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* refactor(transcription): propagate request ctx through ModelTranscription* Replaces context.Background() with the HTTP request ctx so client disconnects start cancelling the gRPC call. No backend-side abort wiring yet — that comes in a later commit. Pure plumbing. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cli): pass ctx to backend.ModelTranscription Follow-up toe65d3e1fwhich threaded ctx through ModelTranscription but missed the CLI caller. CLI commands have no request-scoped ctx, so context.Background() is correct here. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(audio): propagate request ctx into TTS, sound-gen, audio-transform Same ctx-plumbing pattern applied to the rest of the audio path. CLI callers use context.Background() since there is no request scope; HTTP callers use c.Request().Context(). Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(backend): propagate request ctx into biometric, detection, rerank, diarization paths Replaces remaining context.Background() sites in core/backend with the caller's ctx. After this commit, every core/backend/*.go entry point threads the request ctx end-to-end to the gRPC client. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(grpc): plumb ctx through AIModel.AudioTranscription{,Stream} Adds context.Context as first parameter to the AIModel interface methods that wrap whisper-style transcription. Server-side gRPC handler now forwards the per-RPC ctx (server-streaming uses stream.Context()). Whisper, Voxtral, vibevoice-cpp, and sherpa-onnx accept the parameter; none uses it yet — the actual cancellation primitive lands in the next commit so this is pure plumbing. Assisted-by: Claude:claude-sonnet-4-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(whisper): add abort_callback hook in the C++ bridge Installs a std::atomic<int> flag, wires it into whisper_full_params.abort_callback, and exposes a set_abort(int) C symbol so Go can flip the flag from a goroutine watching the request context. transcribe() now distinguishes abort (return 2) from real whisper_full failure (return 1). Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(whisper): register set_abort symbol in the purego loader Adds the Go-side binding for the new C export so the next commit can call CppSetAbort(1) from a watcher goroutine on ctx.Done(). Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(whisper): honor ctx cancellation and return codes.Canceled A watcher goroutine watches ctx.Done() during AudioTranscription and calls CppSetAbort(1) on cancel. whisper_full sees abort_callback return true at the next compute graph step, returns non-zero, and the bridge returns 2 -> AudioTranscription maps that to codes.Canceled. Adds an opt-in test (gated on WHISPER_MODEL_PATH / WHISPER_AUDIO_PATH) that asserts cancellation latency under 5s and proves the abort flag resets cleanly so the next transcription succeeds. Assisted-by: Claude:claude-sonnet-4-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(whisper): join the cancel watcher goroutine before returning Follow-up to85edf9d2. The previous commit used `defer close(done)` and called the watcher "joined synchronously" — but close() only signals, it does not block until the goroutine exits. That left a window where a late CppSetAbort(1) from a cancelled call could land on the next call, after its C-side g_abort reset but before whisper_full() began polling the abort callback, corrupting the second transcription. Switch to a sync.WaitGroup join so wg.Wait() blocks until the watcher has actually returned from its select. Assisted-by: Claude:claude-sonnet-4-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(whisper): short-circuit pre-cancelled ctx in AudioTranscription If ctx is already Done() at entry, return codes.Canceled immediately instead of running the full transcription. The C-side g_abort reset happens at the start of transcribe() and would otherwise overwrite a watcher-set abort flag from an already-cancelled ctx, producing a spurious successful transcription on a request the client has already abandoned. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(tests/distributed): update testLLM mock for new AudioTranscription signature Phase B (93c48e19) added context.Context to AIModel.AudioTranscription but missed the testLLM mock in tests/e2e/distributed. CI golangci-lint caught it: *testLLM did not implement grpc.AIModel because the method signature lacked the ctx parameter, which broke the distributed test suite compilation and cascaded through every backend-build job that runs `go build ./...`. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(whisper): port cancellation test to Ginkgo/Gomega Project policy (.agents/coding-style.md, enforced by golangci-lint forbidigo) is that all Go tests must use Ginkgo v2 + Gomega — no stdlib testing patterns (t.Skip, t.Fatalf, etc.). Convert the cancellation test to a Describe/It block with Skip(...) for env gating and Expect/HaveOccurred for assertions. Same coverage: cancel mid-flight returns codes.Canceled within 5s and a follow-up transcription succeeds, proving the C-side g_abort flag resets cleanly. Assisted-by: Claude:claude-opus-4-7 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
- April 2026: Voice recognition, Face recognition, identification & liveness detection, Ollama API compatibility, Video generation in stable-diffusion.ggml, Backend versioning with auto-upgrade, Pin models & load-on-demand toggle, Universal model importer, new backends: sglang, ik-llama-cpp, TurboQuant, sam.cpp, Kokoros, qwen3tts.cpp, tinygrad multimodal
- March 2026: Agent management, New React UI, WebRTC, MLX-distributed via P2P and RDMA, MCP Apps, MCP Client-side
- 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:
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!