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* feat(usage): add Source, APIKeyID, APIKeyName columns to UsageRecord Adds three additive columns plus UsageSource* constants. The columns are auto-migrated by InitDB. APIKeyID is a nullable foreign reference to UserAPIKey.ID; APIKeyName is snapshotted on each row so revoked keys keep showing their name in history. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): backfill Source on pre-feature usage rows InitDB now classifies any pre-existing usage_record with an empty source: 'legacy-api-key' user -> legacy, everything else -> web. The backfill is idempotent (only touches NULL/empty rows). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add GetUserUsageBySource aggregator Groups by (bucket, source, api_key_id, api_key_name). Filters out legacy by default. Returns both per-bucket detail and roll-ups (by_source, by_key sorted desc and capped at 200, grand_total). The MAX(created_at) projection is iterated via Rows().Scan into a string column and parsed manually because the SQLite driver surfaces the aggregated timestamp as a string, which database/sql refuses to scan directly into time.Time. Postgres returns a real timestamp; the same string path handles its RFC3339 form too. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(usage): log Rows() errors and assert LastUsed in tests Adds rows.Err() and Rows() open-failure logging in computeSourceTotals so silent data drops surface in logs. Logs on parseLastUsedString format misses for the same reason. Strengthens the snapshot-survival test to assert LastUsed is a recent timestamp, locking the SQLite time-string parser behaviour. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add admin GetAllUsageBySource with filters and truncation Optional user_id and api_key_id filters (composed with AND). Legacy bucket is included for admin callers. truncated=true when more than 200 distinct keys would be in the by_key roll-up. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(auth): plumb auth_source and auth_apikey through Echo context tryAuthenticate now sets auth_source on every successful branch (web for session/Bearer-session, apikey for Bearer-key/x-api-key/ token-cookie, legacy for legacy env key match). For named-key branches it also stores the resolved *UserAPIKey under auth_apikey so downstream middlewares can snapshot id+name without re-validating. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(auth): expand tryAuthenticate godoc and cover Bearer-session branch Documents all three context-keys side effects (auth_source, auth_apikey, _auth_session) plus the split of responsibilities with the parent Middleware. Adds a test for the Bearer-as-session-token classification so future regressions there fail loudly. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): UsageMiddleware records source + snapshots key name Reads auth_source and auth_apikey from the Echo context (set by auth.Middleware in the previous task). Snapshots UserAPIKey.ID and Name onto each row so revoked keys remain readable in history. Falls back to source=web when no auth_source is set (auth disabled or unrecognised path). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add /api/auth/usage/sources and admin variant Self endpoint filters legacy server-side; admin endpoint includes legacy and accepts user_id + api_key_id filters. Response includes buckets, totals.{by_source, by_key, grand_total}, and a truncated flag set when the per-key roll-up was capped at 200. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(routes): mark test mirror handlers as keep-in-sync with production The newTestAuthApp helper duplicates production route handlers inline because it cannot use RegisterAuthRoutes (which requires a *application.Application). Naming the source path on each mirror makes the drift contract explicit for future maintainers. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add usageApi.getMySources/getAdminSources + i18n strings Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add Sources tab skeleton with data fetch Adds Usage page tab that fetches /api/auth/usage/sources (or the admin variant). Renders raw totals plus a placeholder key list; real visualisations land in subsequent commits. Restructures the existing tab button block so Models and Sources are visible to non-admins (Users remains admin-only). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): source mix ribbon + searchable/sortable sources table Replaces the SourcesTab placeholder rendering with two reusable components: SourceMixRibbon (one segmented bar per source class) and SourcesTable (search + sort + revoked-key dim). Pulls the current API key list to detect revoked keys. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): skip revoked-key detection until the key list is known existingKeyIds defaulted to an empty Set, which made every live api_key row render as (revoked) during the brief window before apiKeysApi.list() resolved, and permanently after a fetch failure. Use null as the unknown state and suppress the revoked badge until the parent provides a real Set. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): top-N stacked time chart and drill-in chip for Sources tab Top 7 sources by total tokens get distinct colours; the rest roll up into 'Other'. Clicking a row in the SourcesTable dims everything except that series in the chart; the chip is the canonical clear. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(usage): document per-API-key Sources tab and endpoints Extends features/authentication.md Usage Tracking section with: - A 'Sources' tab description and source-class taxonomy - Endpoint documentation for /api/auth/usage/sources and the admin variant - Response shape example with by_source / by_key / grand_total - Migration note about pre-feature row backfill Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(usage): silence errcheck on deferred rows.Close CI errcheck flagged the bare 'defer rows.Close()' in computeSourceTotals. Wrap in a closure that discards the close error explicitly; an error here is non-actionable since we have already drained the rows and logged any iteration failure. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(usage): bound batcher intake and add Shutdown/FlushNow hooks The pre-existing usage batcher had no cap on its add() path; the usageMaxPending=5000 constant only guarded the re-queue path after a failed write, leaving memory growth unbounded if the DB fell behind. This commit: - Adds the cap to add() so saturation drops new records (rate-limited warn at 1/1024) instead of growing unbounded. - Raises usageMaxPending to 50000 to absorb realistic inference bursts. - Replaces the package-level batcher global with a mutex-guarded pair plus a currentBatcher() accessor so Init / Shutdown cycles are race-free. - Adds ShutdownUsageRecorder() for graceful drain on process exit (not yet wired into app shutdown, just published). - Adds FlushNow() for deterministic tests; the middleware suite no longer needs 6s sleeps per spec and now runs in ~50ms instead of 18s. - Re-queue on failed flush is now cap-aware: prepends as much of the failed batch as fits alongside concurrent arrivals, instead of dropping the whole batch when full. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): drain usage batcher on graceful shutdown Registers ShutdownUsageRecorder with the existing signals.RegisterGracefulTerminationHandler so SIGINT/SIGTERM synchronously flushes any in-memory usage records before the process exits. Without this, up to one flush interval (5s) of recorded usage was lost when LocalAI restarted. Refs: #9862 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!