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LocalAI/README.md
mudler's LocalAI [bot] 4baa36ddd8 feat(backend): vllm-cpp - text-generation backend for vllm.cpp with llama.cpp-parity tool calling (#11100) 
* feat(backend): add vllm-cpp text-generation backend (vllm.cpp)

Wrap https://github.com/mudler/vllm.cpp - the LocalAI-team from-scratch C++20
port of vLLM (paged KV cache, continuous batching, prefix caching, safetensors
+ GGUF loading, no Python at inference) - as a Go gRPC backend over its stable
C ABI (ABI v2) via purego.

Backend (backend/go/vllm-cpp):
- Load -> vllm_engine_load: accepts a .gguf file or a config.json model dir
  (anything else is refused, satisfying the greedy-probe rule); context_size
  maps to max_model_len, options block_size/num_blocks/max_num_seqs size the
  KV cache and scheduler admission.
- Predict -> vllm_complete (blocking); PredictStream -> vllm_complete_stream
  with the per-delta C callback bridged into the gRPC stream. The backend
  embeds base.Base (not SingleThread): concurrent requests batch continuously
  in the engine's shared AsyncLLM scheduler.
- PredictOptions.Grammar -> the ABI's structured_grammar (GBNF), giving
  grammar-constrained tool calling at parity with llama-cpp; the ABI also
  exposes JSON-schema/regex/choice constraints.
- Hand-mirrored POD structs with layout locked by unit tests
  (unsafe.Offsetof vs the C offsets) and a runtime vllm_abi_version gate.
- One portable library per platform (vllm.cpp uses per-file SIMD tiers with
  runtime dispatch), so no avx/avx2/avx512 variant builds.

Wiring:
- backend-matrix: CPU amd64+arm64 (per-arch + manifest merge), CUDA 12/13
  amd64 (120a;121a Blackwell fat binary), L4T arm64 (121a, GB10/DGX Spark -
  the runtime-proven GPU target), Vulkan amd64, and Darwin arm64 Metal.
- backend/index.yaml meta + 12 image entries (latest/development x cpu,
  cuda12, cuda13, l4t, vulkan, metal); bump_deps registration for the
  VLLM_CPP_VERSION pin; root Makefile registration; test-extra runs the unit
  specs (pure Go, no engine build).
- Importers: preference-only swaps - llama-cpp (GGUF) and vllm (safetensors)
  advertise vllm-cpp via AdditionalBackends and emit backend: vllm-cpp
  without tokenizer templating (the C ABI takes the FINAL prompt; templating
  and tool parsing stay LocalAI-side). No auto-detect importer.
- Docs: backends list, top-level README maintained-engines table,
  compatibility table.

Verified: 20/20 Ginkgo specs against the real pinned engine and
Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU - blocking + streaming parity, greedy
determinism, stop words, GBNF-constrained generation, and 4 concurrent
streams; plus a dlopen/ABI-gate smoke of the built gRPC server binary.
Upstream ABI v2 + production structured-output wiring landed as
mudler/vllm.cpp@86013f3.

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(vllm-cpp): ride the autoparser code path - engine-side chat templating and tool engagement (ABI v3)

The backend now implements AIModelRich (PredictRich / PredictStreamRich) over
vllm.cpp's ABI v3 chat entry points, so chat and tool calling ride the SAME
code path as the llama.cpp autoparser: the ENGINE renders the model's chat
template, decides when a tool call engages, and parses it - LocalAI receives
pre-parsed ChatDelta / ToolCallDelta protos exactly as it does from llama-cpp.

- With use_tokenizer_template + structured Messages, PredictOptions lowers to
  ONE OpenAI chat request JSON (messages, tools, tool_choice, sampling,
  stream_options.include_usage) for vllm_chat / vllm_chat_stream. tool_choice
  auto lowers engine-side to a LAZY structural-tag decode constraint - free
  text until the model emits the tool trigger, then the call is
  grammar-constrained; required/named force a call. Tool output is parsed by
  the engine's streaming Hermes-style parser; each chat.completion.chunk maps
  onto ChatDeltas (content / reasoning_content / tool_calls) which the host
  already prefers over Go-side tag extraction. Without structured messages the
  plain path (LocalAI templating + optional GBNF grammar) applies unchanged.
- The engine resolves the chat template from the GGUF tokenizer.chat_template
  metadata (or tokenizer_config.json); templates beyond its minja subset -
  e.g. the full Qwen3.5 namespace()/macro template - degrade engine-side to a
  Hermes-aware fallback prompt (tools schemas + <tool_call> instruction) with
  a stderr witness, so structural-tag engagement keeps working.
- Importers now emit the same config shape as llama-cpp for vllm-cpp
  (use_tokenizer_template: true, no-grammar autoparser flow); only the
  llama-cpp-specific use_jinja option and the vllm-python parser options are
  dropped.
- Pin bumped to mudler/vllm.cpp@aaed7ec (ABI v3 + chat-prompt resolution).

Verified against the real engine and Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU: full
suite green - blocking chat, streaming deltas concatenating byte-equal to the
blocking answer, a REQUIRED tool call returning schema-valid arguments JSON,
and an AUTO run where the engine itself engages get_weather and streams parsed
tool deltas; plus unit specs for the request lowering, chunk->ChatDelta
mapping, and the C struct mirrors (ABI gate now v3).

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(vllm-cpp): ABI v5 - engine-side parser selection for 30 tool dialects + reasoning

Bump the vllm.cpp pin to the autoparser-parity engine: 30 tool-call dialects
(every pure-text parser in the pinned vLLM registry, each ported 1:1 with its
upstream tests), 7 reasoning parsers, google/minja as the template renderer
(the full Qwen3.5 template now renders engine-side), per-family structural
tags (tool_choice required/named compiles the model's NATIVE syntax where
expressible), and template auto-detection for both parser axes.

Backend changes:
- cModelParams mirrors ABI v5 (tool_parser + reasoning_parser fields,
  layout-locked by the offset tests; ABI gate now v5).
- New model options tool_parser:<name> / reasoning_parser:<name> pass through
  to the engine; unset means template auto-detection (18-row tool marker
  table; [THINK]->mistral, <think>->think_auto for reasoning); "none"
  disables the reasoning split; unknown names fail the first chat call.
- Chat chunks parse the `reasoning` field (the pin renamed
  reasoning_content), flowing into ChatDelta.ReasoningContent which the host
  already prefers.

Live e2e against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU, full suite green: the
real chat template renders (no more fallback), reasoning auto-detection picks
think_auto so markerless answers stay pure content (the live run caught the
deepseek_r1 content-swallow upstream and drove the think_auto fix), required
tool_choice returns schema-valid arguments, auto tool_choice engages
engine-side and streams parsed deltas, and blocking/streaming stay
byte-identical. Turn latency also dropped (proper template EOS behavior).

Upstream program landed as mudler/vllm.cpp 86013f3..5fffe7e (ABI v2-v5,
minja, parser waves B1/B2/B4, reasoning seam, structural-tag registry,
think_auto).

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* chore(vllm-cpp): bump the engine pin to the ENG-wave close-out

mudler/vllm.cpp@df8909b: the six engine-backed vLLM tool-parser families
(qwen3-coder/xml/mimo, kimi_k2, glm45/47, minimax_m2, gemma4, seed_oss)
text-reimplemented from their wire formats and held to the upstream test
suites - 39 registered dialects; the pinned vLLM registry is now covered
except the three Rust/Harmony-backed families, descoped by decision. kimi_k2
also gains a full native structural-tag builder; four new template
auto-detection rows land with test-pinned ordering.

Full backend e2e re-run green against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU.

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): add the vllm-cpp-development gallery meta

The gallery grew the twelve latest/development image entries but was missing
the separate vllm-cpp-development meta (own capabilities map targeting the
-development image names), which every backend ships so the development
gallery resolves per-platform. Validated: all capability targets in both
metas resolve to existing entries, and every image URI's tag suffix matches
a backend-matrix build.

Also full-stack verified in this change's context (single-node local-ai from
this branch, locally-built backend under --backends-path, Qwen3.5-2B GGUF):
/v1/chat/completions non-stream (clean content + usage), streaming (SSE
deltas), tool_choice auto engaging get_weather engine-side with schema-valid
arguments and finish_reason=tool_calls, and streamed tool-call deltas in the
standard name-first cadence.

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): repair the CI backend builds - gcc-14 -Werror + fat-arch Triton

Two distinct failures took down all five vllm-cpp backend builds on the PR:

1. gcc-14 (ubuntu:24.04 CI images; the local toolchain is gcc-13) fails the
   engine build with -Werror=maybe-uninitialized in InputBatch::condense - a
   false positive through a staging std::optional's raw storage. Fixed
   upstream (mudler/vllm.cpp@61f3e85) by moving slot-to-slot directly;
   verified BOTH ways under dockerized g++-14.2 (unfixed reproduces CI's two
   diagnostics exactly, fixed compiles clean) with the engine's behavior
   suites green. Pin bumped to that sha.

2. The amd64 CUDA builds died at CMake configure: the vendored Triton-AOT
   cubin trees are per-arch and the engine refuses -DVLLM_CPP_TRITON=ON on a
   multi-arch (120a;121a) fat build unless pinned to one tree, which would be
   unsound for the other arch. Triton is now enabled only on the single-arch
   arm64/GB10 build (where the cubins matter); the fat amd64 binary uses the
   engine's non-AOT GDN path.

Backend e2e re-run green at the new pin (Qwen3.5-2B on CPU, full suite).

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): cuda-12 images cannot compile compute_121a - target 120a only

The second CI round surfaced a CUDA-version constraint: the cuda-12 (12.8)
image's nvcc rejects 'compute_121a' (GB10 arch support landed with CUDA 13),
killing the amd64 cuda-12 build at nvcc. Gate the architecture list on
CUDA_MAJOR_VERSION (exported by Dockerfile.golang): cuda-12 builds consumer
Blackwell 120a only, cuda-13 keeps the 120a;121a fat binary, arm64/l4t
(cuda-13) keeps single-arch 121a with the Triton cubins. GB10 is arm64, so
the amd64 cuda-12 image never served it - no capability change.

Verified by Makefile dry-run variable dumps for all three combinations
(cuda12 -> 120a; cuda13 -> 120a;121a; cpu -> CUDA off).

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): drop the cuda-12 variant - the engine needs the CUDA 13 toolchain

Third CI round, third layer: with the arch list already narrowed to 120a,
the cuda-12 (12.8) build still dies in ptxas compiling the sm_120a NVFP4 MMA
kernels ("Vector type too large, exceeds 128 bit limit") - the Blackwell fp4
path genuinely requires the CUDA 13 toolchain, and vllm.cpp supports
Blackwell-family GPUs only. Shipping a cuda-12 image without the fp4 kernels
would be a crippled build of an engine whose whole GPU story is fp4, so the
variant is dropped instead:

- backend-matrix: cuda-12 vllm-cpp entry removed (cuda-13 amd64, l4t arm64,
  cpu, vulkan, metal remain).
- gallery: cuda12 image entries removed; the nvidia capability now resolves
  to the cuda13 image in both metas; the nvidia-cuda-12 key is dropped so
  older-driver hosts fall back to the CPU image instead of an unrunnable one.
- backend Makefile: BUILD_TYPE=cublas under CUDA_MAJOR_VERSION=12 now fails
  fast with a clear message; cuda-13 keeps the 120a;121a fat binary and
  arm64/l4t keeps 121a with the Triton cubins.

Verified: Makefile branch dumps for all four combinations (cuda12 loud
error, cuda13 fat, arm64 121a+Triton, cpu off), YAML parses, matrix filter
tests green, gallery capability targets all resolve.

Assisted-by: Claude Code:claude-fable-5

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): forward multi-turn tool identity and reasoning to the engine

chatRequestJSON dropped Message.ToolCallId and Message.Name on role="tool"
replies and Message.ReasoningContent on assistant history, so a second
turn after tool execution reached the engine's chat template without the
fields that bind a tool result to the call it answers. Forward all three
(present-only, matching the OpenAI wire shape) and pin vllm.cpp to
6a0bd3e7, where ChatMessage parses/round-trips tool_calls, tool_call_id,
name and reasoning and the minja adapter exposes them to the template
context.

Adds the round-trip request-lowering spec (user -> assistant tool_call ->
tool reply -> lowered request) and re-ran the gated e2e suite against the
new engine pin with a real Qwen3.5 GGUF: chat, reasoning split, streaming
parity, required-tool and auto-tool cases all green.

Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): bump vllm.cpp for the darwin arm64 i8mm build fix

The darwin-metal CI job was the first build to compile the engine's arm
CPU-quant files on macOS and hit their Linux-only <asm/hwcap.h> /
<sys/auxv.h> includes. vllm.cpp 9e1c9025 detects i8mm per-OS (auxv on
Linux, sysctl on Apple Silicon) with kernels untouched. Gated e2e suite
re-run green against the new pin with a real Qwen3.5 GGUF.

Assisted-by: Claude Code:claude-fable-5 [Bash] [Read]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(vllm-cpp): darwin build - bound cmake parallelism when nproc is absent

The macOS runners have no nproc, so JOBS evaluated empty and
`cmake --build -j$(JOBS)` became bare `-j`: unlimited clang jobs on a
3-core/7GB Mac, which swap-thrashed until the 6h GHA timeout (the log
shows "nproc: Command not found" and 7+ concurrent clang processes being
reaped at the cutoff). Use the same portable fallback chain as the other
darwin backends: nproc, then sysctl hw.ncpu, then 4.

Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read]
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>
2026-07-26 23:04:48 +02:00

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<h1 align="center">
<br>
<img width="300" src="./core/http/static/logo.png"> <br>
<br>
</h1>
<p align="center">
<a href="https://github.com/go-skynet/LocalAI/stargazers" target="blank">
<img src="https://img.shields.io/github/stars/go-skynet/LocalAI?style=for-the-badge" alt="LocalAI stars"/>
</a>
<a href='https://github.com/go-skynet/LocalAI/releases'>
<img src='https://img.shields.io/github/release/go-skynet/LocalAI?&label=Latest&style=for-the-badge'>
</a>
<a href="LICENSE" target="blank">
<img src="https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge" alt="LocalAI License"/>
</a>
</p>
<p align="center">
<a href="https://twitter.com/LocalAI_API" target="blank">
<img src="https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge&logo=X&logoColor=white&label=LocalAI_API" alt="Follow LocalAI_API"/>
</a>
<a href="https://discord.gg/uJAeKSAGDy" target="blank">
<img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Discord&style=for-the-badge&query=approximate_member_count&url=https%3A%2F%2Fdiscordapp.com%2Fapi%2Finvites%2FuJAeKSAGDy%3Fwith_counts%3Dtrue&logo=discord" alt="Join LocalAI Discord Community"/>
</a>
</p>
<p align="center">
<a href="https://trendshift.io/repositories/5539" target="_blank"><img src="https://trendshift.io/api/badge/repositories/5539" alt="mudler%2FLocalAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>
<!-- Keep these links, translations synced daily. -->
<p align="center">
<a href="https://zdoc.app/de/mudler/LocalAI">Deutsch</a> |
<a href="https://zdoc.app/es/mudler/LocalAI">Español</a> |
<a href="https://zdoc.app/fr/mudler/LocalAI">français</a> |
<a href="https://zdoc.app/ja/mudler/LocalAI">日本語</a> |
<a href="https://zdoc.app/ko/mudler/LocalAI">한국어</a> |
<a href="https://zdoc.app/pt/mudler/LocalAI">Português</a> |
<a href="https://zdoc.app/ru/mudler/LocalAI">Русский</a> |
<a href="https://zdoc.app/zh/mudler/LocalAI">中文</a>
</p>
**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
![A small LocalAI core with backends (llama.cpp, vLLM, MLX, whisper.cpp, stable-diffusion, kokoro, parakeet.cpp...) plugged in as separate on-demand images](docs/static/images/diagrams/composable-core.png)
Created by [Ettore Di Giacinto](https://github.com/mudler) and maintained by the [LocalAI team](#team).
> [:book: Documentation](https://localai.io/) | [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) | [💻 Quickstart](https://localai.io/basics/getting_started/) | [🖼️ Models](https://models.localai.io/) | [❓FAQ](https://localai.io/faq/)
## Guided tour
https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18
<details>
<summary>
Click to see more!
</summary>
#### 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
</details>
## Quickstart
### macOS
<a href="https://github.com/mudler/LocalAI/releases/latest/download/LocalAI.dmg">
<img src="https://img.shields.io/badge/Download-macOS-blue?style=for-the-badge&logo=apple&logoColor=white" alt="Download LocalAI for macOS"/>
</a>
> **Note:** The DMG is not signed by Apple. After installing, run: `sudo xattr -d com.apple.quarantine /Applications/LocalAI.app`. See [#6268](https://github.com/mudler/LocalAI/issues/6268) for details.
### Containers (Docker, podman, ...)
> Already ran LocalAI before? Use `docker start -i local-ai` to restart an existing container.
#### CPU only:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
```
#### NVIDIA GPU:
```bash
# 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):
```bash
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):
```bash
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
```
#### Vulkan GPU:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
```
### Loading models
```bash
# 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 test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, `/models` lists installed models and `/model <name>` switches between them.
```bash
# 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](https://localai.io/features/gpu-acceleration/).
For more details, see the [Getting Started guide](https://localai.io/basics/getting_started/).
## Latest News
- **June 2026**: New native biometric backends from the LocalAI team: [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and [face-detect.cpp](https://github.com/mudler/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 `insightface` and `speaker-recognition` backends ([PR #10441](https://github.com/mudler/LocalAI/pull/10441)).
- **June 2026**: New [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-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](https://github.com/mudler/LocalAI/pull/10176) and [configurable WebRTC ICE candidates](https://github.com/mudler/LocalAI/pull/10231).
- **June 2026**: Big speech push: the [parakeet.cpp](https://github.com/mudler/parakeet.cpp) ASR engine gains [NeMo-faithful segment timestamps](https://github.com/mudler/LocalAI/pull/10207), a [multilingual streaming Nemotron-3.5 model](https://github.com/mudler/LocalAI/pull/10199), [dynamic batching for concurrent transcription](https://github.com/mudler/LocalAI/pull/10112) and [CUDA graphs](https://github.com/mudler/LocalAI/pull/10273); the new [CrispASR backend](https://github.com/mudler/LocalAI/pull/10099) adds multi-architecture ASR + TTS, and [60 Piper TTS voices across 42 languages](https://github.com/mudler/LocalAI/pull/10296) land in the gallery (plus [per-request TTS instructions and params](https://github.com/mudler/LocalAI/pull/10172)).
- **June 2026**: New backends and models: [locate-anything.cpp](https://github.com/mudler/LocalAI/pull/10264) for open-vocabulary object detection via ggml, [Ideogram4 image generation](https://github.com/mudler/LocalAI/pull/10201) in stablediffusion-ggml, [llama.cpp video input](https://github.com/mudler/LocalAI/pull/10216), and the [Gemma 4 QAT family with MTP speculative-decoding pairs](https://github.com/mudler/LocalAI/pull/10215). Plus an [interactive CLI chat mode](https://github.com/mudler/LocalAI/pull/10226) and [RAG source citations in agent responses](https://github.com/mudler/LocalAI/pull/10228).
- **June 2026**: Distributed mode hardening: [prefix-cache-aware routing](https://github.com/mudler/LocalAI/pull/10071), a [production-ready request router with auto-sized embedding/rerank batches](https://github.com/mudler/LocalAI/pull/10104), [ds4 layer-split distributed inference](https://github.com/mudler/LocalAI/pull/10098), [NATS JWT auth + TLS/mTLS](https://github.com/mudler/LocalAI/pull/10159), and [resumable file uploads](https://github.com/mudler/LocalAI/pull/10109).
- **May 2026**: **LocalAI 4.3.0** - `llama.cpp` [prompt cache on by default](https://github.com/mudler/LocalAI/pull/9925) (repeated system prompts collapse from minutes to seconds), [keyless cosign signing of backend OCI images](https://github.com/mudler/LocalAI/pull/9823), [per-API-key + per-user usage attribution](https://github.com/mudler/LocalAI/pull/9920), Distributed v3 with [per-request replica routing](https://github.com/mudler/LocalAI/pull/9968). [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.3.0)
- **May 2026**: **LocalAI 4.2.0** - LocalAI sees and hears: [voice recognition](https://github.com/mudler/LocalAI/pull/9500), [face recognition + antispoofing liveness](https://github.com/mudler/LocalAI/pull/9480), speaker diarization. Plus [drop-in Ollama API](https://github.com/mudler/LocalAI/pull/9284), [video generation](https://github.com/mudler/LocalAI/pull/9420), redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.2.0)
- **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](https://github.com/mudler/LocalAI/releases/tag/v4.1.0)
- **March 2026**: **LocalAI 4.0.0** - native agentic orchestration with the new [Agenthub](https://agenthub.localai.io) community hub, full React UI rewrite with Canvas mode, [MCP Apps + client-side](https://github.com/mudler/LocalAI/pull/8947) with tool streaming, [WebRTC realtime audio](https://github.com/mudler/LocalAI/pull/8790), [MLX-distributed](https://github.com/mudler/LocalAI/pull/8801). [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.0.0)
- **February 2026**: [Realtime API for audio-to-audio with tool calling](https://github.com/mudler/LocalAI/pull/6245), [ACE-Step 1.5 support](https://github.com/mudler/LocalAI/pull/8396)
- **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](https://github.com/mudler/LocalAI/releases/tag/v3.10.0)
- **December 2025**: [Dynamic Memory Resource reclaimer](https://github.com/mudler/LocalAI/pull/7583), [Automatic multi-GPU model fitting (llama.cpp)](https://github.com/mudler/LocalAI/pull/7584), [Vibevoice backend](https://github.com/mudler/LocalAI/pull/7494)
- **November 2025**: [Import models via URL](https://github.com/mudler/LocalAI/pull/7245), [Multiple chats and history](https://github.com/mudler/LocalAI/pull/7325)
- **October 2025**: [Model Context Protocol (MCP)](https://localai.io/docs/features/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](https://github.com/mudler/LocalAI/releases/tag/v3.2.0)
For older news and full release notes, see [GitHub Releases](https://github.com/mudler/LocalAI/releases) and the [News page](https://localai.io/basics/news/).
## Features
- [Text generation](https://localai.io/features/text-generation/) (`llama.cpp`, `transformers`, `vllm` ... [and more](https://localai.io/model-compatibility/))
- [Text to Audio](https://localai.io/features/text-to-audio/)
- [Audio to Text](https://localai.io/features/audio-to-text/)
- [Image generation](https://localai.io/features/image-generation)
- [OpenAI-compatible tools API](https://localai.io/features/openai-functions/)
- [Realtime API](https://localai.io/features/openai-realtime/) (Speech-to-speech)
- [Embeddings generation](https://localai.io/features/embeddings/)
- [Constrained grammars](https://localai.io/features/constrained_grammars/)
- [Download models from Huggingface](https://localai.io/models/)
- [Vision API](https://localai.io/features/gpt-vision/)
- [Object Detection](https://localai.io/features/object-detection/)
- [Reranker API](https://localai.io/features/reranker/)
- [P2P Inferencing](https://localai.io/features/distribute/)
- [Distributed Mode](https://localai.io/features/distributed-mode/) — Horizontal scaling with PostgreSQL + NATS
- [Model Context Protocol (MCP)](https://localai.io/docs/features/mcp/)
- [Built-in Agents](https://localai.io/features/agents/) — Autonomous AI agents with tool use, RAG, skills, SSE streaming, and [Agent Hub](https://agenthub.localai.io)
- [Backend Gallery](https://localai.io/backends/) — 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](https://localai.io/backends/).
See the full [Backend & Model Compatibility Table](https://localai.io/model-compatibility/) and [GPU Acceleration guide](https://localai.io/features/gpu-acceleration/).
### 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](https://github.com/mudler/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](https://github.com/mudler/parakeet.cpp) | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
| [moss-transcribe.cpp](https://github.com/localai-org/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](https://github.com/mudler/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](https://github.com/mudler/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](https://github.com/localai-org/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](https://github.com/localai-org/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](https://github.com/mudler/voxtral-tts.c) | Voxtral Realtime 4B speech-to-text in pure C |
| [vibevoice.cpp](https://github.com/mudler/vibevoice.cpp) | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
| [rf-detr.cpp](https://github.com/localai-org/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation |
| [locate-anything.cpp](https://github.com/mudler/locate-anything.cpp) | Open-vocabulary object detection and visual grounding (LocateAnything-3B) |
| [depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) | Depth Anything 3 monocular metric depth + camera pose estimation |
| [face-detect.cpp](https://github.com/mudler/face-detect.cpp) | Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend |
| [free-splatter.cpp](https://github.com/localai-org/free-splatter.cpp) | Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required |
| [privacy-filter.cpp](https://github.com/localai-org/privacy-filter.cpp) | Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier |
| [LocalVQE](https://github.com/localai-org/LocalVQE) | Joint acoustic echo cancellation, noise suppression, and dereverberation |
| [local-store](https://github.com/mudler/LocalAI) | Local-first vector database for embeddings (shipped in-tree) |
We also maintain [apex-quant](https://github.com/localai-org/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](https://localai.io/)
- [LLM fine-tuning guide](https://localai.io/docs/advanced/fine-tuning/)
- [Build from source](https://localai.io/basics/build/)
- [Kubernetes installation](https://localai.io/basics/getting_started/#run-localai-in-kubernetes)
- [Integrations & community projects](https://localai.io/docs/integrations/)
- [Installation video walkthrough](https://www.youtube.com/watch?v=cMVNnlqwfw4)
- [Media & blog posts](https://localai.io/basics/news/#media-blogs-social)
- [Examples](https://github.com/mudler/LocalAI-examples) — including the [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-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](https://github.com/mudler)** — original author and project lead
- **[Richard Palethorpe](https://github.com/richiejp)** — maintainer
A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in [Discord](https://discord.gg/uJAeKSAGDy) — LocalAI is a community-driven project and wouldn't exist without you. See the full [contributors list](https://github.com/mudler/LocalAI/graphs/contributors).
## 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](https://github.com/sponsors/mudler). 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](https://github.com/sponsors/mudler):
<p align="center">
<a href="https://www.spectrocloud.com/" target="blank">
<img height="200" src="https://github.com/user-attachments/assets/72eab1dd-8b93-4fc0-9ade-84db49f24962">
</a>
</p>
<details>
<summary>
Past sponsors
</summary>
<p align="center">
<a href="https://www.premai.io/" target="blank">
<img height="200" src="https://github.com/mudler/LocalAI/assets/2420543/42e4ca83-661e-4f79-8e46-ae43689683d6"> <br>
</a>
</p>
</details>
### Individual sponsors
A special thanks to individual sponsors, a full list is on [GitHub](https://github.com/sponsors/mudler) and [buymeacoffee](https://buymeacoffee.com/mudler). Special shout out to [drikster80](https://github.com/drikster80) for being generous. Thank you everyone!
## Star history
[![LocalAI Star history Chart](https://api.star-history.com/svg?repos=go-skynet/LocalAI&type=Date)](https://star-history.com/#go-skynet/LocalAI&Date)
## License
LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/) and maintained by the [LocalAI team](#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/ggerganov/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](https://github.com/exo-explore/exo) for the MLX distributed auto-parallel sharding implementation
## Contributors
This is a community project, a special thanks to our contributors!
<a href="https://github.com/go-skynet/LocalAI/graphs/contributors">
<img src="https://contrib.rocks/image?repo=go-skynet/LocalAI" />
</a>
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