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mudler's LocalAI [bot]andEttore Di Giacinto
a77780ad14
* feat(version): include OS and arch in the outbound User-Agent
Registries and galleries already receive LocalAI/<version>; adding the
platform follows ordinary client convention and discloses nothing a
registry cannot infer from the manifest it is asked for.
Updates the User-Agent note in docs/content/getting-started/models.md,
which documented the old format.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* feat(downloader): identify LocalAI on outbound requests
pkg/oci has always sent a User-Agent; the downloader sent none, so gallery
reads, model-file downloads, resume probes, content-length probes and the
HuggingFace safety scan all went out as a bare Go HTTP client, unattributable
to LocalAI by the hosts serving them.
HuggingFaceScan moves off the client's Get shorthand to an explicit request
for the same reason — the shorthand gives no place to hang a header.
Extends the User-Agent note in docs/content/getting-started/models.md, which
claimed the header was sent only to Ollama and OCI registries.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* feat(gallery): add a mirrors list to gallery configuration
Mirrors are an availability fallback, tried in order only after the primary
URL fails. omitempty keeps existing configurations byte-identical.
The slice makes config.Gallery non-comparable with ==, which broke the two
slices.Equal callers in the runtime settings registry. Replace them with an
explicit Gallery.Equal / GalleriesEqual so a gallery list that differs from
the baseline only by its mirrors still counts as env/CLI-set. Equal compares
the Verification block by value; == compared it by pointer identity, which
called two structurally identical policies different.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* fix(downloader): treat an HTTP error status as a failed read
ReadWithCallback handed the response body to its callback whatever the
status was, so a 404 page or a 502 from a CDN arrived as if it were a
gallery index or a model config: it parsed to nothing, got cached for an
hour, and no caller could tell the source had been down. DownloadFile has
always checked the status; this path never did.
Mirror fallback depends on it — a source that answers with an error page
has to count as unreachable, or the next candidate is never tried.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* feat(gallery): fall back to mirrors when the primary source fails
Candidates are tried primary-first with a bounded timeout each, and a
source that just failed is skipped for a cooldown so a dead host is not
re-dialled on every listing. When every candidate is in cooldown they are
all tried anyway: refusing to serve a gallery we might be able to reach is
worse than one slow request.
The one-hour index cache is untouched and stays keyed on the gallery's own
identity, so a mirror-served fetch fills the entry the primary would have.
No SSRF validation is applied to the candidates. validateGalleryConfigURL
guards GetGalleryConfigFromURL because that URL arrives in a request body;
mirrors come from the operator's gallery configuration, the same place the
primary has always come from, and the index fetch has never validated the
primary. Validating mirrors while the primary goes unchecked would buy
nothing and would break the deployment mirrors exist for — an index served
from a host on the LAN.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* fix(gallery): loosen the mirror fetch timeout and stop blaming the caller
The downloader only ever bounded response headers, never the body, so the
per-attempt deadline added with mirror fallback was the first whole-transfer
timeout this path has had. At 30s the default 2.2 MB index demanded ~75 KB/s
sustained: a rural-DSL, mobile or satellite user who used to wait 60s and
succeed would now fail, and then eat a 10-minute cooldown on a source that
was perfectly healthy. Raised to 120s (~19 KB/s), which no link that could
go on to download a model will miss, and made it a var so a test can shorten
it and prove a hanging candidate is actually abandoned.
Caller cancellation is no longer recorded as a failure of the source.
Unreachable today since getGalleryElements passes context.Background(), but
once a request context is wired through, a browser disconnect would have
blackholed every candidate for ten minutes over something the sources had
no part in.
Also document that mirrors do not cover a .ref gallery URL: the reference is
resolved before mirrors are considered, so a .ref that cannot be fetched
fails the gallery outright. Routing .ref resolution through the candidate
list needs a per-candidate resolve-and-fetch and a decision about cache
identity, which is more than this change should carry.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* feat(gallery): serve the last known good index when everything is offline
A successful fetch is cached alongside the models directory and served when
no source is reachable, so an offline or airgapped machine can still list
its gallery. Entries may be stale in that state, and the fallback is logged.
The copy is deliberately kept out of the models directory, where a <name>.yaml
file is read as an installed model's configuration, and is named after a digest
of the gallery URL so the model and backend galleries cannot collide. Writing
it is best effort: a read-only or full disk must not fail a fetch that
otherwise succeeded.
Also corrects the mirror scheme list in the docs: the HuggingFace prefixes are
huggingface://, hf:// and hf.co/, not huggingface:.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* fix(gallery): only cache a response that is really a gallery index
The last known good copy was written on any 2xx, before anything looked
at the bytes: the parse only happens later, in getGalleryElements. A
captive portal, a corporate proxy or a CDN error page all answer HTTP 200
with HTML, so any of them could overwrite a good copy. The listing fails
then and there, and the next offline start — the one case this cache
exists for — serves the interception page instead of the gallery it
already had.
Probe the body before persisting it: unmarshal into a []any and keep the
older copy unless the result is a non-empty sequence. An empty document
is rejected too. It parses fine, so a parse-only check would still let a
blank response replace a populated index with one that lists nothing,
which from the user's side is the same outage; and an empty index is
worth nothing offline, so there is no case where caching it beats keeping
what came before. The live body is still returned to the caller — the
probe gates persistence only, and getGalleryElements remains the thing
that reports a real parse failure.
Also in this pass:
- The empty-basePath guard only caught exact "". galleryCachePath(".")
and galleryCachePath("models") still resolved the cache sibling against
the process working directory, which is what the guard was written to
prevent. Reject any non-absolute base.
- The docs claimed the offline cache "applies to every gallery, with or
without mirrors". Not true for a .ref URL: the reference is resolved
before the cache is consulted, so a .ref gallery fails offline even
after a successful earlier fetch, and the cache file it writes can
never be read. Extend the .ref warning and qualify the sentence.
- pkg/oci's UserAgent comment never mentioned the platform component
added earlier on this branch.
- resetGalleryFailures and expireGalleryFailure had no non-test callers;
move them into the test file.
- The all-candidates-failed error reported len(attempt), so a three
mirror gallery with two sources in cooldown said "all 1 source(s)
failed" — which reads as a misconfiguration. Report how many were
configured and how many were skipped.
- Give the package's tests their own TMPDIR. The cache is a sibling of
the models directory, which is right in production, but specs that
build a models directory directly under /tmp made the sibling resolve
to /tmp/cache and left it behind after every run.
Assisted-by: Claude:claude-opus-5 [go vet] [go test]
* fix(gallery): convert the new tests to Ginkgo and clear the lint gate
.agents/coding-style.md requires Ginkgo v2 + Gomega for every Go test and
has forbidigo enforce it; the stdlib-style tests still in the tree are tech
debt, not a pattern. Every test file this branch added was written in the
forbidden style, which is what turned CI red.
Convert all five of them. internal had no suite bootstrap, so add one;
core/config, core/gallery and pkg/downloader already have theirs and are
reused, so no package mixes styles. pkg/downloader/useragent_test.go and
read_status_test.go were not in CI's forbidigo list but used the same
forbidden calls, so they are converted too.
The one conversion with a trap in it is core/gallery. Go's t.TempDir()
yields $TMPDIR/<TestName>NNNN/001, so the gallery cache — a sibling of the
models directory — was isolated per test. GinkgoT().TempDir() yields a flat
$TMPDIR/ginkgoNNNN, which would put every spec's cache in one shared
directory and break the specs that count files in it. tempModelsDir()
restores the original isolation.
Also make the deliberate cleanup-path ignores explicit with `_ =`, drop the
gallery cache directory to 0750 (nothing outside the server's own user and
group reads it), and justify the cache read with a #nosec G304 comment in
the form already used elsewhere in the tree: the path is a hex sha256 under
a fixed directory with a non-absolute base already rejected, so no
caller-supplied text reaches it.
Re-ran the mutations these specs were verified against — dropping the
platform suffix from UserAgent, making Gallery.Equal ignore Mirrors and
ignore Name, removing persistGalleryIndex's validity probe, removing the
!filepath.IsAbs guard, not skipping a cooled-down candidate, and dropping
the per-attempt timeout. All seven still fail the converted specs.
Assisted-by: Claude:claude-opus-5 [go vet] [go test] [golangci-lint] [gosec]
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
A small core, not a bundle. Each backend wraps a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX...) in its own image, pulled only when a model needs it. You install nothing you don't use.
- Composable by design: backends are separate and pulled on demand, so you install only what your model needs
- Open and extensible: load any model, or build your own backend in any language against an open interface
- Drop-in API compatibility: OpenAI, Anthropic, and ElevenLabs APIs across every backend
- Any model, any modality: LLMs, vision, voice, image, and video behind one API
- Any hardware: NVIDIA, AMD, Intel, Apple Silicon, Vulkan, or CPU-only
- Multi-user ready: API key auth, user quotas, role-based access
- Built-in AI agents: autonomous agents with tool use, RAG, MCP, and skills
- Privacy-first: your data never leaves your infrastructure
Created by Ettore Di Giacinto and maintained by the LocalAI team.
📖 Documentation | 💬 Discord | 💻 Quickstart | 🖼️ Models | ❓FAQ
Guided tour
https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18
Click to see more!
User and auth
https://github.com/user-attachments/assets/228fa9ad-81a3-4d43-bfb9-31557e14a36c
Agents
https://github.com/user-attachments/assets/6270b331-e21d-4087-a540-6290006b381a
Usage metrics per user
https://github.com/user-attachments/assets/cbb03379-23b4-4e3d-bd26-d152f057007f
Fine-tuning and Quantization
https://github.com/user-attachments/assets/5ba4ace9-d3df-4795-b7d4-b0b404ea71ee
WebRTC
https://github.com/user-attachments/assets/ed88e34c-fed3-4b83-8a67-4716a9feeb7b
Quickstart
macOS
Note: The DMG is not signed by Apple. After installing, run:
sudo xattr -d com.apple.quarantine /Applications/LocalAI.app. See #6268 for details.
Containers (Docker, podman, ...)
Already ran LocalAI before? Use
docker start -i local-aito restart an existing container.
CPU only:
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
NVIDIA GPU:
# CUDA 13
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13
# CUDA 12
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
# NVIDIA Jetson ARM64 (CUDA 12, for AGX Orin and similar)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
# NVIDIA Jetson ARM64 (CUDA 13, for DGX Spark)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13
AMD GPU (ROCm):
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
Intel GPU (oneAPI):
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
Vulkan GPU:
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
Loading models
# From the model gallery (see available models with `local-ai models list` or at https://models.localai.io)
local-ai run llama-3.2-1b-instruct:q4_k_m
# From Huggingface
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
# From the Ollama OCI registry
local-ai run ollama://gemma:2b
# From a YAML config
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
# From a standard OCI registry (e.g., Docker Hub)
local-ai run oci://localai/phi-2:latest
To work with a running LocalAI server from the terminal, start the built-in agent from another shell. It answers questions, reads your files and runs commands on your machine, asking you to approve anything that changes state. Inside a session, /models lists installed models and /model <name> switches between them. See the Terminal agent docs.
# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m
# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m
Automatic Backend Detection: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see GPU Acceleration.
For more details, see the Getting Started guide.
Latest News
- June 2026: New native biometric backends from the LocalAI team: voice-detect.cpp for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and face-detect.cpp for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python
insightfaceandspeaker-recognitionbackends (PR #10441). - June 2026: New realtime voice assistant demo (a tiny Go client for the Realtime API with a full talk-back voice loop and tool calling), plus streaming of the realtime LLM / TTS / transcription pipeline stages and configurable WebRTC ICE candidates.
- June 2026: Big speech push: the parakeet.cpp ASR engine gains NeMo-faithful segment timestamps, a multilingual streaming Nemotron-3.5 model, dynamic batching for concurrent transcription and CUDA graphs; the new CrispASR backend adds multi-architecture ASR + TTS, and 60 Piper TTS voices across 42 languages land in the gallery (plus per-request TTS instructions and params).
- June 2026: New backends and models: locate-anything.cpp for open-vocabulary object detection via ggml, Ideogram4 image generation in stablediffusion-ggml, llama.cpp video input, and the Gemma 4 QAT family with MTP speculative-decoding pairs. Plus an interactive CLI chat mode and RAG source citations in agent responses.
- June 2026: Distributed mode hardening: prefix-cache-aware routing, a production-ready request router with auto-sized embedding/rerank batches, ds4 layer-split distributed inference, NATS JWT auth + TLS/mTLS, and resumable file uploads.
- May 2026: LocalAI 4.3.0 -
llama.cppprompt cache on by default (repeated system prompts collapse from minutes to seconds), keyless cosign signing of backend OCI images, per-API-key + per-user usage attribution, Distributed v3 with per-request replica routing. Release notes - May 2026: LocalAI 4.2.0 - LocalAI sees and hears: voice recognition, face recognition + antispoofing liveness, speaker diarization. Plus drop-in Ollama API, video generation, redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. Release notes
- April 2026: LocalAI 4.1.0 - LocalAI becomes a control tower: distributed cluster mode with VRAM-aware smart routing + autoscaling, multi-user platform with OIDC and API keys, per-user quotas with predictive analytics, in-UI fine-tuning with TRL (auto-export to GGUF), on-the-fly quantization backend, visual pipeline editor. Release notes
- March 2026: LocalAI 4.0.0 - native agentic orchestration with the new Agenthub community hub, full React UI rewrite with Canvas mode, MCP Apps + client-side with tool streaming, WebRTC realtime audio, MLX-distributed. Release notes
- February 2026: Realtime API for audio-to-audio with tool calling, ACE-Step 1.5 support
- January 2026: LocalAI 3.10.0 — Anthropic API support, Open Responses API, video & image generation (LTX-2), unified GPU backends, tool streaming, Moonshine, Pocket-TTS. Release notes
- December 2025: Dynamic Memory Resource reclaimer, Automatic multi-GPU model fitting (llama.cpp), Vibevoice backend
- November 2025: Import models via URL, Multiple chats and history
- October 2025: Model Context Protocol (MCP) support for agentic capabilities
- September 2025: New Launcher for macOS and Linux, extended backend support for Mac and Nvidia L4T, MLX-Audio, WAN 2.2
- August 2025: MLX, MLX-VLM, Diffusers, llama.cpp now supported on Apple Silicon
- July 2025: All backends migrated outside the main binary — lightweight, modular architecture
For older news and full release notes, see GitHub Releases and the blog.
Features
- Text generation (
llama.cpp,transformers,vllm... and more) - Text to Audio
- Audio to Text
- Image generation
- OpenAI-compatible tools API
- Realtime API (Speech-to-speech)
- Embeddings generation
- Constrained grammars
- Download models from Huggingface
- Vision API
- Object Detection
- Reranker API
- P2P Inferencing
- Distributed Mode — Horizontal scaling with PostgreSQL + NATS
- Model Context Protocol (MCP)
- Built-in Agents — Autonomous AI agents with tool use, RAG, skills, SSE streaming, and Agent Hub
- Backend Gallery — Install/remove backends on the fly via OCI images
- Voice Activity Detection (Silero-VAD)
- Integrated WebUI
Supported Backends & Acceleration
LocalAI supports 60+ backends including llama.cpp, vLLM, SGLang, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for NVIDIA (CUDA 12/13), AMD (ROCm), Intel (oneAPI/SYCL), Apple Silicon (Metal), Vulkan, and NVIDIA Jetson (L4T). All backends can be installed on-the-fly from the Backend Gallery.
See the full Backend & Model Compatibility Table and GPU Acceleration guide.
Backends built by us
Most backends wrap a best-in-class upstream engine. A handful of them are native C/C++/GGML engines (no Python at inference) developed and maintained by the LocalAI project itself:
| Backend | What it does |
|---|---|
| vllm.cpp | From-scratch C++20 port of vLLM for text generation: paged KV cache, continuous batching, prefix caching, safetensors + GGUF loading, engine-enforced structured output, on CPU, CUDA, Metal and Vulkan |
| parakeet.cpp | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
| moss-transcribe.cpp | C++/GGML port of OpenMOSS MOSS-Transcribe-Diarize: joint long-form transcription, speaker diarization and timestamping in a single pass |
| moss-tts.cpp | C++/GGML port of the OpenMOSS MOSS-TTS family: text-to-speech (MOSS-TTS-Local v1.5, 48 kHz stereo) with reference-audio voice cloning, through the MOSS-Audio-Tokenizer neural codec |
| magpie-tts.cpp | C++/GGML port of NVIDIA's Magpie TTS Multilingual 357M: 22.05 kHz mono text-to-speech in 5 voices and 9+ languages, with the NanoCodec neural codec and tokenizer/G2P embedded in a single GGUF |
| ced.cpp | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
| voice-detect.cpp | Speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion), replacing the Python speaker-recognition backend |
| voxtral-tts.c | Mistral Voxtral-4B-TTS text-to-speech in pure C: 20 preset voices across 9 languages, 24 kHz WAV output, no dependencies beyond libc |
| vibevoice.cpp | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
| rf-detr.cpp | Native RF-DETR object detection and instance segmentation |
| locate-anything.cpp | Open-vocabulary object detection and visual grounding (LocateAnything-3B) |
| depth-anything.cpp | Depth Anything 3 monocular metric depth + camera pose estimation |
| face-detect.cpp | Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend |
| free-splatter.cpp | Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required |
| trellis2.cpp | C++/GGML port of Microsoft TRELLIS.2: single-image to textured 3D mesh (GLB with PBR materials) |
| privacy-filter.cpp | Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier |
| LocalVQE | Joint acoustic echo cancellation, noise suppression, and dereverberation |
| local-store | Local-first vector database for embeddings (shipped in-tree) |
We also maintain apex-quant, a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.
Resources
- Documentation
- LLM fine-tuning guide
- Build from source
- Kubernetes installation
- Integrations & community projects
- Installation video walkthrough
- Blog: release write-ups, benchmarks and engineering notes
- Examples — including the realtime voice assistant demo (Go client for the Realtime API with tool calling)
Team
LocalAI is maintained by a small team of humans, together with the wider community of contributors.
- Ettore Di Giacinto — original author and project lead
- Richard Palethorpe — maintainer
A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in Discord — LocalAI is a community-driven project and wouldn't exist without you. See the full contributors list.
Citation
If you utilize this repository, data in a downstream project, please consider citing it with:
@misc{localai,
author = {Ettore Di Giacinto},
title = {LocalAI: The free, Open source OpenAI alternative},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/go-skynet/LocalAI}},
Sponsors
Do you find LocalAI useful?
Support the project by becoming a backer or sponsor. Your logo will show up here with a link to your website.
A huge thank you to our generous sponsors who support this project covering CI expenses, and our Sponsor list:
Past sponsors
Individual sponsors
A special thanks to individual sponsors, a full list is on GitHub and buymeacoffee. Special shout out to drikster80 for being generous. Thank you everyone!
Star history
License
LocalAI is a community-driven project created by Ettore Di Giacinto and maintained by the LocalAI team.
MIT - Author Ettore Di Giacinto mudler@localai.io
Acknowledgements
LocalAI couldn't have been built without the help of great software already available from the community. Thank you!
- llama.cpp
- https://github.com/tatsu-lab/stanford_alpaca
- https://github.com/cornelk/llama-go for the initial ideas
- https://github.com/antimatter15/alpaca.cpp
- https://github.com/EdVince/Stable-Diffusion-NCNN
- https://github.com/ggerganov/whisper.cpp
- https://github.com/rhasspy/piper
- exo for the MLX distributed auto-parallel sharding implementation
Contributors
This is a community project, a special thanks to our contributors!