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* feat(crispasr): backend source files (Go gRPC server, C-ABI shim, build files) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * polish(crispasr): brand error strings + fix stale shim comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): register backend in root Makefile Mirror the whisper Go backend registration for the new crispasr backend: NOTPARALLEL entry, prepare-test-extra/test-extra hooks, BACKEND_CRISPASR definition, docker-build target generation, and the docker-build-backends aggregate target. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): add backend build matrix entries Mirror the 11 whisper golang Dockerfile matrix entries (CPU amd64/arm64, CUDA 12/13, L4T CUDA 13, Intel SYCL f32/f16, Vulkan amd64/arm64, L4T arm64, ROCm hipblas) with backend and tag-suffix substituted to crispasr. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add crispasr backend gallery entries Add the crispasr meta anchor and its full set of image gallery entries (cpu, metal, cuda12/13, rocm, intel-sycl f32/f16, vulkan, L4T arm64, L4T cuda13 arm64, plus -development variants), mirroring the whisper backend gallery block. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): bump CRISPASR_VERSION via bump_deps workflow Track CrispStrobe/CrispASR main branch and bump CRISPASR_VERSION in backend/go/crispasr/Makefile. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): don't wire fixture-gated test into test-extra Mirror the whisper Go backend: its AudioTranscription test is gated on model/audio fixtures and skips in CI, so building crispasr (the heaviest ggml compile in the tree) inside the unit-test lane adds a long compile for zero coverage. The backend image build in backend-matrix.yml remains the authoritative compile check. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): add darwin metal build entry (mirror whisper) The metal-crispasr gallery entries and capabilities.metal mapping reference -metal-darwin-arm64-crispasr, which is only produced by an includeDarwin entry. Mirror whisper's darwin metal entry so the tag actually gets built. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): place hipblas matrix entry next to whisper twin Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): register crispasr as pref-only ASR backend + test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): port whisper behavioral suite (cancellation + streaming) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): fix skip message env var names to CRISPASR_* Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): switch shim to crispasr_session_* multi-architecture API The shim used whisper_full(), which in CrispASR is the whisper-only path: libcrispasr only transcribes Whisper GGUFs through it. Multi-architecture transcription (Parakeet, Voxtral, Qwen3-ASR, Canary, Granite, FunASR, Paraformer, SenseVoice, ...) goes through the crispasr_session_* C-ABI, which auto-detects the architecture from the GGUF and dispatches to the matching backend. Rewrite the C shim around crispasr_session_open / _transcribe_lang / _result_* and add get_backend() so the selected backend is logged. load_model now takes a threads param (session_open binds n_threads at open). The session result is segment+word based with no token IDs and no per-decode callback, so drop n_tokens / get_token_id / get_segment_speaker_turn_next / set_new_segment_callback. set_abort is kept for API parity but is best-effort: the session transcribe is blocking with no abort hook. Update the purego bindings and gocrispasr.go to match: tokens are left empty, speaker-turn handling is removed, and AudioTranscriptionStream emits one delta per non-empty segment after the blocking decode returns (no progressive streaming via the session API), preserving the concat(deltas) == final.Text invariant. crispasr_session_set_translate is exported by libcrispasr but not declared in crispasr.h, so it is forward-declared in the shim alongside the open/transcribe/result functions. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): link full CrispASR backend set for multi-arch support The shim's crispasr_session_* dispatch calls into the per-architecture backend libs (parakeet, voxtral, qwen3_asr, canary, funasr, paraformer, sensevoice, ...), which CrispASR builds as static archives. Linking only crispasr + ggml dead-stripped every backend object from the final module (nm backend-symbol count: 0), leaving a whisper-only .so. Link the same backend set as crispasr-cli so the static archives are pulled in. After this the module carries the backend symbols (nm count 407, .so grows from ~2.1MB to ~6.7MB) and the session API can dispatch to every compiled-in architecture. Also rewrite ${CMAKE_SOURCE_DIR}/examples/talk-llama to ${PROJECT_SOURCE_DIR}/... in the vendored src/CMakeLists.txt: CrispASR locates its vendored llama.cpp via ${CMAKE_SOURCE_DIR}, which is wrong when CrispASR is add_subdirectory'd (CMAKE_SOURCE_DIR points at this backend dir, not the CrispASR root). PROJECT_SOURCE_DIR is correct both standalone and as a subproject; the sed is idempotent. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): adapt suite to session API (blocking, no decode callback) Register the new symbol set (drop the removed token/speaker/callback funcs, add get_backend; load_model now takes 2 args). The session transcribe is blocking with no abort hook, so a mid-decode cancel can't interrupt it: change the cancellation spec to cancel the context before the call and assert codes.Canceled from the pre-call ctx.Err() check, dropping the <5s mid-decode timing assertion. The streaming spec still holds with per-segment post-decode emission (>=2 deltas, concat(deltas) == final.Text). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add CrispASR ASR model entries (-crispasr) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): keep only session-auto-detectable CrispASR ASR models The crispasr backend loads models via crispasr_session_open, which auto-detects the backend from the GGUF general.architecture using crispasr_detect_backend_from_gguf. Architectures not in that detect map cannot be opened, so those gallery entries fail to load. Removed entries whose architecture is not wired into CrispASR v0.6.11's session auto-detect router (they can be re-added when upstream maps them): - Not in the detect map: data2vec, firered-asr, funasr, fun-asr-mlt-nano, glm-asr, hubert, kyutai-stt, mega-asr, mimo-asr, moonshine{,-de,-streaming,-tiny-de}, omniasr{,-llm,-llm-1b}, paraformer, sensevoice. - Pending verification (filename-heuristic routed, not arch-detected): parakeet-ctc-0.6b, parakeet-ctc-1.1b. Their GGUFs are routed to the fastconformer-ctc backend by a filename heuristic in the model registry, which implies general.architecture is not a mapped string. Kept the parakeet rnnt/tdt_ctc variants: convert-parakeet-to-gguf.py writes general.architecture="parakeet" unconditionally and encodes the rnnt/ctc distinction in metadata fields, so they session-auto-detect. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): TTS synthesis via crispasr_session_synthesize (24kHz) Add tts_synthesize/tts_free/tts_set_voice to the C-ABI shim. They reuse the already-open g_session (crispasr_session_open auto-detects a TTS model) and dispatch to the upstream synthesis call, which returns malloc'd 24 kHz mono float PCM. Orpheus needs a SNAC codec path that we do not set, so it returns NULL here and surfaces as an error Go-side. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): implement TTS/TTSStream gRPC methods Bind the new shim functions via purego and implement TTS, TTSStream and a writeWAV24k helper. synthesize copies the C-owned PCM out before freeing it; TTS writes a 24 kHz mono 16-bit WAV to req.Dst via go-audio/wav. CrispASR has no progressive synth, so TTSStream synthesizes fully, encodes to WAV, and emits the bytes as a single chunk; it owns the results-channel close (the gRPC server wrapper ranges until close), mirroring vibevoice-cpp's TTSStream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): log when a TTS voice override is not honored Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add CrispASR vibevoice-tts model entry Only vibevoice-tts works through the current shim: qwen3-tts, chatterbox, and orpheus require companion codec/s3gen/SNAC paths (set_codec_path / set_s3gen_path) that the shim doesn't wire yet, and kokoro/indextts/voxcpm2 aren't in the session auto-detect map. Those are follow-ups. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): gated TTS synthesis spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(crispasr): satisfy golangci-lint (errcheck defers + unsafeptr nolint) The crispasr Go file is entirely new, so new-from-merge-base lints every line (unlike the grandfathered whisper backend it was forked from): - handle os.RemoveAll / fh.Close return values in AudioTranscription - annotate the two intentional C-pointer unsafe.Slice sites with //nolint:govet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): backend: and codec: model options (explicit arch + companion files) Add two model-config options to the CrispASR backend via opts.Options: - backend:<name> selects an explicit CrispASR backend (bypassing auto-detect) by routing load_model through crispasr_session_open_explicit, unlocking architectures the detector won't pick on its own (qwen3, cohere, granite, voxtral, moonshine, mimo-asr, orpheus, kokoro, chatterbox, etc.). - codec:<path> loads a companion file (qwen3-tts codec, orpheus SNAC, chatterbox s3gen, or mimo-asr tokenizer) via the universal crispasr_session_set_codec_path setter after the session opens. A relative path resolves against the model directory. rc==0 means success or not-applicable; only a negative rc is fatal. The C shim load_model gains a backend_name argument and a new set_codec_path entry point; the Go bridge parses the prefix:value options and registers the new symbol. The vad_only path is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): expand CrispASR models via backend:/codec: options (explicit arch + companions) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(gallery): use virtual.yaml base for crispasr models The crispasr entries are just backend + model + a couple options, fully expressed inline via overrides:/files: in gallery/index.yaml. Point each url: at the shared gallery/virtual.yaml (the established 'virtual' model trick) and drop the 36 redundant per-model gallery/*-crispasr.yaml files. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): drop voice-requiring TTS entries (keep vibevoice-tts) Real e2e showed qwen3-tts/orpheus/chatterbox don't synthesize through the current shim: the codec: companion loads fine, but these engines additionally need a voice pack / voice prompt / reference clip (qwen3-tts base errors 'no voice'; chatterbox is zero-shot cloning; orpheus uses named voices) that the backend doesn't wire. (qwen3-tts also can't auto-detect: its GGUF arch is 'qwen3tts', unmapped by the detector — would need backend:qwen3-tts.) Removed to avoid shipping non-working gallery entries; vibevoice-tts (built-in voice, e2e-verified) remains the working TTS. Voice-pack wiring is a follow-up. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): speaker: and voice: TTS options (baked speakers + voice packs/prompts) speaker:<name> -> crispasr_session_set_speaker_name (baked speakers: qwen3-tts CustomVoice, orpheus). voice:<path>(+voice_text:<ref>) -> crispasr_session_set_voice (voice-pack GGUF, or WAV zero-shot clone with ref text). Applied at Load as the default voice; req.Voice still overrides the speaker per request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): re-add e2e-verified TTS engines (chatterbox, qwen3-tts-customvoice, orpheus) 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>
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
- Drop-in API compatibility — OpenAI, Anthropic, ElevenLabs APIs
- 36+ backends — llama.cpp, vLLM, transformers, whisper, diffusers, MLX...
- 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
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
- 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 News page.
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 36+ backends including llama.cpp, vLLM, 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.
Resources
- Documentation
- LLM fine-tuning guide
- Build from source
- Kubernetes installation
- Integrations & community projects
- Installation video walkthrough
- Media & blog posts
- Examples
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!