# 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:

```js
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 `grep`s 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:

1. **`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.
2. **`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.
3. **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`.
4. **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](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:

```go
 	{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](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](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:

- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/backend-matrix.yml` for all desired platforms (per-arch entries with `platform-tag` for multi-arch; `builder-base-image` for llama-cpp / ik-llama-cpp / turboquant)
- [ ] **OS coverage considered**: added to `includeDarwin:` (macOS/Apple Silicon) if the backend can build there — with the `backend/index.yaml` `metal:` capability + `metal-<backend>` image entries, a `run.sh` Darwin/DYLD branch and `inferBackendPathDarwin` case (in `scripts/lib/backend-filter.mjs`) for C++ backends — or the PR explains why an OS is unsupported. Do not ship Linux-only by default.
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
- [ ] Makefile updated with all 6 required changes (`.NOTPARALLEL`, `prepare-test-extra`, `test-extra`, backend definition, docker-build target eval, `docker-build-backends`)
- [ ] No YAML syntax errors (check with linter)
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
- [ ] **`Load` validates its input and refuses models it can't serve.** When a model config has no explicit `backend:`, the model loader greedily probes *every* installed backend with the model's name and binds to the first `Load` that succeeds — an accept-anything `Load` will capture arbitrary LLMs (issue #9287). Backends that load a real artefact get this for free (the load fails); backends with no artefact must gate on the name: `opus` accepts only its own name (or none), `local-store` requires the `store.NamespacePrefix` namespace marker sent by `core/backend/stores.go`.
- [ ] **Gallery variant ranking considered**: if this backend should be preferred over another on some hardware, it is listed in `engineNamePreferenceRules` (NOT `backendBuildTagPreferenceRules`, NOT `servingFeaturePreferenceTokens`) in `pkg/system/capabilities.go`. A missing entry silently ranks it last and lets the next sort key decide.
- [ ] Documented: added to the category list in `docs/content/features/backends.md` (and any new endpoint/realtime capability documented under `docs/content/`)
- [ ] If it is an in-house native C/C++/GGML engine, added to the maintained-engines table in the top-level `README.md`

## 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:

1. Make sure the library is installed in the builder stage of `backend/Dockerfile.python` (add it to the top-level `apt-get install`).
2. Drop a `package.sh` in your backend directory that copies the library — and its soname symlinks — into `$(dirname $0)/lib`. See `backend/python/vllm/package.sh` for a reference implementation that walks `/usr/lib/x86_64-linux-gnu`, `/usr/lib/aarch64-linux-gnu`, etc.
3. `Dockerfile.python` already runs `package.sh` automatically if it exists, after `package-gpu-libs.sh`.
4. `libbackend.sh` automatically prepends `${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:

1. **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.
2. **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.
3. **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.
4. **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`
