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LocalAI [bot]andEttore Di Giacinto
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* feat(parakeet-cpp): L0 backend scaffold, LoadModel + AudioTranscription (text) Add a Go gRPC backend that bridges LocalAI to parakeet.cpp via the flat C-API (parakeet_capi.h), loaded with purego (cgo-less, mirrors the whisper / vibevoice-cpp backends). L0 scope: - main.go: dlopen libparakeet.so (override via PARAKEET_LIBRARY), register the C-API entry points, start the gRPC server. - goparakeetcpp.go: Load (parakeet_capi_load), AudioTranscription (parakeet_capi_transcribe_path, decoder=0 = per-arch default head), Free, serialized through base.SingleThread since the C engine is a thread-unsafe singleton. char* returns are bound as uintptr so the malloc'd buffer is freed via parakeet_capi_free_string after copy. - AudioTranscriptionStream returns a clear "not implemented in L0" error (closes the channel so the server doesn't hang), wired in L2. - Makefile: clone-at-pin + cmake (PARAKEET_VERSION for bump_deps.sh), with a local-symlink dev shortcut; run.sh / package.sh mirror whisper. - Test auto-skips without PARAKEET_BACKEND_TEST_MODEL/_WAV fixtures. Builds clean (CGO_ENABLED=0), gofmt clean, test passes. The single unsafeptr vet note in goStringFromCPtr is documented and matches the whisper backend's tolerated pattern. Word/segment timestamps (L1) and cache-aware streaming (L2) follow. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(parakeet-cpp): L1 word/segment timestamps via transcribe_path_json AudioTranscription now calls parakeet_capi_transcribe_path_json and shapes the per-word / per-token timestamps into the TranscriptResult: - Bind parakeet_capi_transcribe_path_json (purego, char* as uintptr like the other returns) and register it in main.go + the test loader. - Parse the JSON document ({"text","words":[{w,start,end,conf}], "tokens":[{id,t,conf}]}) into typed structs. - Synthesise a single whole-clip segment (parakeet emits no native segment boundaries) spanning the first word start to the last word end; token ids populate Segment.Tokens. - Attach word-level timings only when timestamp_granularities=["word"], matching the OpenAI API (segment-level default). secondsToNanos mirrors the whisper backend's nanosecond convention. Verified end-to-end against tdt_ctc-110m (f16): both the default and word-granularity specs pass; builds clean, gofmt clean, vet shows only the one documented unsafeptr note shared with the whisper backend. Cache-aware streaming (L2) follows. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(parakeet-cpp): L2 cache-aware streaming with EOU segmentation Wire AudioTranscriptionStream to the streaming RNN-T C-API: - Bind parakeet_capi_stream_{begin,feed,finalize,free}; feed takes 16 kHz mono float PCM ([]float32 via purego) and writes *eou_out on <EOU>/<EOB>. - Decode opts.Dst to 16 kHz mono PCM (utils.AudioToWav + go-audio, same as the whisper backend), feed it in 1 s chunks, and emit each newly-finalized text run as a TranscriptStreamResponse delta. - <EOU>/<EOB> events close the current segment; a closing FinalResult carries the full transcript plus the per-utterance segments (with a whole-clip fallback segment when no EOU fired). - stream_begin returns 0 for non-streaming models, surfaced as a clear error instead of an empty stream. Honours context cancellation between chunks. Frees every malloc'd delta and the session. Verified end-to-end against realtime_eou_120m-v1 (f16): the streamed transcript matches the offline 110m reference word-for-word, deltas reconstruct the final text, and the spec passes alongside the offline specs. Builds clean, gofmt clean, vet shows only the shared documented unsafeptr note. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(parakeet-cpp): L3 register backend in build/CI/gallery (whisper parity) Wire the new Go gRPC parakeet-cpp backend (parakeet.cpp ggml port of NVIDIA NeMo Parakeet ASR) into LocalAI's build/CI/gallery surfaces, matching the existing ggml whisper Go backend 1:1. - .github/backend-matrix.yml: add 11 linux entries + 1 darwin entry mirroring every whisper build (cpu amd64/arm64, intel sycl f32/f16, vulkan amd64/arm64, nvidia cuda-12, nvidia cuda-13, nvidia-l4t-arm64, nvidia-l4t-cuda-13-arm64, rocm hipblas, metal-darwin-arm64), all on ./backend/Dockerfile.golang with backend: "parakeet-cpp" and -*-parakeet-cpp tag-suffixes. - scripts/changed-backends.js: explicit inferBackendPath branch resolving parakeet-cpp to backend/go/parakeet-cpp/ before the generic golang branch. - .github/workflows/bump_deps.yaml: track the PARAKEET_VERSION pin in backend/go/parakeet-cpp/Makefile (repo mudler/parakeet.cpp, branch master). - backend/index.yaml: add ¶keetcpp meta + latest/development image entries for every matrix tag-suffix. - Makefile: add backends/parakeet-cpp to .NOTPARALLEL, BACKEND_PARAKEET_CPP definition, docker-build target eval, and test-extra-backend-parakeet-cpp- transcription target (mirrors test-extra-backend-whisper-transcription). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(parakeet-cpp): L4 gallery importer for parakeet GGUFs Add ParakeetCppImporter so parakeet.cpp GGUFs auto-detect on /import-model and route to the parakeet-cpp backend (it also surfaces in /backends/known, which drives the import dropdown). - Match is narrow: a .gguf whose name carries a parakeet architecture token (<arch>-<size>-<quant>.gguf, e.g. tdt_ctc-110m-f16.gguf, rnnt-0.6b-q4_k.gguf, realtime_eou_120m-v1-q8_0.gguf), a direct URL to one, or preferences.backend="parakeet-cpp". It deliberately does NOT claim arbitrary llama-style GGUFs, nor the upstream nvidia/parakeet-* NeMo repos (.nemo, not runnable here). - Registered in the ASR batch BEFORE LlamaCPPImporter so its GGUFs aren't swallowed by the generic .gguf importer. - Import nests files under parakeet-cpp/models/<name>/, defaults to the smallest quant (q4_k, near-lossless on parakeet) with a size-ladder fallback, and honours preferences.quantizations / name / description. Tested with synthetic HF details (no network): metadata, positive matches (HF repo, direct URL, preference), narrowness negatives (llama GGUF, NeMo repo), and import (default quant, override, direct URL), 9 specs pass, build/vet/gofmt clean. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(parakeet-cpp): document the parakeet-cpp transcription backend Add parakeet-cpp to the audio-to-text backend list and a dedicated usage section: direct GGUF import (auto-detects to the backend), model YAML, word-level timestamps via timestamp_granularities[]=word, and cache-aware streaming with the realtime_eou model. Points at the mudler/parakeet-cpp-gguf collection repo. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(parakeet-cpp): wire transcription gRPC e2e test into test-extra The L3 commit added the test-extra-backend-parakeet-cpp-transcription Makefile target but never invoked it in CI. Mirror the whisper job: - Add a parakeet-cpp output to detect-changes (emitted by changed-backends.js from the matrix entry). - Add tests-parakeet-cpp-grpc-transcription, gated on the parakeet-cpp path filter / run-all, building the backend image and running the transcription e2e against tdt_ctc-110m + the JFK clip. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(parakeet-cpp): drop em dashes from comments and docs Replace em dashes with plain punctuation in the backend comments, the importer, package.sh, and the audio-to-text docs section (and use "and" instead of the multiplication sign). No behaviour change. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add parakeet-cpp f16 models to the model gallery Add the 10 NVIDIA Parakeet models (f16, the recommended quality/speed default) as gallery entries that install on the parakeet-cpp backend from mudler/parakeet-cpp-gguf: tdt_ctc-110m/1.1b, tdt-0.6b-v2/v3, tdt-1.1b, ctc-0.6b/1.1b, rnnt-0.6b/1.1b, and the cache-aware streaming realtime_eou_120m-v1. Each pins the file sha256 and routes transcript usecases to the backend. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(parakeet-cpp): satisfy govet lint + bump PARAKEET_VERSION - goparakeetcpp.go: //nolint:govet on the C-owned-pointer unsafe.Pointer conversion (golangci-lint reports new-only issues, so unlike the whisper backend's identical line this one is flagged). - Makefile: bump PARAKEET_VERSION to the current parakeet.cpp master commit (the previous pin's commit no longer exists after upstream history was squashed), so the backend image clone/build resolves again. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(parakeet-cpp): pin PARAKEET_VERSION to a tag-stable commit The previous SHA pin was orphaned when parakeet.cpp's single-commit master was amended/force-pushed, so the backend image clone (git fetch <sha>) failed across every build variant. Repoint to 845c29e, which upstream now keeps permanently fetchable via the `localai-backend-pin` tag, so future upstream amends no longer break the backend build. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(parakeet-cpp): init the ggml submodule in the backend image clone The backend Dockerfile clones parakeet.cpp at PARAKEET_VERSION with a shallow fetch + checkout but never initialised submodules, so third_party/ggml was empty and the parakeet.cpp cmake build failed at `add_subdirectory(third_party/ggml)` (CMakeLists.txt:53) on every build variant. Add `git submodule update --init --recursive --depth 1 --single-branch` after checkout, mirroring the whisper backend. Verified locally: clone + submodule + cmake configure now succeeds. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(parakeet-cpp): statically link ggml into libparakeet.so The shared libparakeet.so linked ggml's shared libs (libggml*.so), but the package only ships libparakeet.so, so at runtime dlopen failed with "libggml.so.0: cannot open shared object file" (the e2e transcription test panicked on load). Build ggml static + PIC (BUILD_SHARED_LIBS=OFF, CMAKE_POSITION_INDEPENDENT_CODE=ON) so libparakeet.so embeds ggml and depends only on system libs already present in the runtime image. Verified locally: ldd shows no libggml dependency. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(parakeet-cpp): non-streaming fallback in AudioTranscriptionStream The e2e streaming test ran AudioTranscriptionStream against tdt_ctc-110m (not a cache-aware streaming model), so stream_begin returned 0 and the call errored. Per LocalAI's streaming contract (and the whisper backend), a non-streaming model should fall back to a single offline transcription emitted as one delta plus a closing FinalResult. Do that instead of erroring, so the streaming endpoint works for every parakeet model. Verified locally: the streaming spec passes against the non-streaming 110m model via fallback. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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!