Adding a New Backend

When adding a new backend to LocalAI, you need to update several files to ensure the backend is properly built, tested, and registered. Here's a step-by-step guide based on the pattern used for adding backends like moonshine:

1. Create Backend Directory Structure

Create the backend directory under the appropriate location:

Python backends: backend/python/<backend-name>/
Go backends: backend/go/<backend-name>/
C++ backends: backend/cpp/<backend-name>/
Rust backends: backend/rust/<backend-name>/

For Python backends, you'll typically need:

backend.py - Main gRPC server implementation
Makefile - Build configuration
install.sh - Installation script for dependencies
protogen.sh - Protocol buffer generation script
requirements.txt - Python dependencies
run.sh - Runtime script
test.py / test.sh - Test files

For Rust backends, you'll typically need (see backend/rust/kokoros/ as a reference):

Cargo.toml - Crate manifest; depend on the upstream project as a submodule under sources/
build.rs - Invokes tonic_build to generate gRPC stubs from backend/backend.proto (use the BACKEND_PROTO_PATH env var so the Makefile can inject the canonical copy)
src/ - The gRPC server implementation (implement Backend via tonic)
Makefile - Copies backend.proto into the crate, runs cargo build --release, then package.sh
package.sh - Uses ldd to bundle the binary's dynamic deps and ld.so into package/lib/
run.sh - Sets LD_LIBRARY_PATH/SSL_CERT_DIR and execs the binary via the bundled lib/ld.so
sources/<UpstreamProject>/ - Git submodule with the upstream Rust crate

2. Add Build Configurations to `.github/backend-matrix.yml`

The build matrix is data-only YAML at .github/backend-matrix.yml (not inside backend.yml itself). backend.yml (master push) and backend_pr.yml (PR) load it via scripts/changed-backends.js, which also handles per-file path filtering so only touched backends rebuild on PRs and master pushes alike. Add build matrix entries to .github/backend-matrix.yml for each platform/GPU type you want to support. Look at similar backends for reference — chatterbox/faster-whisper for Python, piper/silero-vad for Go, kokoros for Rust.

Without an entry here no image is ever built or pushed, and the gallery entry in backend/index.yaml will point at a tag that does not exist. The dockerfile: field must point at ./backend/Dockerfile.<lang> matching the language bucket from step 1 (e.g. Dockerfile.python, Dockerfile.golang, Dockerfile.rust). The tag-suffix must match the uri: in the corresponding backend/index.yaml image entry exactly.

Path-filter registration — REQUIRED for any new dockerfile suffix. This is the single most common omission, because it has no effect on the PR that adds the backend (when no prior path filter could catch it anyway) — it only breaks the next PR that touches your backend's directory, which then gets zero CI jobs and looks broken for unrelated reasons. Edit scripts/lib/backend-filter.mjs:inferBackendPath and add a branch BEFORE the more-generic suffixes:

if (item.dockerfile.endsWith("<your-dockerfile-suffix>")) {
    return `backend/cpp/<your-backend>/`;   // or backend/python|go|rust/...
}

The endsWith() test is against the matrix entry's dockerfile: value (e.g. ./backend/Dockerfile.ds4 → endsWith("ds4")). Specificity order matters here just like it does for importers: more-specific suffixes go BEFORE more-generic ones (e.g. ds4 before llama-cpp even though both end with letters, because some upstream might one day call itself super-ds4-llama-cpp). Verify locally before pushing:

bash

# Confirm your dockerfile suffix is unique enough
node -e "
const yaml = require('js-yaml'); const fs = require('fs');
const m = yaml.load(fs.readFileSync('.github/backend-matrix.yml','utf8'));
for (const e of m.include.filter(e => e.backend === '<your-backend>')) {
  console.log(e.dockerfile, '->', e.dockerfile.endsWith('<suffix>'));
}"

A quick way to find the right insertion point: grep -n 'item.dockerfile.endsWith' scripts/lib/backend-filter.mjs.

If your backend consumes a shared build input that lives outside its own directory (a new script under scripts/build/, a new file copied into every image), add a rule to SHARED_BUILD_INPUTS in the same file — the per-backend prefix match cannot see those, and a miss ships your change to no image at all. See scripts/lib/backend-filter_test.mjs for the pattern; make test-ci-scripts runs it.

bump_deps.yaml registration — REQUIRED for any backend pinning an upstream commit. If your backend's Makefile has a *_VERSION?=<sha> pin to a third-party repo, the daily auto-bump bot at .github/workflows/bump_deps.yaml won't notice it unless you register the backend in its matrix. The bot runs .github/bump_deps.sh which greps for ^$VAR?= in the Makefile you list — so the pin MUST live in the Makefile (not in a separate shell script). The bump for ds4 (#9761) had to walk this back because the original landed the pin in prepare.sh, which the bot can't see. Pattern (for antirez/ds4):

yaml

# .github/workflows/bump_deps.yaml
matrix:
  include:
    - repository: "antirez/ds4"
      variable: "DS4_VERSION"
      branch: "main"
      file: "backend/cpp/ds4/Makefile"

And the corresponding Makefile shape (mirror backend/cpp/llama-cpp/Makefile):

makefile

DS4_VERSION?=ae302c2fa18cc6d9aefc021d0f27ae03c9ad2fc0
DS4_REPO?=https://github.com/antirez/ds4
...
ds4:
	mkdir -p ds4
	cd ds4 && git init -q && \
	git remote add origin $(DS4_REPO) && \
	git fetch --depth 1 origin $(DS4_VERSION) && \
	git checkout FETCH_HEAD

If you have a prepare.sh doing the clone, delete it — the recipe belongs in the Makefile target so make purge && make works as a clean-and-rebuild and so the bump bot finds the pin.

Placement in file:

CPU builds: Add after other CPU builds (e.g., after cpu-chatterbox)
CUDA 12 builds: Add after other CUDA 12 builds (e.g., after gpu-nvidia-cuda-12-chatterbox)
CUDA 13 builds: Add after other CUDA 13 builds (e.g., after gpu-nvidia-cuda-13-chatterbox)

Additional build types you may need:

ROCm/HIP: Use build-type: 'hipblas' with base-image: "rocm/dev-ubuntu-24.04:7.2.1"
Intel/SYCL: Use build-type: 'intel' or build-type: 'sycl_f16'/sycl_f32 with base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
L4T (ARM): Use build-type: 'l4t' with platforms: 'linux/arm64' and runs-on: 'ubuntu-24.04-arm'

Per-arch native builds (linux/amd64 + linux/arm64):

Multi-arch backends are NOT a single matrix entry with platforms: 'linux/amd64,linux/arm64'. Instead, add two entries — one with platforms: 'linux/amd64' + platform-tag: 'amd64' + runs-on: 'ubuntu-latest', one with platforms: 'linux/arm64' + platform-tag: 'arm64' + runs-on: 'ubuntu-24.04-arm' — both sharing the same tag-suffix. The script detects the shared tag-suffix and emits a merge-matrix entry, so backend-merge-jobs (in backend.yml/backend_pr.yml) automatically assembles the manifest list from per-arch digest artifacts. See -cpu-faster-whisper in .github/backend-matrix.yml for a reference shape.

llama-cpp / ik-llama-cpp / turboquant variants only — builder-base-image:

Entries whose dockerfile is ./backend/Dockerfile.{llama-cpp,ik-llama-cpp,turboquant} must also set a builder-base-image field pointing at a prebuilt base from quay.io/go-skynet/ci-cache:base-grpc-* (CI builds these via .github/workflows/base-images.yml). The mapping is by (build-type, platforms) — see existing entries for the pattern. CI uses these prebuilt bases to skip the gRPC compile (~25–35 min cold). Local make backends/<name> ignores builder-base-image and uses the from-source path inside the Dockerfile, so you don't need quay access for local builds.

Cover every OS the project supports (Linux and Darwin)

.github/backend-matrix.yml has two matrices, and they are the source of truth for which OS a backend ships on:

include: — the Linux matrix (x86_64 + arm64; CPU and CUDA / ROCm / SYCL / Vulkan).
includeDarwin: — the macOS / Apple Silicon matrix (arm64; Metal where the engine supports it, otherwise a native arm64 CPU build).

A new backend must target every OS it can build for — do not ship Linux-only by default. A backend that appears only under include: is silently unavailable on macOS even when its code would run there. Most C/C++/GGML engines build on Darwin out of the box (ggml defaults GGML_METAL=ON on Apple, so a plain build is Metal-enabled), and many Python backends do too (CPU / MPS wheels). If a backend genuinely cannot support an OS (e.g. CUDA-only, no CPU variant), state that in the PR description instead of omitting it silently.

Wiring a backend into includeDarwin: is more than the matrix entry:

includeDarwin: entry — tag-suffix: "-metal-darwin-arm64-<backend>", build-type: "metal", lang: "go" for go+ggml backends; omit build-type for the bespoke C++ ones (llama-cpp / ds4 / privacy-filter). Match an existing entry of the same shape.
backend/index.yaml — add metal: to the backend's capabilities map (main and -development) and concrete metal-<backend> / metal-<backend>-development image entries pointing at the -metal-darwin-arm64-<backend> images.
C/C++ backends only — add an inferBackendPathDarwin case in scripts/lib/backend-filter.mjs returning backend/cpp/<backend>/ (the generic fallthrough assumes backend/<lang>/, which is wrong for a C++ source tree driven with lang: go), and give run.sh a Darwin branch that exports DYLD_LIBRARY_PATH instead of LD_LIBRARY_PATH. If the build is bespoke (single grpc-server + dylib bundling), model it on scripts/build/ds4-darwin.sh and add a backends/<backend>-darwin make target plus a gated step in .github/workflows/backend_build_darwin.yml.
C++ proto gotcha — if the backend compiles the generated gRPC/protobuf in a separate CMake target (e.g. hw_grpc_proto), that target must link protobuf::libprotobuf + gRPC::grpc++ so the Homebrew include dirs propagate; otherwise macOS fails with google/protobuf/runtime_version.h not found (Linux hides this because apt headers sit in /usr/include).

The CI path filter only builds a backend on a PR when a file under its directory changes, so a darwin-only YAML edit builds nothing — touch a file under backend/<lang>/<backend>/ (a one-line comment is enough) in the same PR.

3. Add Backend Metadata to `backend/index.yaml`

Step 3a: Add Meta Definition

Add a YAML anchor definition in the ## metas section (around line 2-300). Look for similar backends to use as a template such as diffusers or chatterbox

Step 3b: Add Image Entries

Add image entries at the end of the file, following the pattern of similar backends such as diffusers or chatterbox. Include both latest (production) and master (development) tags.

Note on integrity: OCI backends installed from a gallery whose verification: block is set are verified against a keyless-cosign policy before extraction; tarball/HTTP backends use the optional sha256: field. New backends do not need any extra YAML — the gallery-level verification: block covers every entry. See .agents/backend-signing.md for the producer-side CI step.

4. Update the Makefile

The Makefile needs to be updated in several places to support building and testing the new backend:

Step 4a: Add to .NOTPARALLEL

Add backends/<backend-name> to the .NOTPARALLEL line (around line 2) to prevent parallel execution conflicts:

makefile

.NOTPARALLEL: ... backends/<backend-name>

Step 4b: Add to prepare-test-extra

Add the backend to the prepare-test-extra target to prepare it for testing. Use the path matching your language bucket (backend/python/, backend/go/, backend/rust/, …):

makefile

prepare-test-extra: protogen-python
	...
	$(MAKE) -C backend/<lang>/<backend-name>

For Rust backends the target is usually the crate build target itself (e.g. $(MAKE) -C backend/rust/<backend-name> <backend-name>-grpc) so the binary is in place before test runs.

Step 4c: Add to test-extra

Add the backend to the test-extra target to run its tests — applies to Go and Rust backends too, not only Python:

makefile

test-extra: prepare-test-extra
	...
	$(MAKE) -C backend/<lang>/<backend-name> test

Each backend's own Makefile should define a test target so this line works regardless of language. Integration tests that need large model downloads should be gated behind an env var (see backend/rust/kokoros/'s KOKOROS_MODEL_PATH pattern) so CI only runs unit tests.

Step 4d: Add Backend Definition

Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:

For Python backends with root context (like faster-whisper, coqui):

makefile

BACKEND_<BACKEND_NAME> = <backend-name>|python|.|false|true

For Python backends with ./backend context (like chatterbox, moonshine):

makefile

BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true

For Go backends:

makefile

BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true

For Rust backends:

makefile

BACKEND_<BACKEND_NAME> = <backend-name>|rust|.|false|true

The language field (python/golang/rust/…) must match a backend/Dockerfile.<lang> file.

Step 4e: Generate Docker Build Target

Add an eval call to generate the docker-build target (around line 480-501):

makefile

$(eval $(call generate-docker-build-target,$(BACKEND_<BACKEND_NAME>)))

Step 4f: Add to docker-build-backends

Add docker-build-<backend-name> to the docker-build-backends target (around line 507):

makefile

docker-build-backends: ... docker-build-<backend-name>

Determining the Context:

If the backend is in backend/python/<backend-name>/ and uses ./backend as context in the workflow file, use ./backend context
If the backend is in backend/python/<backend-name>/ but uses . as context in the workflow file, use . context
Check similar backends to determine the correct context

Engine preference for gallery model variants

A gallery entry can declare variants, alternative builds of the same weights, and LocalAI picks one per host: it drops builds whose backend cannot run here or that do not fit memory, then ranks the survivors by engine preference first, serving feature second, size third (SelectVariant in core/gallery/resolve_variant.go).

Ask whether your backend should outrank another one on some hardware. If it should, add it to engineNamePreferenceRules in pkg/system/capabilities.go, best engine first for that capability:

 	{Nvidia, []string{engineVLLM, engineSGLang, engineLlamaCpp}},
+	{Nvidia, []string{engineVLLM, engineSGLang, engineMyEngine, engineLlamaCpp}},

That is the ENGINE NAME table, matched as a substring of a gallery entry's backend: value. Two sibling tables in the same file speak different vocabularies and are matched against different things:

Table	Vocabulary	Matched against	Consumer
`backendBuildTagPreferenceRules`	build tags (`cuda`, `rocm`, `metal`)	installed build directory names, as a substring	alias resolution in `ListSystemBackends`
`engineNamePreferenceRules`	engine names (`vllm`, `llama-cpp`, `mlx`)	a gallery entry's `backend:`, as a substring	gallery variant ranking
`servingFeaturePreferenceTokens`	serving features (`dflash`, `mtp`)	a gallery entry's `tags:`, compared whole and case-insensitively, and nothing else	gallery variant ranking, one rank below the engine

Putting a token in the wrong table matches nothing and does not error: every candidate scores equal and the next sort key decides, so the preference silently stops existing. The block comment above all three tables spells the contract out.

The serving feature table is the odd one: it is not keyed by capability, because no hardware prefers a plain build over an equivalent faster build of the same weights. It reads a declared tag and nothing else. The entry name was the original signal and is gone: a naming convention is not a contract, and names are author-supplied free text where a short marker like mtp turns up inside unrelated words or on weights whose entry enables nothing. overrides.options was rejected for the mirror-image reason: spec_type: is llama.cpp's config vocabulary, whereas a cross-backend ranking decision must work the same for ds4's mtp_path: and sglang's speculative_algorithm:.

If your backend can serve the same weights faster (speculative decoding, multi-token prediction), say so in the docs for its gallery entries so curators tag them: the tagging rule and the per-backend evidence table live in adding-gallery-models.md. A backend never needs to appear in the token table itself; it ranks builds, not engines.

Leaving your backend out is a valid choice when no ordering can be justified for it. It then ranks below every known engine and selection falls back to size, which is the behaviour that predates preference.

Leaving a whole capability out is not. A missing row gives that host an empty preference list, so size alone decides among everything that survives the filters, and the filter will not save you: IsBackendCompatible derives hardware support from the engine NAME, so vllm and sglang carry no darwin, cuda, rocm or sycl token and are never dropped on a host with no GPU. That is why default (no usable accelerator, including a GPU under the 4 GiB VRAM floor) and darwin-x86 both have rows putting llama-cpp first. Every capability getSystemCapabilities() can return needs a row unless every engine really is equally at home there. When you add one, enumerate the engines you are demoting rather than relying on them falling through unmatched: unmatched engines all tie with each other, so size decides among them.

Documenting the backend (README + docs)

A backend is not "added" until it is discoverable. Update the user-facing docs:

docs/content/features/backends.md - add the backend to the right category in the "LocalAI supports various types of backends" list (and add a new category if it introduces a new modality, e.g. sound classification).
If the backend introduces a new API surface (a new endpoint or a realtime capability), document it under docs/content/ where its area lives (audio, vision, etc.) and follow the api-endpoints checklist in api-endpoints-and-auth.md.

If the backend is a native C/C++/GGML engine created and maintained by the LocalAI team (a from-scratch port like parakeet.cpp, ced.cpp, vibevoice.cpp, rf-detr.cpp, not a wrapper around a third-party runtime), it ALSO belongs in the top-level README.md table under "native C/C++/GGML engines ... developed and maintained by the LocalAI project itself". Add a row linking the upstream engine repo with a one-line description. This is the project's showcase of its own engines; a new in-house backend that is missing from it is a documentation bug.

5. Verification Checklist

After adding a new backend, verify:

Bundling runtime shared libraries (`package.sh`)

The final Dockerfile.python stage is FROM scratch — there is no system libc, no apt, no fallback library path. Only files explicitly copied from the builder stage end up in the backend image. That means any runtime dlopen your backend (or its Python deps) needs must be packaged into ${BACKEND}/lib/.

Pattern:

Make sure the library is installed in the builder stage of backend/Dockerfile.python (add it to the top-level apt-get install).
Drop a package.sh in your backend directory that copies the library — and its soname symlinks — into $(dirname $0)/lib. See backend/python/vllm/package.sh for a reference implementation that walks /usr/lib/x86_64-linux-gnu, /usr/lib/aarch64-linux-gnu, etc.
Dockerfile.python already runs package.sh automatically if it exists, after package-gpu-libs.sh.
libbackend.sh automatically prepends ${EDIR}/lib to LD_LIBRARY_PATH at run time, so anything packaged this way is found by dlopen.

How to find missing libs: when a Python module silently fails to register torch ops or you see AttributeError: '_OpNamespace' '...' object has no attribute '...', run the backend image's Python with LD_DEBUG=libs to see which dlopen failed. The filename in the error message (e.g. libnuma.so.1) is what you need to package.

To verify packaging works without trusting the host:

bash

make docker-build-<backend>
CID=$(docker create --entrypoint=/run.sh local-ai-backend:<backend>)
docker cp $CID:/lib /tmp/check && docker rm $CID
ls /tmp/check    # expect the bundled .so files + symlinks

Then boot it inside a fresh ubuntu:24.04 (which intentionally does not have the lib installed) to confirm it actually loads from the backend dir.

Importer integration

When you add a new backend, you MUST also make it importable via the model import form (/import-model). The import form dropdown is sourced dynamically from GET /backends/known — it reads the importer registry at core/gallery/importers/importers.go, so the steps below are the ONLY way to make your backend show up.

Required steps:

If your backend has unambiguous detection signals (unique file extension, HF pipeline_tag, unique repo name pattern, unique artefact like modules.json):
- Create an importer file at core/gallery/importers/<backend>.go following the Match/Import pattern in llama-cpp.go.
- Register it in importers.go:defaultImporters in specificity order — more specific detectors must appear BEFORE more generic ones (e.g. sentencetransformers before transformers, stablediffusion-ggml before llama-cpp, vllm-omni before vllm). First match wins.
If your backend is a drop-in replacement (same artefacts as another backend, e.g. ik-llama-cpp and turboquant both consume GGUF the same way llama-cpp does):
- Do NOT create a new importer. Extend the existing importer's Import() to swap the emitted backend: field when preferences.backend matches. See llama-cpp.go for the pattern.
If your backend has no reliable auto-detect signal (preference-only — e.g. sglang, tinygrad, whisperx):
- Do NOT create an importer. Instead add the backend name to the curated pref-only slice in core/http/endpoints/localai/backend.go that feeds /backends/known. A single line addition.
Always add a table-driven test in core/gallery/importers/importers_test.go (Ginkgo/Gomega):
- Use a real public HuggingFace repo URI as the test fixture (existing tests already hit the live HF API — follow that pattern).
- Cover detection (auto-match without preferences), preference-override (explicit backend: in preferences wins), and — if the backend's modality has a common pipeline_tag but ambiguous artefacts — an ambiguity test asserting errors.Is(err, importers.ErrAmbiguousImport).

Rules of thumb:

When in doubt, lean pref-only. A wrong auto-detect is worse than a forced preference.
Never silently emit a modality mismatch (e.g. emit llama-cpp for a TTS repo because .gguf is present). Return ErrAmbiguousImport instead.
Registration order is the single most common source of bugs. Check by running go test ./core/gallery/importers/... — the existing suite will fail if you've shadowed a pre-existing detector.

6. Example: Adding a Python Backend

For reference, when moonshine was added:

Files created: backend/python/moonshine/{backend.py, Makefile, install.sh, protogen.sh, requirements.txt, run.sh, test.py, test.sh}
Workflow entries: 3 build configurations (CPU, CUDA 12, CUDA 13)
Index entries: 1 meta definition + 6 image entries (cpu, cuda12, cuda13 x latest/development)
Makefile updates:
- Added to .NOTPARALLEL line
- Added to prepare-test-extra and test-extra targets
- Added BACKEND_MOONSHINE = moonshine|python|./backend|false|true
- Added eval for docker-build target generation
- Added docker-build-moonshine to docker-build-backends

Adding a New Backend

Adding a New Backend

1. Create Backend Directory Structure

2. Add Build Configurations to .github/backend-matrix.yml

Cover every OS the project supports (Linux and Darwin)

3. Add Backend Metadata to backend/index.yaml

4. Update the Makefile

Engine preference for gallery model variants

Documenting the backend (README + docs)

5. Verification Checklist

Bundling runtime shared libraries (package.sh)

Importer integration

6. Example: Adding a Python Backend

2. Add Build Configurations to `.github/backend-matrix.yml`

3. Add Backend Metadata to `backend/index.yaml`

Bundling runtime shared libraries (`package.sh`)