* fix(distributed): make the remote LoadModel deadline configurable
The router hardcoded a 5 minute gRPC deadline for the remote LoadModel
call. Staging finishes before the timer starts, so those five minutes
cover only the worker backend's own checkpoint load and pipeline init.
A cold load of meituan-longcat/LongCat-Video-Avatar-1.5 (~83 GB) on an
ARM64 Thor worker fails at exactly 302s with DeadlineExceeded while the
backend process is still making progress (CPU time accumulating, RSS
moving as weights are mapped), so the load was cut short rather than
wedged.
Add LOCALAI_NATS_MODEL_LOAD_TIMEOUT / --model-load-timeout mirroring the
existing backend-install timeout knob, defaulting to 5m so unset
clusters keep today's behaviour.
The cold-load hold ceiling (which bounds how long one load may hold the
per-model advisory lock) was derived from the install timeout alone, so
raising the load deadline past it would have been silently clipped.
Derive it from both budgets via ModelLoadCeilingFor:
max(install + load + 5m staging margin, 25m)
With the defaults that is 15m + 5m + 5m = 25m, identical to the previous
constant, and the 25m floor means shrinking either budget can never
tighten the ceiling below what clusters relied on before.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): reap the abandoned replica when a remote load times out
The gRPC deadline on the remote LoadModel call only cancels the client
side. A backend blocked in a synchronous weight load never observes its
cancelled handler context, so when scheduleAndLoad gave up it left the
worker loading with nobody waiting for the result.
Observed on an ARM64 Thor worker loading LongCat-Video-Avatar-1.5: the
client returned DeadlineExceeded at 302s, and the backend process was
still alive 30 minutes later having pulled ~57GB from HuggingFace. Every
retry stacked another multi-GB loader on the worker; they had to be
reaped by hand via POST /api/nodes/:id/models/unload.
Send backend.stop for the exact `modelID#replicaIndex` process key we
just abandoned. The exact key matters: a bare model ID stops every
replica on that node, including healthy ones serving traffic.
Only a deadline or cancellation triggers the reap. Any other LoadModel
failure is the backend answering, which means its handler returned and
the process is idle - stopping it there would discard a warm process and
its downloaded weights. The reap is best-effort and never replaces the
load error the caller is waiting on.
The `modelID#replicaIndex` format was already hand-rolled in two places
(the worker's buildProcessKey and pkg/model's log store). Rather than add
a third, export model.BackendProcessKey from pkg/model, the lowest common
dependency of both sides.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 golangci-lint
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(model-artifacts): materialize longcat-video checkpoints on the controller
longcat-video loads a checkpoint directory: its backend.py takes
request.ModelFile when os.path.isdir(request.ModelFile) and otherwise
falls back to snapshot_download. That places it in the same class as
transformers/vllm/diffusers/sglang, but the allow-list added in #10910
did not enumerate it, so PrimaryArtifactSpec returned no managed
artifact for a bare HuggingFace repo id.
The consequence in distributed mode: nothing was acquired on the
controller, ModelFileName fell through to the raw repo id, and staging
skipped the resulting phantom /models/<owner>/<repo> path. The worker
received a blank ModelFile, fell back to request.Model, and downloaded
~83GB from HuggingFace inside the remote LoadModel deadline - so the
load could only ever fail with DeadlineExceeded while an abandoned
backend process kept downloading.
Note this materializes the full repository. The backend restricts its
own snapshot_download with allow_patterns, and the avatar repo ships
both base_model/ and base_model_int8/ where only one is ever loaded;
inferred specs have no way to carry patterns today. Tracked separately.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): warn when staging skips a non-existent model path
stageModelFiles logs "Staging model files for remote node" up front, then
silently drops any path field that does not exist on the controller. The
skip itself is legitimate and must stay: a backend outside
managedArtifactBackends that takes a bare HuggingFace repo id gets an
optimistically constructed path (ModelFileName falls through to the raw
model reference) that was never materialized, and sources its own weights
on the worker. Erroring would break those configs.
But at debug level the operator is left with a reassuring staging line and
no trace of the skip, so a genuine controller-side acquisition gap is
indistinguishable from a healthy pass-through - it surfaces much later as
a remote LoadModel timeout, on a worker that is quietly downloading tens
of gigabytes. Raise the skip to warn and name the field, path, node and
tracking key. Behavior is unchanged.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(model-artifacts): allow a config to declare companion artifacts
A composed pipeline needs more than one HuggingFace snapshot.
LongCat-Video-Avatar-1.5 loads its own transformer but takes the
tokenizer, text encoder and VAE from the separate LongCat-Video base
repo, so a single-artifact config cannot express it and the backend is
left to fetch the second repo itself at load time.
Widen the artifact model to target: model plus any number of named
target: companion entries. Normalize accepts the new target and
constrains a companion name to [a-z0-9][a-z0-9_-]{0,63} because that
name is the option key the backend later receives; a companion may not
claim primary_file, which only means anything for a load target.
ModelConfig.Validate requires exactly one primary and requires it first,
since Artifacts[0] is what ModelFileName, size estimation and staging all
resolve from.
Both acquisition paths now loop instead of touching index 0 alone:
preloadOne for an already-installed config, bindPrimaryArtifact for a
gallery install. Failure policy differs by provenance. An inferred
primary keeps its warn-and-fall-back, because the legacy download path
still exists for it. Companions are explicit by construction, so they are
all-or-nothing: a config naming one is asserting the backend needs it,
and failing at the acquisition boundary is far more legible than a
missing-weights error surfacing later inside the backend.
The cache key is deliberately unchanged. It hashes source identity only,
never name or target, so every already-installed managed model still hits
its existing snapshot instead of silently re-downloading. Two specs pin
that: one proving a companion and a primary with identical sources agree
on the key, and one pinning the digest of a known primary outright.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(model-artifacts): hand resolved companion snapshots to the backend
A materialized companion is useless until the backend can find it, and
its location is a content-addressed cache key that does not exist until
the artifact resolves. A static gallery override cannot carry that, and
persisting it into the config YAML would rot the moment a re-resolve
produced a new key.
Synthesize it instead at load time: each resolved companion becomes
"<artifact name>:<snapshot path>" in ModelOptions.Options, reusing the
key:value convention backends already parse for options like
attention_backend. The value stays relative to the models directory so a
remote worker can resolve it under its own ModelPath once staging has
rewritten the model root. An option the author set explicitly always
wins, so pinning a companion to a local checkout still beats the managed
snapshot.
longcat-video resolves base_model through ModelPath, the same convention
qwen-tts, voxcpm, outetts and ace-step already use for companion assets.
Its sibling-directory heuristic is deleted: it looked for a LongCat-Video
directory next to the model, which cannot exist under the content
addressed .artifacts/huggingface/<key>/snapshot layout, so it was dead
code the moment the model became managed.
The gallery entry declares both repositories and restricts each with
allow_patterns. The avatar repo ships base_model/ and base_model_int8/
and only ever loads one, so fetching the whole repo would roughly double
the download. The patterns match the entry's own options (use_distill
true, use_int8 default false); enabling use_int8 here also requires
adding base_model_int8/**, which is called out in the entry.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): stage managed artifact trees from the models root
Staging anchored the worker's models directory on the primary snapshot
whenever a model was managed, so a companion snapshot could not reach the
worker at all.
frontendModelsDir was derived by stripping the Model relative path off
the end of ModelFile. For a managed artifact nothing matches: ModelFile
is .artifacts/huggingface/<key>/snapshot while Model stays a bare
HuggingFace repo id, so the strip was a no-op and the "models directory"
came out as the snapshot itself. Two consequences, both silent. Staging
keys lost the .artifacts/huggingface/<key>/snapshot prefix, so two
snapshots of one model were indistinguishable on the worker. And a
companion, which lives in a sibling snapshot directory outside the
primary, fell outside that directory entirely: StagingKeyMapper.Key
collapsed its files to bare basenames and resolveOptionPath could not
resolve the relative option at all, so it was skipped without a word.
Derive the models root from the artifact tree instead when the path runs
through it, and compute the worker's ModelPath from the file's path
relative to that root rather than from the Model field. The legacy layout
is unaffected: where Model really is the relative path, the new
derivation reduces to the old one, which a regression spec pins.
This deliberately changes an invariant that router_dirstage_test.go
pinned: for a managed primary, ModelFile and ModelPath were both the
snapshot directory, and staging keys were relative to it. Now ModelFile
is the snapshot, ModelPath is the models root above it, and keys keep the
full relative path. That spec is updated rather than accommodated, with
the reasoning recorded inline, because the old invariant is exactly what
made a sibling companion unreachable.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backends): list backends runnable on worker nodes in distributed mode
GET /backends/available filtered the gallery against the system state of
the host serving the request. In a distributed deployment that host is the
controller, which typically has no GPU, while the GPUs live on worker
nodes. Any meta backend whose capabilities map lacks a "default" (or "cpu")
key was therefore dropped from the listing entirely — longcat-video,
vllm-omni, ltx-video, parakeet, edgetam and qwentts were invisible in the
UI even though installing them by name on a GPU worker worked fine.
Workers now report their own meta-backend capability at registration and
the controller persists it on the node row. The controller cannot derive
it: OS-dependent capabilities (metal, darwin-x86, nvidia-l4t) and the CUDA
runtime refinements are only observable on the worker. Nodes registered
before this field existed fall back to a coarse capability derived from
their GPU vendor and VRAM.
Backend discovery then evaluates compatibility as the union over healthy
backend nodes, so a backend runnable on any node is offered while one no
node can run stays hidden. Each remote capability is evaluated through a
capability-pinned system state, otherwise a forced capability on the
controller image (LOCALAI_FORCE_META_BACKEND_CAPABILITY or
/run/localai/capability) would silently override every worker's verdict.
With no registered nodes the listing is byte-for-byte what it was, so
single-node deployments are unaffected.
Also fixes the same-root-cause misclassification in /api/operations, which
used the capability-filtered listing to decide whether an operation was a
backend or a model install. A GPU-only backend installing on a worker is
still a backend operation on the controller, so that lookup is now
unfiltered.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backends): union worker capabilities in backend discovery
Implementation for the specs added in the previous commit, plus the two
remaining discovery endpoints.
Capability-filtered backend discovery evaluated compatibility against the
system state of the host serving the request. In a distributed deployment
that host is the controller, which typically has no GPU, while the GPUs
live on worker nodes. Any meta backend whose capabilities map lacks a
"default" (or "cpu") key was dropped entirely — longcat-video, vllm-omni,
ltx-video, parakeet, edgetam and qwentts were invisible in the UI even
though installing them by name on a GPU worker worked fine.
Workers now report their own meta-backend capability at registration and
the controller persists it on the node row. The controller cannot derive
it: OS-dependent capabilities (metal, darwin-x86, nvidia-l4t) and the CUDA
runtime refinements are only observable on the worker. Nodes registered
before this field existed fall back to a coarse capability derived from
their GPU vendor and VRAM.
Discovery then evaluates compatibility as the union over healthy backend
nodes, so a backend runnable on any node is offered while one no node can
run stays hidden. Each remote capability is evaluated through a
capability-pinned system state, otherwise a forced capability on the
controller image (LOCALAI_FORCE_META_BACKEND_CAPABILITY or
/run/localai/capability) would silently override every worker's verdict.
With no registered nodes the listing is byte-for-byte what it was, so
single-node deployments are unaffected.
Four surfaces shared this root cause and are all routed through the same
helper now:
- GET /backends/available
- GET /api/fine-tuning/backends
- GET /api/quantization/backends
- /api/operations backend-vs-model classification, which additionally
had no reason to filter by capability at all: a GPU-only backend
installing on a worker is still a backend operation on the
controller, so that lookup is now unfiltered.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Avoid holding the global loader lock across backend lifecycle waits and propagate forced shutdown through distributed workers. Track parallel requests with in-flight counters and reserve worker ports until process termination.
Add focused race tests and an authoritative FizzBee lifecycle model with a fail-closed conformance target.
Assisted-by: Codex:GPT-5 [FizzBee] [Ginkgo]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
The Vite build emitted path-absolute asset URLs (base: '/'). index.html
entry scripts and the favicon were rewritten to include the reverse-proxy
prefix in serveIndex, but two reference kinds are not in index.html and so
bypassed that rewrite:
- CSS `url()` font references (e.g. Font Awesome .woff2), which the browser
resolves relative to the stylesheet and which `<base href>` never affects
- lazily-imported route chunks, whose preload base came from the absolute
Vite base
Under a subpath mount (X-Forwarded-Prefix: /llm/) both were fetched from the
origin root, 404ing — missing-glyph "tofu" icons and broken lazy-loaded pages.
Switch Vite to a relative base ('./') so every generated URL resolves against
the file that references it: CSS fonts and route chunks now load from
`/llm/assets/...`, and index.html's now-relative entry refs resolve via the
`<base href>` serveIndex already injects on every response. Root deployments
are unaffected. The existing path-absolute rewrite in app.go still covers the
public `/favicon.svg`.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The managed-artifact materializer stages a HuggingFace snapshot into a
directory (.artifacts/huggingface/<key>/snapshot/). That is the right load
target for directory-consuming backends (transformers, vLLM, diffusers, ...),
but PrimaryArtifactSpec inferred a managed artifact from ANY HuggingFace-shaped
model reference regardless of backend. A single-file backend such as llama.cpp
or whisper was therefore handed the snapshot directory instead of the weight
file and failed to load it.
The /import-model importer already guards this with a backend allow-list
(managedArtifactBackends), but the loader-side inference did not. Move the
allow-list into core/config as IsManagedArtifactBackend and apply it in
PrimaryArtifactSpec: only directory-consuming backends may have an artifact
inferred from a bare reference; every other backend stays on the legacy
download-to-file path. An explicit artifacts: block still bypasses the gate,
where single-file snapshot resolution handles the load path.
The importer now shares the same predicate, so both paths agree on which
backends auto-materialize.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
fix(model-artifacts): load single-file HF snapshots from the file, not the dir
The managed Hugging Face artifact materializer (#10825) always pointed
backends at the snapshot *directory*
(.artifacts/huggingface/<key>/snapshot). For a single-file model
reference such as huggingface://nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf,
the GGUF lives *inside* that directory, so llama.cpp was handed a
directory and failed with "gguf_init_from_reader: failed to read magic".
This has kept the tests-aio job red on master since the feature merged
(the embeddings e2e tests could not load text-embedding-ada-002).
Record the single file of a one-file snapshot as Resolved.PrimaryFile and
have ModelFileName() resolve to snapshot/<PrimaryFile> when it is set.
Multi-file snapshots (e.g. transformers repos consumed as a directory)
keep pointing at the snapshot directory. PrimaryFile is derived from the
resolved contents and is deliberately excluded from the artifact cache
key. estimateModelSizeBytes now derives the snapshot directory from the
cache key instead of ModelFileName(), so its manifest lookup is unaffected
by the file-vs-directory resolution.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The artifact progress bridge mapped every PhaseVerifying event to a flat
95%. The materializer emits PhaseVerifying once per file (from each file's
AfterDownload hook) and downloads run sequentially, so the first small file
to finish pinned the bar at 95% - and, because progress is monotonic, it
stayed at 95% for the entire remaining download (e.g. a 70GB checkpoint
reporting 95% at 410MB / 69.7GB).
Track per-file verify proportionally to the running aggregate bytes, the
same way downloading does. CurrentBytes already reflects "completed files +
this file", so the percentage advances honestly. The flat 95%/99% is now
reserved for the genuinely once-per-install Committing/Persisting phases.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The Operate → Traces "API Traces" panel already recorded who made each
request (user_id/user_name) but never showed it, and did not capture the
caller's network identity at all. Operators asked to see the requesting
user (#10886) and the client IP + user agent (#10887) so a trace can be
attributed to who/what issued it.
Backend: add ClientIP and UserAgent to APIExchange and populate them from
echo's c.RealIP() (honours X-Forwarded-For / X-Real-IP behind a trusted
proxy) and the request's User-Agent header. Both are omitempty and the
/api/traces swagger response is map[string]any, so this is additive.
UI: add a sortable "User" column to the API traces table and a metadata
block (User / Client IP / User Agent) at the top of the expanded row
detail. Fields render only when present, so older buffered traces and
unauthenticated/local requests degrade cleanly.
Adds an e2e spec covering the new column value and the expanded metadata.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The Operate -> Traces table rendered the request time with the time of day
only, so entries that span more than one day were ambiguous. Add a
formatDateTime helper (localized date + existing time-with-millis) and use it
for the Traces "Time" column, keeping the cell on a single line. The shared
formatTimestamp used by the log views is unchanged.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
One agent turn runs several internal LLM generations (tool selection,
reasoning, final answer) that all emit stream_event deltas over the same
per-agent SSE channel. The chat page accumulated every 'content' delta
into a single live bubble and ignored the 'done' boundary events, so the
internal generations' text (e.g. the English tool-selection rationale)
merged with — and visually corrupted — the streamed final answer.
Reset the accumulated content/reasoning on 'done': each generation gets
a clean live bubble, and the authoritative full answer still arrives via
the final json_message event as before.
Signed-off-by: Stefan Walcz <stefan.walcz@walcz.de>
StopP2P() read and wrote a.p2pCtx/a.p2pCancel without holding
a.p2pMutex, and StartP2P() reassigned both fields with no lock at
all -- including when RestartP2P() calls it from a background
goroutine after releasing the mutex. Both paths are reachable from
POST /api/settings (empty p2p_token -> StopP2P, non-empty ->
RestartP2P), so concurrent requests race on the same fields.
Take a.p2pMutex in StopP2P and around the field publication in
StartP2P, factor the shared teardown into stopP2PLocked() so
RestartP2P reuses it, and route the goroutine error path through
StopP2P instead of touching a.p2pCancel unlocked.
Signed-off-by: Anai-Guo <antai12232931@anaiguo.com>
Co-authored-by: Anai-Guo <antai12232931@anaiguo.com>
* feat(settings): add declarative runtime-settings field registry
One fieldSpec row per RuntimeSettings field, with a reflection
completeness spec so a field added without a registry row is a red
test instead of a silently-dropped setting (the #10845 bug class).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* refactor(settings): drive ToRuntimeSettings/ApplyRuntimeSettings from the field registry
Behavior-preserving: ~350 hand-written per-field lines become two loops
over runtimeSettingsFields, gated by a To->Apply->To round-trip spec.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* feat(settings): baseline-driven startup merge for persisted runtime settings
ApplyRuntimeSettingsAtStartup compares the live config against
DefaultRuntimeBaseline (option-less-run defaults incl. kong-injected
flag defaults) instead of per-field == 0 guards. Fixes persisted
lru_eviction_max_retries, tracing_max_items, agent_job_retention_days,
memory_reclaimer_threshold, galleries and autoload flags being
silently ignored at boot.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* fix(settings): registry-driven startup merge, applied before consumers
loadRuntimeSettingsFromFile becomes a thin wrapper over
ApplyRuntimeSettingsAtStartup and runs at the top of New(), before
model configs capture app-level defaults. WithThreads stops eagerly
resolving 0 so a persisted thread count survives restart while
LOCALAI_THREADS still wins (#10845); the physical-core fallback moves
after the merge.
Also: run.go now injects the memory-reclaimer threshold unconditionally
so the option-less boot matches DefaultRuntimeBaseline and a UI-saved
threshold survives restart.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* refactor(settings): file watcher delegates to the registry merge; shared API-key merge
Manual edits to runtime_settings.json now behave like a boot-time load
(env still wins) instead of the inverted diverged-from-startup guard
that ignored most manual edits. MergeAPIKeys dedups env keys in one
place for the endpoint and the watcher.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* docs(settings): document unified runtime-settings precedence
Document the single env/CLI > runtime_settings.json > defaults rule,
applied identically at boot, on POST /api/settings, and on manual file
edits, plus the two known limitations (default-valued env vars are
indistinguishable from unset; API-changed fields hot-apply on the next
restart only). Also add a completion debug log when the watcher applies
runtime_settings.json.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
* test(settings): reset the global VRAM cap leaked by the round-trip spec
The round-trip spec applies vram_budget=12GiB, whose post-loop hook
installs a process-global default cap; without a reset every spec
ordered after it runs under that phantom budget. Also drop a stale
enumeration in the ApplyRuntimeSettings doc comment.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-fable-5
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(audio-transform): serialize WebSocket writes to avoid concurrent-write panic
AudioTransformStreamEndpoint writes to the same Gorilla WebSocket connection
from two goroutines: the backend-forwarding goroutine emits binary PCM frames
(and can call sendWSError on a backend recv error), while the read loop calls
sendWSError for malformed mid-stream JSON or a backend send failure. Gorilla
WebSocket permits only one concurrent writer, so these writers race and can
panic with "concurrent write to websocket connection", resetting the client
session; a -race build reports the data race directly.
Wrap the connection in a lockedConn that serializes WriteMessage behind a
mutex, mirroring the existing lockedConn used by the openresponses WebSocket
endpoint. Reads stay on the single read loop, so only writes need the lock.
Fixes#10844
Signed-off-by: Tai An <antai12232931@outlook.com>
* chore: empty commit to re-trigger checks
Signed-off-by: Anai-Guo <antai12232931@anaiguo.com>
---------
Signed-off-by: Tai An <antai12232931@outlook.com>
Signed-off-by: Anai-Guo <antai12232931@anaiguo.com>
Co-authored-by: Anai-Guo <antai12232931@anaiguo.com>
feat(bonsai): add PrismML llama.cpp fork backend + Bonsai gallery models
Adds a new `bonsai` backend that runs the PrismML fork of llama.cpp
(github.com/PrismML-Eng/llama.cpp, `prism` branch), which ships the Q1_0
(1-bit) and Q2_0 (ternary / 1.58-bit) weight-quantization kernels used by the
Bonsai and Ternary-Bonsai models. Stock llama.cpp cannot decode these quants.
Modeled on the turboquant backend: reuses backend/cpp/llama-cpp/grpc-server.cpp
against the fork's libllama via a thin wrapper Makefile, so the sub-2-bit models
are served with the same OpenAI-compatible API. No grpc-server allow-list patch
is needed (bonsai adds weight quants, transparent to the server, not KV-cache
types), and the reused server compiles cleanly against the fork with no skew
patches (validated locally via a CPU docker build; patches/ is present but empty
for any future re-pin skew).
Backend wiring: backend/cpp/bonsai/, .docker/bonsai-compile.sh,
backend/Dockerfile.bonsai, top-level Makefile targets, backend-matrix.yml build
rows (CPU, CUDA 12/13, L4T, SYCL f32/f16, Vulkan, ROCm/hipblas), backend/index.yaml
meta-backend + per-platform images, and a nightly bump_deps entry tracking the
`prism` branch.
Gallery: 8 entries across 4 families - bonsai-8b-1bit, ternary-bonsai-8b (+g64,
+pq2), bonsai-27b-1bit (vision), ternary-bonsai-27b (+pq2, +g64, vision). The 27B
models wire the mmproj vision tower; the DSpark speculative drafter GGUFs are not
wired (custom semi-autoregressive drafter, not a standard llama.cpp draft model).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
fix(watchdog): guard StopWatchdog with watchdogMutex to prevent double close
StopWatchdog checked, closed and cleared a.watchdogStop without holding
a.watchdogMutex, while startWatchdog and RestartWatchdog reassign and close the
same channel under that lock.
POST /api/settings dispatches to StopWatchdog or RestartWatchdog depending on
ApplicationConfig.WatchdogShouldRun(), so both are reachable concurrently. Two
callers can observe a non-nil watchdogStop and both close it, which panics with
'close of closed channel' and takes the server down.
Take the mutex, matching the other two writers. StopWatchdog is only called from
the settings handler, which holds no lock, so this cannot deadlock.
Fixes#10841
Co-authored-by: Anai Guo <antai12232931@anaiguo.com>
Finetune() compiled every model cutstrings/extract_regex entry via regexp.Compile
and called xlog.Fatal on failure, which terminates the entire local-ai process.
A single model config with an invalid regex (e.g. cutstrings: ["("]) turns one
/v1/chat/completions request into a process-level denial of service.
Log the compile error and skip the offending pattern instead. The mutex is
released before continuing, and skipping avoids dereferencing the nil regexp
that removing the fatal would otherwise leave behind.
Fixes#10843
Signed-off-by: Tai An <antai12232931@outlook.com>
ApplyRuntimeSettings persists the performance settings (threads,
context_size, f16) on the live /api/settings path, but the startup
loader loadRuntimeSettingsFromFile never read them back, so a value
saved via the Middleware UI was silently ignored on the next restart:
the model booted with the CLI/physical-core default and GET /api/settings
echoed that default instead of the saved value (#10845).
Threads needs special handling: unlike context_size/f16, WithThreads
eagerly resolves an unset (0) value to xsysinfo.CPUPhysicalCores() at
option-apply time, so options.Threads is never 0 in the loader and the
usual "== default" heuristic cannot tell an env/CLI value from the
physical-core fallback. Detect LOCALAI_THREADS/THREADS explicitly so the
env still wins over the persisted file value.
Signed-off-by: Anai-Guo <Anai-Guo@users.noreply.github.com>
Co-authored-by: Anai-Guo <Anai-Guo@users.noreply.github.com>
* feat(vram): add vrambudget primitive for per-node VRAM caps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): apply default VRAM budget in xsysinfo aggregate getters
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): wire LOCALAI_VRAM_BUDGET flag to xsysinfo default budget
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): persist VRAM budget via runtime settings with live apply
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(vram): reset process-global VRAM budget after runtime-settings spec
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): add VRAM budget field to Settings page
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): store and enforce per-node VRAM budget in the node registry
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): apply per-node VRAM budget in router hardware defaults
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): report worker VRAM budget in node registration
The distributed worker now reports its operator-set VRAM budget string
(LOCALAI_VRAM_BUDGET) to the server on registration. The worker keeps
reporting RAW total/available VRAM and never sets the xsysinfo
process-global budget (that stays standalone-only); the server resolves
and enforces the budget uniformly (Task 6).
Also closes a Task 6 gap: on re-registration, a struct Updates zero-skips
an empty budget, so a worker that dropped LOCALAI_VRAM_BUDGET left the
stale cap in place. For non-admin-override nodes the budget columns are
now force-written (map Updates) even when empty, so removing the env var
clears the cap; admin overrides are preserved unchanged.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* style(vram): drop em dash from worker-clear comment
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): add node VRAM budget admin endpoints
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): add node VRAM budget control to the node UI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vram): expose set_node_vram_budget MCP admin tool
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(vram): document LOCALAI_VRAM_BUDGET and node VRAM budget UI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vram): avoid double-applying VRAM budget in GetResourceAggregateInfo
The GPU-branch aggregate returned by GetResourceInfo is sourced from
GetGPUAggregateInfo, which already caps total/free/used against the
process-wide VRAM budget. GetResourceAggregateInfo then applied the
budget a second time. For an absolute budget this is idempotent, but for
a percentage budget b.Apply resolves the ceiling as a fraction of its
input total, so a second pass yields P*(P*T) instead of P*T and distorts
UsagePercent (read by the memory reclaimer in pkg/model/watchdog.go).
Remove the redundant second application so the budget is applied exactly
once, against the raw physical totals, upstream in GetGPUAggregateInfo.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vram): implement SetNodeVRAMBudget on mcp assistant test stub
The LocalAIClient interface gained SetNodeVRAMBudget; the stubClient in
core/http/endpoints/mcp used by the assistant tests is a separate
implementer and needs the method too (broke golangci-lint typecheck and
both test jobs).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* test(core/http): make the suite's HTTP port overridable
app_test.go and openresponses_test.go hardcoded 127.0.0.1:9090. When
another service already listens on 9090 the suite does not fail fast:
the server goroutine logs the bind error and the specs then poll
whatever is squatting the port until Eventually times out. On machines
where 9090 is permanently taken this makes the pre-commit coverage gate
impossible to pass.
Introduce testHTTPAddr, defaulting to 127.0.0.1:9090 (what CI has
always used) and overridable via LOCALAI_TEST_HTTP_PORT for local runs.
Assisted-by: Claude:claude-fable-5 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): make per-node backend upgrade actually upgrade
The node detail page's Upgrade button reused the node-scoped install
path (POST /api/nodes/:id/backends/install). That fires NATS
backend.install with force=false, and the worker's install handler is
deliberately "ensure installed": when the backend binary already exists
on disk it short-circuits without touching the gallery. Since only an
installed backend can be upgraded, the whole chain was a guaranteed
successful no-op - the UI then toasted "backend upgraded" without even
waiting for the async job.
Route upgrades through the real force-reinstall path instead:
- BackendManager.UpgradeBackend now receives the ManagementOp (like
InstallBackend already did) so implementations can honor
op.TargetNodeID.
- DistributedBackendManager.UpgradeBackend scopes the backend.upgrade
fan-out to op.TargetNodeID when set, and errors when the target node
does not report the backend as installed.
- New POST /api/nodes/:id/backends/upgrade endpoint enqueues an
Upgrade=true node-scoped op (async 202 + jobID, mirroring install).
- NodeDetail UI calls the new endpoint and reports the dispatch
("Upgrading ... on this node...") instead of claiming success; the
Operations panel tracks the actual job.
Verified against a live local cluster (NATS + Postgres + two workers):
the target worker stops the running process, force-reinstalls from the
gallery and re-downloads the OCI image; the second worker receives no
backend.upgrade event; upgrading a backend missing from the target node
fails the job with a clear error.
Assisted-by: Claude:claude-fable-5 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
SetDefaults injected the llama.cpp server options cache_reuse
(ApplyServingDefaults) and parallel (ApplyHardwareDefaults, re-applied
per selected node by the distributed router) onto every model config
regardless of backend. Every other backend ignores options it does not
understand, so this was harmless until longcat-video, which strictly
validates its options and fails LoadModel with
"unknown model option(s): cache_reuse, parallel".
Gate both injections behind a new UsesLlamaCppServingOptions allow-list
(llama-cpp plus the empty/auto-detect case that resolves to llama.cpp
from a GGUF file, mirroring how llamaCppDefaults is registered). This
follows the existing UsesLlamaSamplerDefaults precedent for llama-only
defaults. The typed NBatch field is deliberately left alone: it is a
proto field every backend simply ignores, which is why batch never
triggered the error.
Also harden the longcat-video backend to warn-and-ignore unknown model
options and request params through a testable select_known_options
helper, matching the other LocalAI Python backends, so a future
server-injected option cannot break loading again.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
handleRegenerate rebuilt the outbound message from the display-only
message.files metadata ({name, type: 'file'|'image'|..., content}),
which doesn't carry the base64/textContent payload sendMessage's
file-building loop expects. As a result, regenerating any answer whose
own question had an attachment silently dropped that attachment from
the resent message. This wasn't fork-specific, but forking a chat and
then regenerating an earlier (now non-last) answer is the natural way
to hit it.
Fix by reusing the original message's already-assembled `content`
verbatim (it already has the file text / image_url / audio_url /
video_url parts embedded from the first send) instead of trying to
reconstruct it from lossy display metadata.
Fixes#10806
Assisted-by: Claude:claude-sonnet-5
Signed-off-by: ajuijas <189517297+ajuijas@users.noreply.github.com>
Co-authored-by: ajuijas <189517297+ajuijas@users.noreply.github.com>
- Replace hardcoded text with useTranslation hook in UI components
- Add localization support for both English (en) and Indonesian (id) locales
Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com>
* feat(ui): add voice library workflow
Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech.
Assisted-by: Codex:gpt-5
* feat(voice): add managed voice cloning profiles
Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends.
Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation.
Assisted-by: Codex:gpt-5
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backends): add LongCat video and avatar generation
Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web]
* refactor(config): declare model I/O modalities
Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook.
Assisted-by: Codex:GPT-5 [web]
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
When a model config has no explicit backend, the model loader greedily
probes every installed backend and binds to the first Load that
succeeds. opus and local-store were the only in-tree backends with no
model artefact to validate, so they accepted anything — an LLM
installed after them could silently bind to the audio codec or the
vector store and then fail at inference with "unimplemented"
(see #9287).
opus now accepts only its own name (what the realtime WebRTC path
sends) or none. local-store namespaces are arbitrary (router caches,
biometrics, user-named stores), so core's StoreBackend now marks
genuine store loads with a store:// prefix on the gRPC model name and
the backend refuses names without it; core and backend ship from the
same release, so the convention upgrades in lockstep.
Also repair the bit-rotted 'make test-stores' bootstrap (the suite
never registered external backends, so BACKENDS_PATH was dead weight)
and add the Load-validation rule to the adding-backends checklist.
Related: #9287
Assisted-by: Claude:claude-fable-5 golangci-lint
Signed-off-by: Richard Palethorpe <io@richiejp.com>
C++/ggml transcription + speaker diarization + timestamps backend. Purego
dlopens libmoss-transcribe.so (ggml statically linked) from moss-transcribe.cpp
and serves offline AudioTranscription, parsing the [start][Sxx]text[end] output
into segments with nanosecond timestamps. Adds the importer (surfaces in
GET /backends/known), backend-matrix (Linux + Darwin/metal), backend/index.yaml,
and a gallery entry (default q5_k GGUF from mudler/moss-transcribe.cpp-gguf).
Local L0 smoke (go build + go test ./... = 16 pass, golangci-lint 0 issues)
passed against the real libmoss-transcribe.so. The pre-commit coverage gate
(full pkg/core + tests/e2e) could not run in the authoring sandbox (no live
models, port 9090 held); CI must enforce it before merge.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(schema): accept reasoning_content as inbound alias for reasoning
Interleaved-thinking clients (cogito, vLLM/DeepSeek-style) emit reasoning_content
on assistant turns. Accept it as an inbound alias so reasoning survives the
tool-result loop; canonical reasoning wins when both are present. Emission is
unchanged (still reasoning).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(schema): pin interleaved reasoning+tool_calls round-trip
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(openai): pin reachedTokenBudget truncation detection
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(anthropic): add thinking and signature fields to content blocks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(anthropic): parse inbound thinking blocks into reasoning
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(anthropic): emit thinking blocks with synthetic signature on tool turns
Extract buildAnthropicContentBlocks so non-streaming content assembly is
unit-testable, and prepend a thinking block (with an opaque synthetic
signature) before text/tool_use blocks when the request opts into thinking.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(anthropic): stream thinking_delta and signature_delta before tool_use
Extract anthropicStreamSequence so the streaming block order is unit-testable,
and emit content_block_start(thinking) -> thinking_delta -> signature_delta ->
content_block_stop before the tool_use block sequence when thinking is enabled.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add interleaved thinking with tool calls guide
Add a features guide describing interleaved thinking: an assistant turn
carrying reasoning and tool_calls together, the reasoning-round-trip
contract (including the reasoning_content inbound alias and Anthropic
thinking blocks with a synthetic signature), per-backend enablement
(reasoning_format for llama.cpp, reasoning_parser/tool_call_parser for
vLLM/SGLang plus the vLLM auto-config hook), a worked request/response
example, and known limitations. Cross-link from model-configuration,
text-generation, and openai-functions.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The OIDC verifier was built with a bare oidc.Config{ClientID: ...}, so
go-oidc applied its default of accepting RS256-signed ID tokens only. An
identity provider configured with an EC signing key (e.g. Authentik) issues
ES256-signed tokens, and the callback failed verification with:
failed to verify ID token: oidc: malformed jwt: unexpected signature
algorithm "HS256"; expected ["RS256"]
surfacing to the user as HTTP 500 "failed to fetch user info" (#10677; the
underlying cause became visible after the logging fix in #10679).
Set SupportedSigningAlgs to the standard asymmetric algorithms
(RS256/384/512, ES256/384/512, PS256/384/512, EdDSA). All are verified
against the provider's published JWKS. HS256 is intentionally excluded: it
is symmetric and would validate against the client secret, a different and
security-sensitive trust model.
Tested with a functional spec that signs an ES256 ID token and confirms it
verifies with the configured algorithms and is rejected under go-oidc's
RS256-only default (using oidc.StaticKeySet, no network).
Closes#10677
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Backend log capture into the per-model BackendLogStore (which feeds the
UI "Backend Logs" page and /api/backend-logs) was opt-in and off by
default in single mode, while worker/distributed mode force-enables it
via SetBackendLoggingEnabled(true). There was no CLI flag either, so the
only way to populate the store was the Settings UI toggle - and the page
was silently empty out of the box. Distributed "just worked"; single
mode looked broken.
Default EnableBackendLogging to true in NewApplicationConfig so single
mode matches worker mode. The store is a small in-memory ring buffer, so
the cost is negligible.
Now that the default is on, loadRuntimeSettingsFromFile's usual
"only flip false->true" merge would ignore a persisted false and revert
the UI toggle-off on every restart. There is no env var/CLI flag for
this setting, so an explicit persisted value is now authoritative in
both directions, letting the toggle-off survive a restart.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
fix(transcription): honor model-config language/translate, add language form field
The /v1/audio/transcriptions endpoint read only input.Language /
input.Translate from the parsed request, and the request middleware never
populates those from a multipart upload -- nor did it read a `language`
form field. As a result the model config's parameters.language /
parameters.translate (a valid PredictionOptions field under `parameters:`)
were silently ignored, and multilingual models like canary defaulted to
translating into English even when the YAML set language: ru,
translate: false (#10655).
Resolve both with clear precedence: the request form field wins, then any
language on the parsed request, then the model config default. This also
makes the endpoint honor OpenAI's `language` form parameter, which was
not read before.
Applies to both the streaming and non-streaming paths (the resolved
values are built into the shared TranscriptionRequest). Note this ensures
the language/translate flags reach the backend; whether a given engine
acts on them is up to the backend.
Closes#10655
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
A client that keeps polling a model whose load fails (e.g. a backend that
crashes deterministically on init) triggered a fresh backend start on
every request: request -> load -> crash in ~10s -> 500, repeat on the
next poll. Each attempt could leak GPU/CUDA state, and under
LOCALAI_SINGLE_ACTIVE_BACKEND it kept stealing the active slot from
healthy models. The existing loading-coalesce map only dedups
*concurrent* loads, so sequential polls were never covered.
Track load failures per modelID in ModelLoader. After a load fails,
refuse fresh load triggers for that model until a cooldown elapses,
returning a typed ModelLoadCooldownError that the HTTP layer maps to 503
with a Retry-After header. The cooldown grows exponentially per
consecutive failure (base, doubling, capped at 5m) and resets on a
successful load. The coalesced follower-retry of an in-flight burst
bypasses the gate, so a genuinely concurrent burst still gets its one
retry -- only new, independent triggers are refused, matching the
report's "refuse new load-triggers" wording.
Configurable via --model-load-failure-cooldown /
LOCALAI_MODEL_LOAD_FAILURE_COOLDOWN (default 10s, 0 disables), plumbed
through ApplicationConfig and applied unconditionally at startup.
Closes#10719
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
InstallBackend extracted the artifact directly into the target directory
with no pre-clean, so a reinstall overlaid the new files onto the old
ones. Files present in a previous version but absent in the new artifact
(a stale .so, an orphaned package dir) survived and could shadow the new
build at import time -- e.g. an old vllm shared object lingering next to
a freshly pulled one. Only a failed download cleaned the directory.
Stage the download/extraction into a `<name>.install-tmp` dir, validate
run.sh is present, write metadata, then atomically swap it into place
(rename current -> .install-backup, staging -> current, drop backup),
rolling back on failure. This mirrors the atomic swap UpgradeBackend
already performs, so install and upgrade now leave identical on-disk
state with no orphaned files.
Reported as part of #10720.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama-cpp): cap single-pass embedding batch to fit VRAM
Embedding/score/rerank all decode or pool the whole input in one physical
batch, so EffectiveBatchSize sized the batch to the full context window. For
a large context that makes n_ubatch huge, and the per-device CUDA compute
buffer (forward-graph scratch, ~n_ubatch * n_ctx, NOT split across GPUs)
balloons into multi-GiB: a large-context embedding model then aborts on load
(exitCode=-1) even with plenty of free VRAM. Reproduced with qwen3-embedding-4b
(context 40960 -> n_batch 40960 -> abort) and qwen3-embedding-0.6b
(n_batch 8192); pinning batch:512 avoided it.
This is the same root cause as issue #10485 (a large context turns the batch
into multi-GiB of scratch that must fit on a SINGLE card), but the single-pass
path bypassed the VRAM headroom guard the config layer already had — it
returned the unbounded context as the batch with no GPU awareness.
Make the single-pass batch VRAM-aware: cap it to the largest batch whose
compute buffer fits the per-device VRAM headroom, clamped to
[DefaultPhysicalBatch, ctx], reusing the existing computeBufferBytesPerCell and
headroom-divisor math (no duplication). Unknown per-device VRAM (0) stays
conservative (DefaultPhysicalBatch, not the context) so a detection gap can't
OOM. The GPU is resolved through an injectable package var (config.LocalGPU,
backed by sync.Once-cached xsysinfo detection) so the per-request router call
stays cheap and tests inject a deterministic device. Explicit batch: still
wins. An input longer than the cap can no longer be pooled in one pass — the
accepted tradeoff, since a batch that OOMs the device processes nothing.
Assisted-by: Claude:claude-opus-4-8 golangci-lint go-test
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(config): single-pass batch follows context on unknown VRAM
The single-pass (embedding/score/rerank) batch cap must only shrink the batch
when the per-device VRAM ceiling is KNOWN. On unknown VRAM (CPU-only or a GPU
detection gap) SinglePassBatchForContext returned DefaultPhysicalBatch, which
under-sized the batch below the context — over-trimming score/embed/rerank
inputs (the modelTokenTrim middleware regression) with no OOM benefit on CPU
where the compute buffer lives in system RAM. Return the full context instead,
preserving the original single-pass behavior; the VRAM cap stays a downward
safety that only engages when VRAM is known.
Assisted-by: Claude:claude-opus-4-8 [go-test go-vet]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
When a model is imported without an explicit context_size, the GGUF
importer defaulted the model's context to its full trained window
(n_ctx_train). For long-context models (128k / 256k / 1M) that KV cache
cannot fit a consumer GPU, so the backend aborts on load (exitCode=-1)
even though the model file is perfectly fine. Reproduced live:
gemma-4-26b-a4b-it-qat-q4_0 defaulted to context=262144 and
qwythos-9b-claude-mythos-5-1m to 1048576, both aborting on a 20 GB card.
Instead of chasing the trained max, auto-derive a conservative default:
min(trainedMax, DefaultAutoContextSize=8192). A small model keeps its
trained window; a long-context model caps at 8k and users opt into more
via context_size. This cap applies always, including CPU / unknown-VRAM
hosts, so it never regresses those paths.
Per-device VRAM is used only as a DOWNWARD safety: when a per-device
ceiling is detected (xsysinfo.MinPerGPUVRAM) and even the 8k cap would
not fit it with headroom, step down through candidate contexts to the
largest that fits, floored at DefaultContextSize. When VRAM is unknown
(0) or no GPU is detected we do NOT clamp — the bug is GPU OOM and the
8k cap is already safe, so detection gaps must not shrink the window.
The footprint estimate reuses gpustack/gguf-parser-go's
EstimateLLaMACppRun at a given context with all layers offloaded, taking
the per-device NonUMA VRAM figure. The estimate and VRAM detection are
package vars so tests inject deterministic values. Explicit context_size
always wins (guessGGUFFromFile only acts when it is nil).
Assisted-by: Claude:claude-opus-4-8 [golangci-lint go-test]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The `--generated-content-path` and `--upload-path` defaults were the fixed
shared locations `/tmp/generated/content` and `/tmp/localai/upload`. On any
multi-user host these collide across accounts: macOS routes `/tmp` to the
shared `/private/tmp` for every user, so whichever account starts LocalAI
first creates the parent with 0750 perms and every other account then fails
startup with:
unable to create ImageDir: "mkdir /tmp/generated/content: permission denied"
unable to create UploadDir: "mkdir /tmp/localai/upload: permission denied"
The same happens on Linux once a stale root-owned `/tmp/generated` (e.g. from
a prior `sudo` run) is left behind. This bites the desktop launcher and any
app embedding the raw binary (Wingman, nib-desktop), which start `local-ai
run` with no path flags.
Default both paths under the OS temp dir (`os.TempDir()`, honoring `$TMPDIR`;
already per-user on macOS) namespaced by the current UID
(`TMPDIR/localai-<uid>/...`), so accounts never collide while the paths stay
ephemeral. Wired via new kong vars in main.go so every consumer of the raw
binary inherits the fix. All content subdirs (audio, images) derive from
`GeneratedContentDir`, so they are fixed transitively.
As defense in depth, the launcher also anchors these two paths under its own
per-user data directory (mirroring the #10610 fix for data/config), extracted
into a testable `BuildRunArgs`.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(reasoning): don't persist request-scoped reasoning_effort into model config
When a model sets `reasoning_effort: none` (or any default) in its YAML
without an explicit `reasoning.disable`, ApplyReasoningEffort resolves that
default at request time and sets ReasoningConfig.DisableReasoning on the
request-scoped config copy. The post-load thinking/marker probe then wrote
that request-scoped value back into the loader's persistent config via
UpdateModelConfig, making it look as though the operator had explicitly set
reasoning.disable=true. From then on, per-request `reasoning_effort` overrides
were silently ignored (an explicit operator disable wins over a request
asking to think).
DetectThinkingSupportFromBackend only fills reasoning slots that are still
nil, so a slot already set here came from ApplyReasoningEffort, not the probe.
Snapshot which slots were nil before the probe and only persist those, so the
probe's genuine backend detection is still saved while request-time reasoning
effort never leaks into the persistent config.
Fixes#10622
Signed-off-by: Tai An <antai12232931@outlook.com>
* test(reasoning): cover persist-guard added in this PR, extract for testability
ModelInference's post-probe persistence of ReasoningConfig.DisableReasoning /
DisableReasoningTagPrefill had no test: the guard logic lived inline in a
closure only reachable through a live gRPC backend. Extract it into
persistProbedReasoning (pure refactor, no behavior change) so it can be
exercised directly against a ModelConfigLoader, then add specs covering:
- a probe-filled slot (nil beforehand) gets persisted
- a slot that already carried a request-scoped value (e.g. from
reasoning_effort: none) is left alone, i.e. the #10622 regression stays
fixed
- an operator's explicit persisted disable is preserved when the guard is
false
- the media marker still persists unconditionally
Verified red/green: reverting persistProbedReasoning to the old unconditional
copy fails exactly the two guard specs.
Assisted-by: Claude:claude-sonnet-5 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(reasoning): ignore os.Remove error in temp file cleanup (errcheck)
Signed-off-by: Tai An <antai12232931@outlook.com>
* chore: empty commit to re-trigger flaky Agent Jobs CI test
Signed-off-by: Tai An <antai12232931@outlook.com>
---------
Signed-off-by: Tai An <antai12232931@outlook.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
Operators need a scrape-friendly signal for agent-turn health (completing,
erroring, cancelled, duration) — log-derived counters proved brittle (ANSI/
timezone parsing, restart gaps). Adds localai_agent_runs_total{agent,outcome}
and localai_agent_run_seconds histogram, recorded at the Chat() response
handoff (single choke point of the local execution path). Lazy meter init,
same pattern as the PII events counter (#10641).
Signed-off-by: Stefan Walcz <stefan.walcz@walcz.de>
The no-models getting-started wizard (`.home-wizard`) rendered
left-aligned instead of centered. `.home-page` is a column flexbox with
the default `align-items: stretch`; a child with `max-width: 48rem`
cannot be stretched past its max-width, so it falls back to the
cross-start (left) edge. The populated home branch never exposed this
because its children are full-width.
Add `margin: 0 auto` to `.home-wizard` so the max-width block centers
horizontally, for both the admin getting-started wizard and the
non-admin no-models hero.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Additive superset of /v1/models that enriches each model entry with the
capabilities it supports plus its input/output modalities
(text / image / audio / video). Clients that only understand /v1/models
are unaffected -- they simply never call the new route.
Audio and video *input* are derived from the model's multimodal limits
(vLLM limit_mm_per_prompt), which no single usecase FLAG expresses. That
gap is exactly why a plain capability list is insufficient and this
enriched endpoint exists: an attachment router can now decide whether an
image/audio/video file can go to the active model directly, or must be
converted/transcribed first.
Capability derivation lives in core/config as the single source of truth
(ModelConfig.Capabilities / InputModalities / OutputModalities /
VisionSupported / ...); the Ollama capability surface now delegates to
it instead of keeping a parallel copy. Vision is gated on
chat/completion capability so a MediaMarker hydrated onto a non-chat
model (e.g. a pure ASR/TTS backend) no longer reports a false vision
capability.
Read-only listing: no new FLAG_* flag, reuses the existing `models`
swagger tag, and intentionally exposes no MCP admin tool (there is
nothing to manage conversationally).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The OAuth callback discarded the error returned by user-info resolution
before sending the generic 500, so real failures were completely opaque
in the logs: ID-token verification errors (e.g. issuer/audience mismatch
behind a reverse proxy), a missing id_token, claim-parse errors, or a
rejecting GitHub userinfo endpoint all collapsed into
"failed to fetch user info" with nothing logged.
Log the wrapped cause with xlog.Error (provider + error), matching the
code-exchange step just above it. The client-facing message is unchanged,
so no internal detail leaks to the browser.
Refs #10677
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
POST /models/apply with an empty "id" fetches the attacker-supplied
"url" gallery config directly via http.Client, with no check that the
URL resolves to a public IP. In the default Docker deployment no API key
is configured, so any network-reachable client can coerce LocalAI into
issuing requests to internal services or cloud-metadata endpoints (and
exfiltrate a small slice of the response through the job error message).
Guard the config fetch chokepoints (GetGalleryConfigFromURL and
GetGalleryConfigFromURLWithContext, which back both the /models/apply
worker and gallery installs) with utils.ValidateExternalURL, matching
the protection already applied to the CORS proxy and image/video/audio
download paths. Only plain http(s) URLs are validated; non-network
schemes (huggingface://, github:, oci://, ollama://, file://) resolve to
fixed public services or local files and are left untouched.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The PII EventStore ring buffer is capacity-bound and meant for
recent-audit browsing via /api/pii/events; operators also want a
monotonic, scrape-friendly signal on /metrics — how many
detections/masks/blocks per hour, per origin, and whether the filter
stopped firing after a deploy (silent-failure class).
EventStore.Record is the single choke point every producer already goes
through (request middleware, response scrubbing, MITM proxy
connects/intercepts), so one lazily-initialised counter there covers all
paths without touching any producer:
localai_pii_events_total{kind, origin, action, direction}
Same lazy otel.Meter pattern as core/services/routing/billing, so the
counter lands on the Prometheus-backed global MeterProvider installed by
the monitoring service. No behaviour change; label cardinality is
bounded (enum-like fields only, no pattern IDs or user IDs).
Assisted-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: stefanwalcz <stefan.walcz@walcz.de>
