOW: an
// abandoned/expired/stopped pause never reaches the answer-resume stamp,
// and the streamed args were dropped (name-less chunks) — without this the
// unfinished turn saves an empty ask part the record card can't render.
if (interrupt.payload?.type === 'ask_user_question' && Array.isArray(this.contentParts)) {
const stamped = attachAskUserQuestionArgs(this.contentParts, interrupt.payload.question);
if (stamped !== this.contentParts) {
this.contentParts.length = 0;
this.contentParts.push(...stamped);
}
}
const pendingAction = buildPendingAction(interrupt.payload, {
streamId,
conversationId: this.conversationId,
// runId mirrors the LangGraph checkpoint namespace when the SDK provides it
// (its documented meaning), falling back to the response message id.
runId: interrupt.checkpointNs ?? this.responseMessageId,
responseMessageId: this.responseMessageId,
interruptId: interrupt.interruptId,
// thread_id was bound to conversationId at run config (config.configurable);
// fall back to it when the SDK doesn't echo threadId on the interrupt.
threadId: interrupt.threadId ?? this.conversationId,
ttlMs: getApprovalTtlMs(checkpointerCfg),
// Pin the graph-determining request fields so resume can't rebuild this paused
// run on a different agent/tool set (esp. ephemeral agents, whose agent_id is
// undefined so the id guard can't tell two configs apart).
requestFingerprint: computeAgentRequestFingerprint(this.options.req?.body ?? {}),
// Persist those same fields verbatim so the resume route can REPLAY them — a
// reload/cross-replica resume can't reconstruct the ephemeral config client-side,
// so the server restores it and rebuilds the same graph (and the fingerprint matches).
resumeContext,
});
// Job-replacement guard: streamId == conversationId is reused per conversation, so a
// newer request can replace this run's job. If this (older) run hits an interrupt
// after a replacement, pausing would flip the NEWER job to requires_action with this
// stale run's pending action, blocking fresh work behind the wrong approval. Only
// pause when the live job is still the one THIS run created (mirrors request.js).
if (this.jobCreatedAt != null) {
const liveJob = await GenerationJobManager.getJobStore().getJob(streamId);
if (!liveJob || liveJob.createdAt !== this.jobCreatedAt) {
logger.debug(`[AgentClient] Interrupt fired but job ${streamId} was replaced; not pausing`);
return;
}
}
const paused = await GenerationJobManager.approvals.pause(streamId, pendingAction);
if (!paused) {
logger.debug(
`[AgentClient] Interrupt fired but job ${streamId} was not running; not pausing`,
);
return;
}
// Capture deferred tools discovered (via tool_search) earlier in THIS turn so resume
// can replay them into createRun. The resumed graph is rebuilt with `messages: []`
// (state comes from the checkpoint), so the in-turn tool_search results that mark a
// deferred tool discovered aren't present there — without this the paused deferred
// tool would be missing from the rebuilt schema-only toolMap and resume would fail
// with "unknown tool". Inert for non-deferred turns (the set comes back empty).
try {
const runMessages =
typeof run.getRunMessages === 'function' ? run.getRunMessages() : undefined;
if (Array.isArray(runMessages) && runMessages.length > 0) {
const discovered = extractDiscoveredToolsFromHistory(runMessages);
if (discovered.size > 0) {
await GenerationJobManager.updateMetadata(
streamId,
{
discoveredTools: Array.from(discovered),
},
this.jobCreatedAt,
);
}
}
} catch (err) {
logger.warn(
`[AgentClient] Failed to capture discovered tools for resume on ${streamId}`,
err?.message ?? err,
);
}
this.pendingApproval = pendingAction;
// Release the concurrency slot this request held the MOMENT the turn is durably
// paused — before the approval card is emitted — so the user's `/resume` can
// re-acquire one immediately. Otherwise a fast Approve races the HTTP-driver
// teardown (request.js pause branch / resume.js finally) that would otherwise
// release it, and `/resume` 429s under LIMIT_CONCURRENT_MESSAGES. Idempotent via
// the flag; if it fails here, the teardown still releases (it checks the flag).
if (!this.pendingRequestReleased) {
try {
await decrementPendingRequest(this.options.req?.user?.id);
this.pendingRequestReleased = true;
} catch (err) {
logger.error(`[AgentClient] Failed to release request slot on pause ${streamId}`, err);
}
}
await GenerationJobManager.emitChunk(
streamId,
{
event: ApprovalEvents.ON_PENDING_ACTION,
data: toClientPendingAction(pendingAction),
},
{ expectedCreatedAt: this.jobCreatedAt },
);
// Steers queued before this pause stay IN the store for the whole approval
// window: `resumeState.pendingSteers` re-seeds the client's chips on
// reload, and the resumed run drains them at its first tool boundary.
// Draining here would leave the only copy in ephemeral client state — a
// reload during the pause would silently lose the user's message. New
// steers are rejected while paused (enqueue is status-guarded), and the
// requires_action TTL extension keeps the queue key alive.
logger.debug(
`[AgentClient] Paused ${streamId} for ${interrupt.payload.type} (action ${pendingAction.actionId})`,
);
}
async chatCompletion({ payload, userMCPAuthMap, abortController = null }) {
/** @type {Partial<GraphRunnableConfig>} */
let config;
/** @type {ReturnType<createRun>} */
let run;
/** @type {Promise<(TAttachment | null)[] | undefined>} */
let memoryPromise;
const appConfig = this.options.req.config;
const balanceConfig = getBalanceConfig(appConfig);
const transactionsConfig = getTransactionsConfig(appConfig);
try {
if (!abortController) {
abortController = new AbortController();
}
/** Fire-and-forget: boot the per-conversation stateful sandbox in
* parallel with generation so the first execute_code/bash call lands
* on a warm VM. No-op unless a reachable agent resolved
* `statefulCodeSessions`. */
maybePrewarmCodeSandbox({
req: this.options.req,
conversationId: this.conversationId,
agents: [this.options.agent, ...(this.agentConfigs?.values() ?? [])],
});
/** @type {AppConfig['endpoints']['agents']} */
const agentsEConfig = appConfig.endpoints?.[EModelEndpoint.agents];
config = {
runName: 'AgentRun',
configurable: {
thread_id: this.conversationId,
last_agent_index: this.agentConfigs?.size ?? 0,
user_id: this.user ?? this.options.req.user?.id,
hide_sequential_outputs: this.options.agent.hide_sequential_outputs,
requestBody: {
messageId: this.responseMessageId,
conversationId: this.conversationId,
parentMessageId: this.parentMessageId,
},
user: createSafeUser(this.options.req.user),
},
recursionLimit: resolveRecursionLimit(agentsEConfig, this.options.agent),
signal: abortController.signal,
streamMode: 'values',
version: 'v2',
};
const toolSet = buildToolSet(this.options.agent);
const tokenCounter = createTokenCounter(this.getEncoding());
/** Pre-resolve invoked skill bodies + re-prime files before formatting messages */
const skillPrimeResult = this.options.primeInvokedSkills
? await this.options.primeInvokedSkills(payload)
: undefined;
/**
* Seed `Graph.sessions` with code-env files primed across every
* reachable agent (primary, handoff/addedConvo, and nested
* subagents) plus skill-priming output. The merge logic and its
* run-wide semantics live in `buildInitialToolSessions`; see that
* helper's doc for why this is intentionally NOT per-agent.
*/
const initialSessions = buildInitialToolSessions({
skillSessions: skillPrimeResult?.initialSessions,
agents: [this.options.agent, ...(this.agentConfigs ? this.agentConfigs.values() : [])],
});
/**
* Reconstruct `reasoning_content` on prior tool-call turns: DeepSeek
* thinking-mode (#13366) or custom endpoints opting in via
* `customParams.includeReasoningHistory` (e.g. Xiaomi MiMo, Kimi).
* Walks subagents too — the opted-in endpoint may appear only as a
* nested subagent, not the primary or a top-level handoff agent.
*/
const needsReasoningContentFormat = anyAgentReplaysReasoningContent([
this.options.agent,
...(this.agentConfigs ? Array.from(this.agentConfigs.values()) : []),
]);
/**
* Skills primed fresh this turn — manual ($ popover) and always-apply
* (frontmatter). `injectSkillPrimes` (below) splices their SKILL.md
* bodies in, so `formatAgentMessages` must NOT also reconstruct the
* same names from a historical `skill` tool_call — otherwise the body
* lands twice and a prompt-cache marker can pin to the duplicated
* synthetic prefix. Names NOT primed this turn still reconstruct from
* history, preserving sticky manual re-priming across turns.
*/
const manualSkillPrimes = this.options.agent?.manualSkillPrimes;
const alwaysApplySkillPrimes = this.options.agent?.alwaysApplySkillPrimes;
const freshSkillPrimeNames = collectFreshSkillPrimeNames({
manualSkillPrimes,
alwaysApplySkillPrimes,
});
const formatOptions =
needsReasoningContentFormat || freshSkillPrimeNames.size > 0
? {
...(needsReasoningContentFormat ? { preserveReasoningContent: true } : {}),
...(freshSkillPrimeNames.size > 0
? { skipSkillBodyNames: freshSkillPrimeNames }
: {}),
}
: undefined;
let {
messages: initialMessages,
indexTokenCountMap,
summary: initialSummary,
boundaryTokenAdjustment,
} = formatAgentMessages(
stripActivityLabelParts(payload),
this.indexTokenCountMap,
toolSet,
skillPrimeResult?.skills,
formatOptions,
);
if (boundaryTokenAdjustment) {
logger.debug(
`[AgentClient] Boundary token adjustment: ${boundaryTokenAdjustment.original}${boundaryTokenAdjustment.adjusted} (${boundaryTokenAdjustment.remainingChars}/${boundaryTokenAdjustment.totalChars} chars)`,
);
}
/**
* Skill priming — both manual ($ popover) and always-apply (frontmatter).
*
* Splice + index-shift logic lives in `injectSkillPrimes`
* (packages/api/src/agents/skills.ts) so the delicate position math
* can be unit-tested in TS without standing up AgentClient. The
* resolver enforces a combined ceiling (manual-first, always-apply
* truncated first when over cap) before reaching here; the splice
* re-applies the cap as defense-in-depth. Runs for both single-
* agent and multi-agent runs; how primes interact with handoff /
* added-convo agents' per-agent state is an agents-SDK concern,
* not this layer's to gate.
*
* `manualSkillPrimes` / `alwaysApplySkillPrimes` are resolved above
* (used to build `freshSkillPrimeNames` for dedupe against historical
* skill reconstruction).
*/
if (
(manualSkillPrimes && manualSkillPrimes.length > 0) ||
(alwaysApplySkillPrimes && alwaysApplySkillPrimes.length > 0)
) {
const primeResult = injectSkillPrimes({
initialMessages,
indexTokenCountMap,
manualSkillPrimes,
alwaysApplySkillPrimes,
});
indexTokenCountMap = primeResult.indexTokenCountMap;
if (primeResult.inserted > 0) {
const manualNames = (manualSkillPrimes ?? []).map((p) => p.name);
const alwaysApplyNames = (alwaysApplySkillPrimes ?? []).map((p) => p.name);
logger.debug(
`[AgentClient] Primed ${primeResult.inserted} skill(s) at message index ${primeResult.insertIdx} — manual: [${manualNames.join(', ')}], always-apply: [${alwaysApplyNames.join(', ')}]`,
);
}
if (primeResult.alwaysApplyDropped > 0) {
logger.warn(
`[AgentClient] Dropped ${primeResult.alwaysApplyDropped} always-apply prime(s) to stay within MAX_PRIMED_SKILLS_PER_TURN.`,
);
}
}
if (indexTokenCountMap && isEnabled(process.env.AGENT_DEBUG_LOGGING)) {
const entries = Object.entries(indexTokenCountMap);
const perMsg = entries.map(([idx, count]) => {
const msg = initialMessages[Number(idx)];
const type = msg ? msg._getType() : '?';
return `${idx}:${type}=${count}`;
});
logger.debug(
`[AgentClient] Token map after format: [${perMsg.join(', ')}] (payload=${payload.length}, formatted=${initialMessages.length})`,
);
}
indexTokenCountMap = hydrateMissingIndexTokenCounts({
messages: initialMessages,
indexTokenCountMap,
tokenCounter,
});
const memoryMessages =
this.processMemory && this.memoryPayload
? formatAgentMessages(
stripActivityLabelParts(this.memoryPayload),
undefined,
toolSet,
skillPrimeResult?.skills,
formatOptions,
).messages
: initialMessages;
/**
* @param {BaseMessage[]} messages
*/
const runAgents = async (messages) => {
const agents = [this.options.agent];
// Include additional agents when:
// - agentConfigs has agents (from addedConvo parallel execution or agent handoffs)
// - Agents without incoming edges become start nodes and run in parallel automatically
if (this.agentConfigs && this.agentConfigs.size > 0) {
agents.push(...this.agentConfigs.values());
}
// TODO: needs to be added as part of AgentContext initialization
// const noSystemModelRegex = [/\b(o1-preview|o1-mini|amazon\.titan-text)\b/gi];
// const noSystemMessages = noSystemModelRegex.some((regex) =>
// agent.model_parameters.model.match(regex),
// );
// if (noSystemMessages === true && systemContent?.length) {
// const latestMessageContent = _messages.pop().content;
// if (typeof latestMessageContent !== 'string') {
// latestMessageContent[0].text = [systemContent, latestMessageContent[0].text].join('\n');
// _messages.push(new HumanMessage({ content: latestMessageContent }));
// } else {
// const text = [systemContent, latestMessageContent].join('\n');
// _messages.push(new HumanMessage(text));
// }
// }
// let messages = _messages;
// if (agent.useLegacyContent === true) {
// messages = formatContentStrings(messages);
// }
// if (
// agent.model_parameters?.clientOptions?.defaultHeaders?.['anthropic-beta']?.includes(
// 'prompt-caching',
// )
// ) {
// messages = addCacheControl(messages);
// }
if (this.processMemory) {
memoryPromise = this.runMemory(memoryMessages);
}
/** Seed calibration state from previous run if encoding matches */
const currentEncoding = this.getEncoding();
const prevMeta = this.contextMeta;
const encodingMatch = prevMeta?.encoding === currentEncoding;
const calibrationRatio =
encodingMatch && prevMeta?.calibrationRatio > 0 ? prevMeta.calibrationRatio : undefined;
if (prevMeta) {
logger.debug(
`[AgentClient] contextMeta from parent: ratio=${prevMeta.calibrationRatio}, encoding=${prevMeta.encoding}, current=${currentEncoding}, seeded=${calibrationRatio ?? 'none'}`,
);
}
const streamId = this.options.req?._resumableStreamId;
// HITL: clear any checkpoint orphaned by a prior paused turn in this
// conversation (one that expired or was aborted while paused) so this fresh
// turn starts clean instead of rehydrating a stale interrupt — thread_id is
// the stable conversationId. No-op when HITL is off or nothing is orphaned.
// Deliberately UNCONDITIONAL per HITL turn: any cheaper gate (job metadata,
// a Redis flag) can go stale across replicas/restarts and skip the prune
// exactly when an orphan exists, while these are two indexed, usually-empty
// deleteMany ops — correctness over a micro-optimization.
// The gate mirrors createRun's checkpointer condition: the approval policy
// OR an ask_user_question-capable agent (which attaches a checkpointer
// WITHOUT the approval policy) — an ask pause abandoned via job replacement
// or Stop would otherwise rehydrate here and silently duplicate context.
//
// Start the prune alongside graph construction. The all-settled barrier
// below still guarantees it completes before the graph is exposed or run.
const shouldPruneCheckpoint =
streamId &&
(isHITLEnabled(agentsEConfig?.toolApproval) || agents.some(agentRequestsAskUserQuestion));
const checkpointPrunePromise = shouldPruneCheckpoint
? deleteAgentCheckpoint(this.conversationId, agentsEConfig?.checkpointer)
: Promise.resolve();
const createRunPromise = createRun({
agents,
messages,
// This controller implements the full HITL pause/resume lifecycle (handleRunInterrupt
// persists the pending action; the /resume route rebuilds + continues the run), so it
// opts into the tool-approval wiring. Non-resumable callers (OpenAI-compat, Responses)
// leave this off so an approval-gated tool can't pause where there's no resume path.
hitlCapable: true,
toolInputValidationErrors: this.toolInputValidationErrors,
// Mid-run steering: drain queued user messages at each tool-batch
// boundary and inject them into graph state. The offset wrapper
// shifts SDK content indices past any spliced steer parts.
steering: this.buildSteerWiring(streamId),
activityLabel: this.buildActivityLabelWiring(streamId, abortController.signal),
indexTokenCountMap,
initialSummary,
initialSessions,
calibrationRatio,
runId: this.responseMessageId,
signal: abortController.signal,
customHandlers: createSteerIndexOffsetHandlers(
this.options.eventHandlers,
this.steerOffsetState,
),
requestBody: config.configurable.requestBody,
user: createSafeUser(this.options.req?.user),
tenantId: this.options.req?.user?.tenantId,
summarizationConfig: appConfig?.summarization,
appConfig,
tokenCounter,
/** Bills subagent child-run model calls — child graphs execute
* outside the streamEvents loop, so ModelEndHandler never sees
* them. Entries land in collectedUsage tagged
* `usage_type: 'subagent'` and are spent by recordCollectedUsage.
* The sink also streams each as an `on_token_usage` event so the
* gauge's session cost/totals include billed subagent usage (the
* `subagent` tag keeps it out of the live context meter). */
subagentUsageSink: createSubagentUsageSink(
this.collectedUsage,
this.buildSubagentUsageEmitter(appConfig),
),
}).then((createdRun) => {
if (!createdRun) {
throw new Error('Failed to create run');
}
this.options.startupTelemetry?.mark('run_created');
return createdRun;
});
const [createRunResult, checkpointPruneResult] = await Promise.allSettled([
createRunPromise,
checkpointPrunePromise,
]);
if (createRunResult.status === 'rejected') {
throw createRunResult.reason;
}
if (checkpointPruneResult.status === 'rejected') {
throw checkpointPruneResult.reason;
}
run = createRunResult.value;
this.run = run;
if (this._resolveRun) {
this._resolveRun(run);
this._resolveRun = null;
}
if (streamId && run.Graph) {
GenerationJobManager.setGraph(streamId, run.Graph, this.jobCreatedAt);
}
if (userMCPAuthMap != null) {
config.configurable.userMCPAuthMap = userMCPAuthMap;
}
/** @deprecated Agent Chain */
config.configurable.last_agent_id = agents[agents.length - 1].id;
this.options.startupTelemetry?.mark('stream_processing_started');
/** Flag durable BEFORE the run can claim a label: gap reconciliation
* is gated on it, and ordering it here (one settled-on-failure
* await) keeps the claim-time reservation emit immediate — see
* `emitLabelEvent` in buildActivityLabelWiring. */
if (this.activityLabelsMarkedPromise != null) {
await this.activityLabelsMarkedPromise;
}
await run.processStream({ messages }, config, {
callbacks: {
[Callback.TOOL_ERROR]: logToolError,
},
});
// HITL: if the run paused for tool approval, mark the job
// `requires_action` + emit the prompt and leave the turn unfinalized
// (the resume route continues it). No-op when the run completed.
await this.handleRunInterrupt(run, streamId);
config.signal = null;
};
this.options.startupTelemetry?.mark('run_input_prepared');
await runAgents(initialMessages);
/**
* Surface a completed `skill` tool_call content part per *manually*-
* primed skill so the existing `SkillCall` frontend renderer shows
* a "Skill X loaded" card on the assistant response. Applied after
* the graph finishes to avoid clashing with the aggregator's own
* per-step content indexing. Prepended (not appended) so cards sit
* above the model's output — priming ran before the turn, the
* reply follows.
*
* Always-apply primes intentionally do NOT emit assistant-side
* cards. `extractInvokedSkillsFromPayload` scans history for
* `skill` tool_calls and feeds `primeInvokedSkills`, which is
* Phase 3's sticky-re-prime path — that's the right behavior for
* manual (user picked `$skill` once; re-prime on every subsequent
* turn from history). For always-apply, `resolveAlwaysApplySkills`
* already re-primes every turn from fresh DB state, so persisting
* the card would cause the skill body to get primed twice per
* turn starting on turn 2. The user-facing acknowledgement for
* always-apply lives on the user bubble as the pinned
* `SkillPills` row (`message.alwaysAppliedSkills`), which
* is the durable signal the user wants: "this skill auto-primes".
*
* Live streaming display of manual user-bubble pills is handled
* by `SkillPills` reading `message.manualSkills`. No
* separate SSE emit is needed here; trying to stream a mid-run
* tool_call at index 0 collided with the LLM's first text
* content, while emitting at a sparse offset pushed the card
* below the reply on finalize. Post-run unshift keeps the final
* responseMessage.content in the right order.
*/
/**
* Settle in-flight label fills BEFORE the content is reshaped below.
* A fill emits its claim-time index; the skill-card unshift and the
* hide-sequential filter both shift positions, so a fill landing after
* either would emit a stale index — and a client that already synced
* the reshaped array applies it onto the wrong part. A paused turn
* never gets a final event to repair that. Costs nothing extra: these
* are the same promises the finalization settle would wait on, and
* that later call then sees an empty pending list.
*/
await this.settleActivityLabels();
const manualPrimed = this.options.agent?.manualSkillPrimes ?? [];
if (manualPrimed.length > 0) {
const runId = this.responseMessageId ?? 'skill-prime';
const manualParts = buildSkillPrimeContentParts(manualPrimed, { runId });
this.contentParts.unshift(...manualParts);
}
this.applyHideSequentialOutputsFilter();
} catch (err) {
if (abortController.signal.aborted) {
logger.debug(
'[api/server/controllers/agents/client.js #sendCompletion] Operation aborted by user',
{ conversationId: this.conversationId, name: err?.name, code: err?.code },
);
} else {
logger.error(
'[api/server/controllers/agents/client.js #sendCompletion] Unhandled error type',
err,
);
const videoError = resolveGoogleVideoError({
error: err,
provider: this.options.agent?.provider,
hasYouTubeVideo: this.injectedYouTubeVideo,
});
this.contentParts.push({
type: ContentTypes.ERROR,
[ContentTypes.ERROR]:
videoError ??
`An error occurred while processing the request${err?.message ? `: ${err.message}` : ''}`,
});
}
} finally {
/** Capture calibration state from the run for persistence on the response message.
* Runs in finally so values are captured even on abort. */
const ratio = this.run?.getCalibrationRatio() ?? 0;
if (ratio > 0 && ratio !== 1) {
this.contextMeta = {
calibrationRatio: Math.round(ratio * 1000) / 1000,
encoding: this.getEncoding(),
};
} else {
this.contextMeta = undefined;
}
this.finalizeSubagentContent();
await this.settleActivityLabels();
/** Flush subagent usage emits the sink fired without awaiting, so their
* persist/publish completes before we return and the job is cleaned up
* (resumed clients read this persisted usage). */
if (this.pendingSubagentEmits.length > 0) {
await Promise.allSettled(this.pendingSubagentEmits);
this.pendingSubagentEmits = [];
}
try {
const attachments = await this.awaitMemoryWithTimeout(memoryPromise);
if (attachments && attachments.length > 0) {
this.artifactPromises.push(...attachments);
}
/** Skip token spending if aborted - the abort handler (abortMiddleware.js) handles it
This prevents double-spending when user aborts via `/api/agents/chat/abort` */
const wasAborted = abortController?.signal?.aborted;
if (!wasAborted) {
await this.recordCollectedUsage({
context: 'message',
balance: balanceConfig,
transactions: transactionsConfig,
});
} else {
logger.debug(
'[api/server/controllers/agents/client.js #chatCompletion] Skipping token spending - handled by abort middleware',
);
}
} catch (err) {
logger.error(
'[api/server/controllers/agents/client.js #chatCompletion] Error in cleanup phase',
err,
);
}
if (this._resolveRun) {
this._resolveRun(this.run ?? null);
this._resolveRun = null;
}
// HITL: a non-paused turn deliberately prunes nothing here. The lazy checkpointer
// (LazyMongoSaver) never persists a clean-exit checkpoint, so there is
// nothing this turn left to delete. A checkpoint orphaned by a PRIOR abandoned pause
// is cleared by the pre-run prune (before processStream) on the next turn, with the
// Mongo TTL as the backstop. Dropping this post-completion prune also removes its
// job-replacement race: an older run's late finally can no longer delete a newer
// paused run's checkpoint, because there is no longer a clean-path prune to race.
run = null;
config = null;
memoryPromise = null;
}
}
/**
* Resume a run that paused for human-in-the-loop review.
*
* The original run lives in a detached background task that exits when the run
* pauses, so resume REBUILDS the run on a fresh graph bound to the same
* `thread_id` (= conversationId) and the durable checkpointer. LangGraph rehydrates
* the paused graph state from the checkpoint; `run.resume(value)` re-enters the
* interrupted node with the user's decision. State comes from the checkpoint, so
* no message history is rebuilt here — `createRun` only needs the agent(s) to
* reconstruct the graph structure.
*
* `seedContent` is the content streamed before the pause (the assistant message +
* its tool call). In Redis mode the job store's append log already spans the pause,
* so the finalized message is complete regardless; seeding keeps the in-memory store
* complete too. The run drives events through the same `streamId`, so the client's
* open SSE receives the continuation live.
*
* Unlike `chatCompletion`, this does NOT prune the checkpoint in its `finally` — the
* resume controller owns checkpoint lifecycle (it must also clean up on failures that
* happen before this method runs, and keep the checkpoint on a re-pause).
*
* @param {object} params
* @param {Agents.ToolApprovalDecisionMap | { answer: string }} params.resumeValue
* @param {Array} [params.seedContent] - content aggregated before the pause
* @param {Array<import('@librechat/agents').RunStep>} [params.runSteps] - run steps emitted before the pause
* @param {AbortController} [params.abortController]
* @param {Pick<import('@langchain/langgraph').Command, 'update' | 'goto'>} [params.commandOptions]
*/
async resumeCompletion({
resumeValue,
seedContent = [],
runSteps = [],
abortController = null,
commandOptions,
userMCPAuthMap,
discoveredToolNames,
}) {
/** @type {Partial<GraphRunnableConfig>} */
let config;
/** @type {ReturnType<createRun>} */
let run;
const appConfig = this.options.req.config;
const balanceConfig = getBalanceConfig(appConfig);
const transactionsConfig = getTransactionsConfig(appConfig);
try {
if (!abortController) {
abortController = new AbortController();
}
/** @type {AppConfig['endpoints']['agents']} */
const agentsEConfig = appConfig.endpoints?.[EModelEndpoint.agents];
config = {
runName: 'AgentRun',
configurable: {
thread_id: this.conversationId,
last_agent_index: this.agentConfigs?.size ?? 0,
user_id: this.user ?? this.options.req.user?.id,
hide_sequential_outputs: this.options.agent.hide_sequential_outputs,
requestBody: {
messageId: this.responseMessageId,
conversationId: this.conversationId,
parentMessageId: this.parentMessageId,
},
user: createSafeUser(this.options.req.user),
},
recursionLimit: resolveRecursionLimit(agentsEConfig, this.options.agent),
signal: abortController.signal,
streamMode: 'values',
version: 'v2',
};
// Seed pre-pause content so the in-memory job store reports the complete turn
// (Redis aggregates across the pause via its append log; this covers in-memory).
if (Array.isArray(seedContent) && seedContent.length > 0) {
this.contentParts.push(...seedContent);
}
const tokenCounter = createTokenCounter(this.getEncoding());
const agents = [this.options.agent];
if (this.agentConfigs && this.agentConfigs.size > 0) {
agents.push(...this.agentConfigs.values());
}
// Re-prime skill files invoked in the pre-pause segment (mirrors the normal path's
// `primeInvokedSkills(payload)`), so an approved code/file-backed tool keeps the
// injected skill-file session refs instead of running without them. The pre-pause
// content carries the `skill` tool_calls, so it stands in for the message payload.
let skillSessions;
if (
typeof this.options.primeInvokedSkills === 'function' &&
Array.isArray(seedContent) &&
seedContent.length > 0
) {
try {
const primed = await this.options.primeInvokedSkills([
{ role: 'assistant', content: seedContent },
]);
skillSessions = primed?.initialSessions;
} catch (err) {
logger.warn(
'[api/server/controllers/agents/client.js #resumeCompletion] Failed to re-prime skill sessions',
err?.message ?? err,
);
}
}
// Seed code-env / skill tool sessions so an approved code/file/skill-backed tool
// runs with the same uploaded-file context the pre-pause run had — the rebuilt
// graph otherwise has no `Graph.sessions` entries (especially cross-replica).
const initialSessions = buildInitialToolSessions({ skillSessions, agents });
const streamId = this.options.req?._resumableStreamId;
run = await createRun({
agents,
// State (messages, tool calls) is rehydrated from the checkpoint by
// run.resume; createRun only needs the agents to rebuild the graph.
messages: [],
// The resumed run can pause AGAIN (another tool, a follow-up question), and this
// controller owns that lifecycle, so it must keep the HITL wiring on the rebuilt run.
hitlCapable: true,
toolInputValidationErrors: this.toolInputValidationErrors,
// Steering stays live across a pause/resume cycle: steers queued while
// the resumed segment runs drain at its tool-batch boundaries.
steering: this.buildSteerWiring(streamId),
// Activity labels likewise survive pause/resume: post-resume tool
// batches keep claiming slots and generating group headers.
activityLabel: this.buildActivityLabelWiring(streamId, abortController.signal),
// Replay deferred tools discovered before the pause. With `messages: []` the
// discovery scan finds nothing, so a deferred tool the paused call targets
// would be absent from the rebuilt toolMap; these names (captured at pause)
// force it back in. Undefined/empty for non-deferred turns — a harmless no-op.
discoveredToolNames,
initialSessions,
runId: this.responseMessageId,
signal: abortController.signal,
// The rebuilt graph numbers content indices from 0, but the aggregator was
// just seeded with the pre-pause parts at those same indices — shift every
// resumed step index past the seed, or the new output merges into (or, on a
// type mismatch, is silently dropped against) the pre-pause content. The
// steer wrapper composes on top: resumed indices shift by seed + any
// steer parts spliced in while the resumed segment streams.
customHandlers: createSteerIndexOffsetHandlers(
createContentIndexOffsetHandlers(
this.options.eventHandlers,
Array.isArray(seedContent) ? seedContent : [],
),
this.steerOffsetState,
),
requestBody: config.configurable.requestBody,
user: createSafeUser(this.options.req?.user),
tenantId: this.options.req?.user?.tenantId,
summarizationConfig: appConfig?.summarization,
appConfig,
tokenCounter,
subagentUsageSink: createSubagentUsageSink(
this.collectedUsage,
this.buildSubagentUsageEmitter(appConfig),
),
});
if (!run) {
throw new Error('Failed to create run for resume');
}
hydrateResumeRunSteps(runSteps, this.stepMap, run.Graph, seedContent);
this.run = run;
if (this._resolveRun) {
this._resolveRun(run);
this._resolveRun = null;
}
// Do NOT cache the rebuilt graph on resume: it was created with `messages: []`, so
// RedisJobStore.getContentParts() (which prefers a cached graph over reconstructing
// from the chunk log) would return only the resumed segment and drop the pre-pause
// assistant/tool-call content on a same-replica reload/status poll. Skipping it makes
// introspection fall back to the durable chunk reconstruction, which is complete.
// `setContentParts` still points the in-memory store at the seeded client content.
if (streamId && this.contentParts) {
GenerationJobManager.setContentParts(streamId, this.contentParts, this.jobCreatedAt);
}
// Carry the user's MCP auth into the rebuilt run so an approved MCP tool executes
// with the same OAuth/user credentials it had before the pause.
if (userMCPAuthMap != null) {
config.configurable.userMCPAuthMap = userMCPAuthMap;
}
/** @deprecated Agent Chain */
config.configurable.last_agent_id = agents[agents.length - 1].id;
/** Same flag-before-run ordering as chatCompletion's processStream. */
if (this.activityLabelsMarkedPromise != null) {
await this.activityLabelsMarkedPromise;
}
await run.resume(
resumeValue,
config,
{ callbacks: { [Callback.TOOL_ERROR]: logToolError } },
commandOptions,
);
config.signal = null;
// The model may pause AGAIN (another tool needs approval, or a follow-up
// question). Re-arm the same interrupt gate so the cycle can repeat.
await this.handleRunInterrupt(run, streamId);
// Mirror chatCompletion: settle label fills before the filter below can
// shift part positions out from under an in-flight fill's claimed index.
await this.settleActivityLabels();
// Strip hidden intermediate sequential-agent content
// before resume finalize/re-pause persistence reads `this.contentParts`, so a
// resumed sequential chain doesn't persist/emit outputs hide_sequential_outputs
// is meant to hide.
this.applyHideSequentialOutputsFilter();
} catch (err) {
if (abortController.signal.aborted) {
logger.debug(
'[api/server/controllers/agents/client.js #resumeCompletion] Aborted by user',
{
conversationId: this.conversationId,
name: err?.name,
code: err?.code,
},
);
} else {
logger.error(
'[api/server/controllers/agents/client.js #resumeCompletion] Unhandled error',
err,
);
this.contentParts.push({
type: ContentTypes.ERROR,
[ContentTypes.ERROR]: `An error occurred while resuming the request${err?.message ? `: ${err.message}` : ''}`,
});
}
} finally {
const ratio = this.run?.getCalibrationRatio() ?? 0;
if (ratio > 0 && ratio !== 1) {
this.contextMeta = {
calibrationRatio: Math.round(ratio * 1000) / 1000,
encoding: this.getEncoding(),
};
} else {
this.contextMeta = undefined;
}
this.finalizeSubagentContent();
await this.settleActivityLabels();
if (this.pendingSubagentEmits.length > 0) {
await Promise.allSettled(this.pendingSubagentEmits);
this.pendingSubagentEmits = [];
}
try {
const wasAborted = abortController?.signal?.aborted;
if (!wasAborted) {
await this.recordCollectedUsage({
context: 'message',
balance: balanceConfig,
transactions: transactionsConfig,
});
}
} catch (err) {
logger.error(
'[api/server/controllers/agents/client.js #resumeCompletion] Error in cleanup phase',
err,
);
}
if (this._resolveRun) {
this._resolveRun(this.run ?? null);
this._resolveRun = null;
}
run = null;
config = null;
}
}
/**
* Resolves with the agent run once it is initialized, or `null` if
* initialization fails. Lets immediate-mode title generation await the run
* instead of throwing when fired before `chatCompletion` assigns `this.run`.
* Rejects promptly if the provided signal aborts before the run is ready.
* @param {AbortSignal} [signal]
* @returns {Promise<AgentRun | null>}
*/
_waitForRun(signal) {
if (this.run) {
return Promise.resolve(this.run);
}
if (!this._runReady) {
this._runReady = new Promise((resolve) => {
this._resolveRun = resolve;
});
}
if (!signal) {
return this._runReady;
}
if (signal.aborted) {
return Promise.reject(new Error('Aborted before run initialization'));
}
return new Promise((resolve, reject) => {
const onAbort = () => reject(new Error('Aborted before run initialization'));
signal.addEventListener('abort', onAbort, { once: true });
this._runReady.then((run) => {
signal.removeEventListener('abort', onAbort);
resolve(run);
});
});
}
/**
* @param {Object} params
* @param {string} params.text
* @param {AbortController} params.abortController
* @param {boolean} [params.immediate] When true, the title is generated as soon
* as the request is made — the run is awaited (instead of throwing) and the
* title derives from the user's input only (`contentParts` is empty).
*/
async titleConvo({ text, abortController, immediate = false }) {
if (!this.run) {
if (!immediate) {
throw new Error('Run not initialized');
}
await this._waitForRun(abortController?.signal);
if (!this.run) {
logger.debug(
'[api/server/controllers/agents/client.js #titleConvo] Run unavailable for immediate title generation',
);
return;
}
}
const { handleLLMEnd, collected: collectedMetadata } = createMetadataAggregator();
const { req, agent } = this.options;
if (req?.body?.isTemporary) {
logger.debug(
`[api/server/controllers/agents/client.js #titleConvo] Skipping title generation for temporary conversation`,
);
return;
}
const appConfig = req.config;
let endpoint = agent.endpoint;
/** @type {import('@librechat/agents').ClientOptions} */
let clientOptions = {
model: agent.model || agent.model_parameters.model,
};
let titleProviderConfig = getProviderConfig({ provider: endpoint, appConfig });
/** @type {TEndpoint | undefined} */
const endpointConfig =
appConfig.endpoints?.all ??
appConfig.endpoints?.[endpoint] ??
titleProviderConfig.customEndpointConfig;
if (!endpointConfig) {
logger.debug(
`[api/server/controllers/agents/client.js #titleConvo] No endpoint config for "${endpoint}"`,
);
}
if (endpointConfig?.titleConvo === false) {
logger.debug(
`[api/server/controllers/agents/client.js #titleConvo] Title generation disabled for endpoint "${endpoint}"`,
);
return;
}
if (endpointConfig?.titleEndpoint && endpointConfig.titleEndpoint !== endpoint) {
try {
titleProviderConfig = getProviderConfig({
provider: endpointConfig.titleEndpoint,
appConfig,
});
endpoint = endpointConfig.titleEndpoint;
} catch (error) {
logger.warn(
`[api/server/controllers/agents/client.js #titleConvo] Error getting title endpoint config for "${endpointConfig.titleEndpoint}", falling back to default`,
error,
);
// Fall back to original provider config
endpoint = agent.endpoint;
titleProviderConfig = getProviderConfig({ provider: endpoint, appConfig });
}
}
if (
endpointConfig &&
endpointConfig.titleModel &&
endpointConfig.titleModel !== Constants.CURRENT_MODEL
) {
clientOptions.model = endpointConfig.titleModel;
}
const options = await titleProviderConfig.getOptions({
req,
endpoint,
model_parameters: clientOptions,
db: {
getUserKey: db.getUserKey,
getUserKeyValues: db.getUserKeyValues,
},
});
let provider = options.provider ?? titleProviderConfig.overrideProvider ?? agent.provider;
if (
endpoint === EModelEndpoint.azureOpenAI &&
options.llmConfig?.azureOpenAIApiInstanceName == null
) {
provider = Providers.OPENAI;
} else if (
endpoint === EModelEndpoint.azureOpenAI &&
options.llmConfig?.azureOpenAIApiInstanceName != null &&
provider !== Providers.AZURE
) {
provider = Providers.AZURE;
}
/** @type {import('@librechat/agents').ClientOptions} */
clientOptions = { ...options.llmConfig };
if (options.configOptions) {
clientOptions.configuration = options.configOptions;
}
if (clientOptions.maxTokens != null) {
delete clientOptions.maxTokens;
}
if (clientOptions?.modelKwargs?.max_completion_tokens != null) {
delete clientOptions.modelKwargs.max_completion_tokens;
}
if (clientOptions?.modelKwargs?.max_output_tokens != null) {
delete clientOptions.modelKwargs.max_output_tokens;
}
/** `omitTitleOptions` drops the Anthropic `clientOptions` carrier (thinking,
* streaming, etc.), which would also drop its `defaultHeaders` — preserve the
* original `clientOptions` object so gateway/reverse-proxy metadata still
* reaches title requests (the proxy may require it for auth/routing). Restore
* the SAME object reference, not a copy: the Vertex `createClient` closure from
* `getLLMConfig` closes over this object, so `resolveConfigHeaders` must mutate
* the very object the client is built from. */
const anthropicClientOptions = clientOptions?.clientOptions;
clientOptions = Object.assign(
Object.fromEntries(
Object.entries(clientOptions).filter(([key]) => !omitTitleOptions.has(key)),
),
);
if (anthropicClientOptions?.defaultHeaders != null && clientOptions.clientOptions == null) {
clientOptions.clientOptions = anthropicClientOptions;
}
if (
provider === Providers.GOOGLE &&
(endpointConfig?.titleMethod === TitleMethod.FUNCTIONS ||
endpointConfig?.titleMethod === TitleMethod.STRUCTURED)
) {
clientOptions.json = true;
}
/** Resolve request-based headers across provider-specific header locations:
* OpenAI `configuration.defaultHeaders`, Anthropic `clientOptions.defaultHeaders`
* (preserved above), and Google `customHeaders`.
*/
resolveConfigHeaders({
llmConfig: clientOptions,
user: createSafeUser(this.options.req?.user),
body: {
messageId: this.responseMessageId,
conversationId: this.conversationId,
parentMessageId: this.parentMessageId,
},
});
try {
const titleResult = await this.run.generateTitle({
provider,
clientOptions,
inputText: text,
contentParts: immediate ? [] : this.contentParts,
titleMethod: endpointConfig?.titleMethod,
titlePrompt: endpointConfig?.titlePrompt,
titlePromptTemplate: endpointConfig?.titlePromptTemplate,
chainOptions: {
runName: 'TitleRun',
signal: abortController.signal,
callbacks: [
{
handleLLMEnd,
},
],
configurable: {
thread_id: this.conversationId,
user_id: this.user ?? this.options.req.user?.id,
},
},
});
const collectedUsage = collectedMetadata.map((item) => {
let input_tokens, output_tokens;
if (item.usage) {
input_tokens =
item.usage.prompt_tokens || item.usage.input_tokens || item.usage.inputTokens;
output_tokens =
item.usage.completion_tokens || item.usage.output_tokens || item.usage.outputTokens;
} else if (item.tokenUsage) {
input_tokens = item.tokenUsage.promptTokens;
output_tokens = item.tokenUsage.completionTokens;
} else if (item.usage_metadata) {
input_tokens = item.usage_metadata.input_tokens;
output_tokens = item.usage_metadata.output_tokens;
}
return {
input_tokens: input_tokens,
output_tokens: output_tokens,
};
});
const balanceConfig = getBalanceConfig(appConfig);
const transactionsConfig = getTransactionsConfig(appConfig);
await this.recordCollectedUsage({
collectedUsage,
context: 'title',
model: clientOptions.model,
balance: balanceConfig,
transactions: transactionsConfig,
messageId: this.responseMessageId,
}).catch((err) => {
logger.error(
'[api/server/controllers/agents/client.js #titleConvo] Error recording collected usage',
err,
);
});
return sanitizeTitle(titleResult.title);
} catch (err) {
logger.error('[api/server/controllers/agents/client.js #titleConvo] Error', err);
return;
}
}
/**
* @param {object} params
* @param {number} params.promptTokens
* @param {number} params.completionTokens
* @param {string} [params.model]
* @param {OpenAIUsageMetadata} [params.usage]
* @param {AppConfig['balance']} [params.balance]
* @param {string} [params.context='message']
* @returns {Promise<void>}
*/
async recordTokenUsage({
model,
usage,
balance,
promptTokens,
completionTokens,
context = 'message',
}) {
try {
await db.spendTokens(
{
model,
context,
balance,
messageId: this.responseMessageId,
conversationId: this.conversationId,
user: this.user ?? this.options.req.user?.id,
endpointTokenConfig: this.options.endpointTokenConfig,
},
{ promptTokens, completionTokens },
);
if (
usage &&
typeof usage === 'object' &&
'reasoning_tokens' in usage &&
typeof usage.reasoning_tokens === 'number'
) {
await db.spendTokens(
{
model,
balance,
context: 'reasoning',
messageId: this.responseMessageId,
conversationId: this.conversationId,
user: this.user ?? this.options.req.user?.id,
endpointTokenConfig: this.options.endpointTokenConfig,
},
{ completionTokens: usage.reasoning_tokens },
);
}
} catch (error) {
logger.error(
'[api/server/controllers/agents/client.js #recordTokenUsage] Error recording token usage',
error,
);
}
}
/** Anthropic Claude models use a distinct BPE tokenizer; all others default to o200k_base. */
getEncoding() {
if (this.model && this.model.toLowerCase().includes('claude')) {
return 'claude';
}
return 'o200k_base';
}
}
module.exports = AgentClient;