LocalAI/.agents/adding-backends.md

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>/

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

2. Add Build Configurations to .github/workflows/backend.yml

Add build matrix entries for each platform/GPU type you want to support. Look at similar backends (e.g., chatterbox, faster-whisper) for reference.

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:2025.3.0-0-devel-ubuntu24.04"
• L4T (ARM): Use build-type: 'l4t' with platforms: 'linux/arm64' and runs-on: 'ubuntu-24.04-arm'

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.

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:

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

Step 4b: Add to prepare-test-extra

Add the backend to the prepare-test-extra target (around line 312) to prepare it for testing:

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

Step 4c: Add to test-extra

Add the backend to the test-extra target (around line 319) to run its tests:

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

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):

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

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

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

For Go backends:

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

Step 4e: Generate Docker Build Target

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

$(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):

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

5. Verification Checklist

After adding a new backend, verify:

• Backend directory structure is complete with all necessary files
• Build configurations added to .github/workflows/backend.yml for all desired platforms
• 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)

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:

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.

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