Only load files whose real extension is .yaml or .yml so backup files like model.yaml.bak do not override active configs. Add a regression test covering plain and timestamped backup files.
Assisted-by: Codex:gpt-5.4 docker
Signed-off-by: leinasi2014 <leinasi2014@gmail.com>
Upstream llama.cpp (PR #21962) switched the server-side mtmd media
marker to a random per-server string and removed the legacy
"<__media__>" backward-compat replacement in mtmd_tokenizer. The
Go layer still emitted the hardcoded "<__media__>", so on the
non-tokenizer-template path the prompt arrived with a marker mtmd
did not recognize and tokenization failed with "number of bitmaps
(1) does not match number of markers (0)".
Report the active media marker via ModelMetadataResponse.media_marker
and substitute the sentinel "<__media__>" with it right before the
gRPC call, after the backend has been loaded and probed. Also skip
the Go-side multimodal templating entirely when UseTokenizerTemplate
is true — llama.cpp's oaicompat_chat_params_parse already injects its
own marker and StringContent is unused in that path. Backends that do
not expose the field keep the legacy "<__media__>" behavior.
* fix(schema): serialize ToolCallID and Reasoning in Messages.ToProto
The ToProto conversion was dropping tool_call_id and reasoning_content
even though both proto and Go fields existed, breaking multi-turn tool
calling and reasoning passthrough to backends.
* refactor(config): introduce backend hook system and migrate llama-cpp defaults
Adds RegisterBackendHook/runBackendHooks so each backend can register
default-filling functions that run during ModelConfig.SetDefaults().
Migrates the existing GGUF guessing logic into hooks_llamacpp.go,
registered for both 'llama-cpp' and the empty backend (auto-detect).
Removes the old guesser.go shim.
* feat(config): add vLLM parser defaults hook and importer auto-detection
Introduces parser_defaults.json mapping model families to vLLM
tool_parser/reasoning_parser names, with longest-pattern-first matching.
The vllmDefaults hook auto-fills tool_parser and reasoning_parser
options at load time for known families, while the VLLMImporter writes
the same values into generated YAML so users can review and edit them.
Adds tests covering MatchParserDefaults, hook registration via
SetDefaults, and the user-override behavior.
* feat(vllm): wire native tool/reasoning parsers + chat deltas + logprobs
- Use vLLM's ToolParserManager/ReasoningParserManager to extract structured
output (tool calls, reasoning content) instead of reimplementing parsing
- Convert proto Messages to dicts and pass tools to apply_chat_template
- Emit ChatDelta with content/reasoning_content/tool_calls in Reply
- Extract prompt_tokens, completion_tokens, and logprobs from output
- Replace boolean GuidedDecoding with proper GuidedDecodingParams from Grammar
- Add TokenizeString and Free RPC methods
- Fix missing `time` import used by load_video()
* feat(vllm): CPU support + shared utils + vllm-omni feature parity
- Split vllm install per acceleration: move generic `vllm` out of
requirements-after.txt into per-profile after files (cublas12, hipblas,
intel) and add CPU wheel URL for cpu-after.txt
- requirements-cpu.txt now pulls torch==2.7.0+cpu from PyTorch CPU index
- backend/index.yaml: register cpu-vllm / cpu-vllm-development variants
- New backend/python/common/vllm_utils.py: shared parse_options,
messages_to_dicts, setup_parsers helpers (used by both vllm backends)
- vllm-omni: replace hardcoded chat template with tokenizer.apply_chat_template,
wire native parsers via shared utils, emit ChatDelta with token counts,
add TokenizeString and Free RPCs, detect CPU and set VLLM_TARGET_DEVICE
- Add test_cpu_inference.py: standalone script to validate CPU build with
a small model (Qwen2.5-0.5B-Instruct)
* fix(vllm): CPU build compatibility with vllm 0.14.1
Validated end-to-end on CPU with Qwen2.5-0.5B-Instruct (LoadModel, Predict,
TokenizeString, Free all working).
- requirements-cpu-after.txt: pin vllm to 0.14.1+cpu (pre-built wheel from
GitHub releases) for x86_64 and aarch64. vllm 0.14.1 is the newest CPU
wheel whose torch dependency resolves against published PyTorch builds
(torch==2.9.1+cpu). Later vllm CPU wheels currently require
torch==2.10.0+cpu which is only available on the PyTorch test channel
with incompatible torchvision.
- requirements-cpu.txt: bump torch to 2.9.1+cpu, add torchvision/torchaudio
so uv resolves them consistently from the PyTorch CPU index.
- install.sh: add --index-strategy=unsafe-best-match for CPU builds so uv
can mix the PyTorch index and PyPI for transitive deps (matches the
existing intel profile behaviour).
- backend.py LoadModel: vllm >= 0.14 removed AsyncLLMEngine.get_model_config
so the old code path errored out with AttributeError on model load.
Switch to the new get_tokenizer()/tokenizer accessor with a fallback
to building the tokenizer directly from request.Model.
* fix(vllm): tool parser constructor compat + e2e tool calling test
Concrete vLLM tool parsers override the abstract base's __init__ and
drop the tools kwarg (e.g. Hermes2ProToolParser only takes tokenizer).
Instantiating with tools= raised TypeError which was silently caught,
leaving chat_deltas.tool_calls empty.
Retry the constructor without the tools kwarg on TypeError — tools
aren't required by these parsers since extract_tool_calls finds tool
syntax in the raw model output directly.
Validated with Qwen/Qwen2.5-0.5B-Instruct + hermes parser on CPU:
the backend correctly returns ToolCallDelta{name='get_weather',
arguments='{"location": "Paris, France"}'} in ChatDelta.
test_tool_calls.py is a standalone smoke test that spawns the gRPC
backend, sends a chat completion with tools, and asserts the response
contains a structured tool call.
* ci(backend): build cpu-vllm container image
Add the cpu-vllm variant to the backend container build matrix so the
image registered in backend/index.yaml (cpu-vllm / cpu-vllm-development)
is actually produced by CI.
Follows the same pattern as the other CPU python backends
(cpu-diffusers, cpu-chatterbox, etc.) with build-type='' and no CUDA.
backend_pr.yml auto-picks this up via its matrix filter from backend.yml.
* test(e2e-backends): add tools capability + HF model name support
Extends tests/e2e-backends to cover backends that:
- Resolve HuggingFace model ids natively (vllm, vllm-omni) instead of
loading a local file: BACKEND_TEST_MODEL_NAME is passed verbatim as
ModelOptions.Model with no download/ModelFile.
- Parse tool calls into ChatDelta.tool_calls: new "tools" capability
sends a Predict with a get_weather function definition and asserts
the Reply contains a matching ToolCallDelta. Uses UseTokenizerTemplate
with OpenAI-style Messages so the backend can wire tools into the
model's chat template.
- Need backend-specific Options[]: BACKEND_TEST_OPTIONS lets a test set
e.g. "tool_parser:hermes,reasoning_parser:qwen3" at LoadModel time.
Adds make target test-extra-backend-vllm that:
- docker-build-vllm
- loads Qwen/Qwen2.5-0.5B-Instruct
- runs health,load,predict,stream,tools with tool_parser:hermes
Drops backend/python/vllm/test_{cpu_inference,tool_calls}.py — those
standalone scripts were scaffolding used while bringing up the Python
backend; the e2e-backends harness now covers the same ground uniformly
alongside llama-cpp and ik-llama-cpp.
* ci(test-extra): run vllm e2e tests on CPU
Adds tests-vllm-grpc to the test-extra workflow, mirroring the
llama-cpp and ik-llama-cpp gRPC jobs. Triggers when files under
backend/python/vllm/ change (or on run-all), builds the local-ai
vllm container image, and runs the tests/e2e-backends harness with
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct, tool_parser:hermes,
and the tools capability enabled.
Uses ubuntu-latest (no GPU) — vllm runs on CPU via the cpu-vllm
wheel we pinned in requirements-cpu-after.txt. Frees disk space
before the build since the docker image + torch + vllm wheel is
sizeable.
* fix(vllm): build from source on CI to avoid SIGILL on prebuilt wheel
The prebuilt vllm 0.14.1+cpu wheel from GitHub releases is compiled with
SIMD instructions (AVX-512 VNNI/BF16 or AMX-BF16) that not every CPU
supports. GitHub Actions ubuntu-latest runners SIGILL when vllm spawns
the model_executor.models.registry subprocess for introspection, so
LoadModel never reaches the actual inference path.
- install.sh: when FROM_SOURCE=true on a CPU build, temporarily hide
requirements-cpu-after.txt so installRequirements installs the base
deps + torch CPU without pulling the prebuilt wheel, then clone vllm
and compile it with VLLM_TARGET_DEVICE=cpu. The resulting binaries
target the host's actual CPU.
- backend/Dockerfile.python: accept a FROM_SOURCE build-arg and expose
it as an ENV so install.sh sees it during `make`.
- Makefile docker-build-backend: forward FROM_SOURCE as --build-arg
when set, so backends that need source builds can opt in.
- Makefile test-extra-backend-vllm: call docker-build-vllm via a
recursive $(MAKE) invocation so FROM_SOURCE flows through.
- .github/workflows/test-extra.yml: set FROM_SOURCE=true on the
tests-vllm-grpc job. Slower but reliable — the prebuilt wheel only
works on hosts that share the build-time SIMD baseline.
Answers 'did you test locally?': yes, end-to-end on my local machine
with the prebuilt wheel (CPU supports AVX-512 VNNI). The CI runner CPU
gap was not covered locally — this commit plugs that gap.
* ci(vllm): use bigger-runner instead of source build
The prebuilt vllm 0.14.1+cpu wheel requires SIMD instructions (AVX-512
VNNI/BF16) that stock ubuntu-latest GitHub runners don't support —
vllm.model_executor.models.registry SIGILLs on import during LoadModel.
Source compilation works but takes 30-40 minutes per CI run, which is
too slow for an e2e smoke test. Instead, switch tests-vllm-grpc to the
bigger-runner self-hosted label (already used by backend.yml for the
llama-cpp CUDA build) — that hardware has the required SIMD baseline
and the prebuilt wheel runs cleanly.
FROM_SOURCE=true is kept as an opt-in escape hatch:
- install.sh still has the CPU source-build path for hosts that need it
- backend/Dockerfile.python still declares the ARG + ENV
- Makefile docker-build-backend still forwards the build-arg when set
Default CI path uses the fast prebuilt wheel; source build can be
re-enabled by exporting FROM_SOURCE=true in the environment.
* ci(vllm): install make + build deps on bigger-runner
bigger-runner is a bare self-hosted runner used by backend.yml for
docker image builds — it has docker but not the usual ubuntu-latest
toolchain. The make-based test target needs make, build-essential
(cgo in 'go test'), and curl/unzip (the Makefile protoc target
downloads protoc from github releases).
protoc-gen-go and protoc-gen-go-grpc come via 'go install' in the
install-go-tools target, which setup-go makes possible.
* ci(vllm): install libnuma1 + libgomp1 on bigger-runner
The vllm 0.14.1+cpu wheel ships a _C C++ extension that dlopens
libnuma.so.1 at import time. When the runner host doesn't have it,
the extension silently fails to register its torch ops, so
EngineCore crashes on init_device with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Also add libgomp1 (OpenMP runtime, used by torch CPU kernels) to be
safe on stripped-down runners.
* feat(vllm): bundle libnuma/libgomp via package.sh
The vllm CPU wheel ships a _C extension that dlopens libnuma.so.1 at
import time; torch's CPU kernels in turn use libgomp.so.1 (OpenMP).
Without these on the host, vllm._C silently fails to register its
torch ops and EngineCore crashes with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Rather than asking every user to install libnuma1/libgomp1 on their
host (or every LocalAI base image to ship them), bundle them into
the backend image itself — same pattern fish-speech and the GPU libs
already use. libbackend.sh adds ${EDIR}/lib to LD_LIBRARY_PATH at
run time so the bundled copies are picked up automatically.
- backend/python/vllm/package.sh (new): copies libnuma.so.1 and
libgomp.so.1 from the builder's multilib paths into ${BACKEND}/lib,
preserving soname symlinks. Runs during Dockerfile.python's
'Run backend-specific packaging' step (which already invokes
package.sh if present).
- backend/Dockerfile.python: install libnuma1 + libgomp1 in the
builder stage so package.sh has something to copy (the Ubuntu
base image otherwise only has libgomp in the gcc dep chain).
- test-extra.yml: drop the workaround that installed these libs on
the runner host — with the backend image self-contained, the
runner no longer needs them, and the test now exercises the
packaging path end-to-end the way a production host would.
* ci(vllm): disable tests-vllm-grpc job (heterogeneous runners)
Both ubuntu-latest and bigger-runner have inconsistent CPU baselines:
some instances support the AVX-512 VNNI/BF16 instructions the prebuilt
vllm 0.14.1+cpu wheel was compiled with, others SIGILL on import of
vllm.model_executor.models.registry. The libnuma packaging fix doesn't
help when the wheel itself can't be loaded.
FROM_SOURCE=true compiles vllm against the actual host CPU and works
everywhere, but takes 30-50 minutes per run — too slow for a smoke
test on every PR.
Comment out the job for now. The test itself is intact and passes
locally; run it via 'make test-extra-backend-vllm' on a host with the
required SIMD baseline. Re-enable when:
- we have a self-hosted runner label with guaranteed AVX-512 VNNI/BF16, or
- vllm publishes a CPU wheel with a wider baseline, or
- we set up a docker layer cache that makes FROM_SOURCE acceptable
The detect-changes vllm output, the test harness changes (tests/
e2e-backends + tools cap), the make target (test-extra-backend-vllm),
the package.sh and the Dockerfile/install.sh plumbing all stay in
place.
* feat: add PreferDevelopmentBackends setting, expose isMeta/isDevelopment in API
- Add PreferDevelopmentBackends config field, CLI flag, runtime setting
- Add IsDevelopment() method to GalleryBackend
- Use AvailableBackendsUnfiltered in UI API to show all backends
- Expose isMeta, isDevelopment, preferDevelopmentBackends in backend API response
* feat: upgrade banner with Upgrade All button, detect pre-existing backends
- Add upgrade banner on Backends page showing count and Upgrade All button
- Fix upgrade detection for backends installed before version tracking:
flag as upgradeable when gallery has a version but installed has none
- Fix OCI digest check to flag backends with no stored digest as upgradeable
* feat: add backend versioning data model foundation
Add Version, URI, and Digest fields to BackendMetadata for tracking
installed backend versions and enabling upgrade detection. Add Version
field to GalleryBackend. Add UpgradeAvailable/AvailableVersion fields
to SystemBackend. Implement GetImageDigest() for lightweight OCI digest
lookups via remote.Head. Record version, URI, and digest at install time
in InstallBackend() and propagate version through meta backends.
* feat: add backend upgrade detection and execution logic
Add CheckBackendUpgrades() to compare installed backend versions/digests
against gallery entries, and UpgradeBackend() to perform atomic upgrades
with backup-based rollback on failure. Includes Agent A's data model
changes (Version/URI/Digest fields, GetImageDigest).
* feat: add AutoUpgradeBackends config and runtime settings
Add configuration and runtime settings for backend auto-upgrade:
- RuntimeSettings field for dynamic config via API/JSON
- ApplicationConfig field, option func, and roundtrip conversion
- CLI flag with LOCALAI_AUTO_UPGRADE_BACKENDS env var
- Config file watcher support for runtime_settings.json
- Tests for ToRuntimeSettings, ApplyRuntimeSettings, and roundtrip
* feat(ui): add backend version display and upgrade support
- Add upgrade check/trigger API endpoints to config and api module
- Backends page: version badge, upgrade indicator, upgrade button
- Manage page: version in metadata, context-aware upgrade/reinstall button
- Settings page: auto-upgrade backends toggle
* feat: add upgrade checker service, API endpoints, and CLI command
- UpgradeChecker background service: checks every 6h, auto-upgrades when enabled
- API endpoints: GET /backends/upgrades, POST /backends/upgrades/check, POST /backends/upgrade/:name
- CLI: `localai backends upgrade` command, version display in `backends list`
- BackendManager interface: add UpgradeBackend and CheckUpgrades methods
- Wire upgrade op through GalleryService backend handler
- Distributed mode: fan-out upgrade to worker nodes via NATS
* fix: use advisory lock for upgrade checker in distributed mode
In distributed mode with multiple frontend instances, use PostgreSQL
advisory lock (KeyBackendUpgradeCheck) so only one instance runs
periodic upgrade checks and auto-upgrades. Prevents duplicate
upgrade operations across replicas.
Standalone mode is unchanged (simple ticker loop).
* test: add e2e tests for backend upgrade API
- Test GET /api/backends/upgrades returns 200 (even with no upgrade checker)
- Test POST /api/backends/upgrade/:name accepts request and returns job ID
- Test full upgrade flow: trigger upgrade via API, wait for job completion,
verify run.sh updated to v2 and metadata.json has version 2.0.0
- Test POST /api/backends/upgrades/check returns 200
- Fix nil check for applicationInstance in upgrade API routes
* feat: add toggle mechanism to enable/disable models from loading on demand
Implements #9303 - Adds ability to disable models from being auto-loaded
while keeping them in the collection.
Backend changes:
- Add Disabled field to ModelConfig struct with IsDisabled() getter
- New ToggleModelEndpoint handler (PUT /models/toggle/:name/:action)
- Request middleware returns 403 when disabled model is requested
- Capabilities endpoint exposes disabled status
Frontend changes:
- Toggle switch in System > Models table Actions column
- Visual indicators: dimmed row, red Disabled badge, muted icons
- Tooltip describes toggle function on hover
- Loading state while API call is in progress
* fix: remove extra closing brace causing syntax error in request middleware
* refactor: reorder Actions column - Stop button before toggle switch
* refactor: migrate from toggle to toggle-state per PR review feedback
* feat(ui): Add dynamic model editor with autocomplete
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(docs): Add link to longformat installation video
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* always enable parallel requests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: move tests to ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(smart router): order by available vram
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add distributed mode (experimental)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix data races, mutexes, transactions
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix events and tool stream in agent chat
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* use ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(cron): compute correctly time boundaries avoiding re-triggering
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not flood of healthy checks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not list obvious backends as text backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* tests fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop redundant healthcheck
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: wire min_p
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: inferencing defaults
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(refactor): re-use iterative parser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: generate automatically inference defaults from unsloth
Instead of trying to re-invent the wheel and maintain here the inference
defaults, prefer to consume unsloth ones, and contribute there as
necessary.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: apply defaults also to models installed via gallery
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: be consistent and apply fallback to all endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add fine-tuning endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(experimental): add fine-tuning endpoint and TRL support
This changeset defines new GRPC signatues for Fine tuning backends, and
add TRL backend as initial fine-tuning engine. This implementation also
supports exporting to GGUF and automatically importing it to LocalAI
after fine-tuning.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* commit TRL backend, stop by killing process
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* move fine-tune to generic features
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* add evals, reorder menu
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This creates a new model config page. Presently just allows configuring
pipelines, but can be extending the future to other types of models.
However pipelines are quite easy to create a form for and require
editing to create.
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui): add users and authentication support
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: allow the admin user to impersonificate users
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: ui improvements, disable 'Users' button in navbar when no auth is configured
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add OIDC support
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: gate models
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: cache requests to optimize speed
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* small UI enhancements
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ui): style improvements
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: cover other paths by auth
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: separate local auth, refactor
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* security hardening, approval mode
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: fix tests and expectations
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: update localagi/localrecall
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(gallery): Switch to expandable box instead of pop-over and display model files
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui, backends): Add individual backend logging
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(ui): Set the context settings from the model config
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(mlx-distributed): add new MLX-distributed backend
Add new MLX distributed backend with support for both TCP and RDMA for
model sharding.
This implementation ties in the discovery implementation already in
place, and re-uses the same P2P mechanism for the TCP MLX-distributed
inferencing.
The Auto-parallel implementation is inspired by Exo's
ones (who have been added to acknowledgement for the great work!)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* expose a CLI to facilitate backend starting
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: make manual rank0 configurable via model configs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add missing features from mlx backend
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
feat: add --data-path CLI flag for persistent data separation
- Add LOCALAI_DATA_PATH environment variable and --data-path CLI flag
- Default data path: /data (separate from configuration directory)
- Automatic migration on startup: moves agent_tasks.json, agent_jobs.json, collections/, and assets/ from old config dir to new data path
- Backward compatible: preserves old behavior if LOCALAI_DATA_PATH is not set
- Agent state and job directories now use DataPath with proper fallback chain
- Update documentation with new flag and docker-compose example
This separates mutable persistent data (collectiondb, agents, assets, skills) from configuration files, enabling better volume mounting and data persistence in containerized deployments.
Signed-off-by: localai-bot <localai-bot@noreply.github.com>
Co-authored-by: localai-bot <localai-bot@noreply.github.com>
* feat(functions): add peg-based parsing
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: support returning toolcalls directly from backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: do run PEG only if backend didn't send deltas
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add standalone and agentic functionalities
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* expose agents via responses api
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: Add LOCALAI_DISABLE_MCP environment variable to disable MCP support
- Added DisableMCP field to RunCMD struct in core/cli/run.go
- Added LOCALAI_DISABLE_MCP environment variable support
- Added DisableMCP field to ApplicationConfig struct
- Added DisableMCP AppOption function
- Updated MCP endpoint routing to check appConfig.DisableMCP
- When LOCALAI_DISABLE_MCP is set to true/1/yes, MCP endpoints are not registered
When set, all MCP functionality is disabled and appropriate error messages
are returned to users.
Use Cases:
- Security-conscious deployments where MCP is not needed
- Reducing attack surface
- Compliance requirements that prohibit certain protocol support
Environment variable: LOCALAI_DISABLE_MCP=true
Signed-off-by: localai-bot <localai-bot@users.noreply.github.com>
* docs: Add documentation for LOCALAI_DISABLE_MCP environment variable
- Add section explaining how to disable MCP support using environment variable
- Document use cases for disabling MCP
- Provide examples for CLI and Docker usage
Signed-off-by: localai-bot <localai-bot@users.noreply.github.com>
---------
Signed-off-by: localai-bot <localai-bot@users.noreply.github.com>
Co-authored-by: localai-bot <localai-bot@users.noreply.github.com>
fix: Add vllm-omni backend to video generation model detection
- Include vllm-omni in the list of backends that support FLAG_VIDEO
- This allows models like vllm-omni-wan2.2-t2v to appear in the video model selector UI
- Fixes issue #8659 where video generation models using vllm-omni backend were not showing in the dropdown
Co-authored-by: team-coding-agent-1 <team-coding-agent-1@localai.dev>
When a model is configured with 'known_usecases: [rerank]' in the YAML
config, the reranking endpoint was not being matched because:
1. The GuessUsecases function only checked for backend == 'rerankers'
2. The syncKnownUsecasesFromString() was not being called when loading
configs via yaml.Unmarshal in readModelConfigsFromFile
This fix:
1. Updates GuessUsecases to also check if Reranking is explicitly set to
true in the model config (in addition to checking backend type)
2. Adds syncKnownUsecasesFromString() calls after yaml.Unmarshal in
readModelConfigsFromFile to ensure known_usecases are properly parsed
Fixes#8658
Signed-off-by: localai-bot <localai-bot@users.noreply.github.com>
Co-authored-by: localai-bot <localai-bot@users.noreply.github.com>
* fix(realtime): Use the voice provided by the user or none at all
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(ui,config): Allow pipeline models to have no backend and use same validation in frontend
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* feat(musicgen): add ace-step and UI interface
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Correctly handle model dir
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop auto-download
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add to models, fixup UIs icons
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* l4t13 is incompatbile
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* avoid pinning version for cuda12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop l4t12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: extract reasoning to its own package
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* make sure we detect thinking tokens from template
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Allow to override via config, add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
fix: validate MCP configuration in model config
Fixes#7334
The Validate() function was not checking if MCP configuration
(mcp.stdio and mcp.remote) contains valid JSON. This caused
malformed JSON with missing commas to be silently accepted.
Changes:
- Add MCP configuration validation to ModelConfig.Validate()
- Properly report validation errors instead of discarding them
- Add test cases for valid and invalid MCP configurations
The fix ensures that malformed JSON in MCP config sections
will now be caught and reported during validation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Signed-off-by: majiayu000 <1835304752@qq.com>
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
* feat: allow to set forcing backends eviction while requests are in flight
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: try to make the request sit and retry if eviction couldn't be done
Otherwise calls that in order to pass would need to shutdown other
backends would just fail.
In this way instead we make the request sit and retry eviction until it
succeeds. The thresholds can be configured by the user.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* expose settings to CLI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(loader): refactor single active backend support to LRU
This changeset introduces LRU management of loaded backends. Users can
set now a maximum number of models to be loaded concurrently, and, when
setting LocalAI in single active backend mode we set LRU to 1 for
backward compatibility.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(agent): agent jobs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Multiple webhooks, simplify
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Do not use cron with seconds
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Create separate pages for details
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Detect if no models have MCP configuration, show wizard
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Make services test to run
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): add watchdog settings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Do not re-read env
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Some refactor, move other settings to runtime (p2p)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add API Keys handling
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Allow to disable runtime settings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Documentation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* show MCP toggle in index
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop context default
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
