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# What does this PR do?
This replaces the legacy "pyopenapi + strong_typing" pipeline with a
FastAPI-backed generator that has an explicit schema registry inside
`llama_stack_api`. The key changes:
1. **New generator architecture.** FastAPI now builds the OpenAPI schema
directly from the real routes, while helper modules
(`schema_collection`, `endpoints`, `schema_transforms`, etc.)
post-process the result. The old pyopenapi stack and its strong_typing
helpers are removed entirely, so we no longer rely on fragile AST
analysis or top-level import side effects.
2. **Schema registry in `llama_stack_api`.** `schema_utils.py` keeps a
`SchemaInfo` record for every `@json_schema_type`, `register_schema`,
and dynamically created request model. The OpenAPI generator and other
tooling query this registry instead of scanning the package tree,
producing deterministic names (e.g., `{MethodName}Request`), capturing
all optional/nullable fields, and making schema discovery testable. A
new unit test covers the registry behavior.
3. **Regenerated specs + CI alignment.** All docs/Stainless specs are
regenerated from the new pipeline, so optional/nullable fields now match
reality (expect the API Conformance workflow to report breaking
changes—this PR establishes the new baseline). The workflow itself is
back to the stock oasdiff invocation so future regressions surface
normally.
*Conformance will be RED on this PR; we choose to accept the
deviations.*
## Test Plan
- `uv run pytest tests/unit/server/test_schema_registry.py`
- `uv run python -m scripts.openapi_generator.main docs/static`
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
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|---|---|---|
| .. | ||
| common | ||
| strong_typing | ||
| __init__.py | ||
| agents.py | ||
| batches.py | ||
| benchmarks.py | ||
| conversations.py | ||
| datasetio.py | ||
| datasets.py | ||
| datatypes.py | ||
| eval.py | ||
| files.py | ||
| inference.py | ||
| inspect.py | ||
| models.py | ||
| openai_responses.py | ||
| post_training.py | ||
| prompts.py | ||
| providers.py | ||
| py.typed | ||
| pyproject.toml | ||
| rag_tool.py | ||
| README.md | ||
| resource.py | ||
| safety.py | ||
| schema_utils.py | ||
| scoring.py | ||
| scoring_functions.py | ||
| shields.py | ||
| tools.py | ||
| uv.lock | ||
| vector_io.py | ||
| vector_stores.py | ||
| version.py | ||
llama-stack-api
API and Provider specifications for Llama Stack - a lightweight package with protocol definitions and provider specs.
Overview
llama-stack-api is a minimal dependency package that contains:
- API Protocol Definitions: Type-safe protocol definitions for all Llama Stack APIs (inference, agents, safety, etc.)
- Provider Specifications: Provider spec definitions for building custom providers
- Data Types: Shared data types and models used across the Llama Stack ecosystem
- Type Utilities: Strong typing utilities and schema validation
What This Package Does NOT Include
- Server implementation (see
llama-stackpackage) - Provider implementations (see
llama-stackpackage) - CLI tools (see
llama-stackpackage) - Runtime orchestration (see
llama-stackpackage)
Use Cases
This package is designed for:
- Third-party Provider Developers: Build custom providers without depending on the full Llama Stack server
- Client Library Authors: Use type definitions without server dependencies
- Documentation Generation: Generate API docs from protocol definitions
- Type Checking: Validate implementations against the official specs
Installation
pip install llama-stack-api
Or with uv:
uv pip install llama-stack-api
Dependencies
Minimal dependencies:
pydantic>=2.11.9- For data validation and serializationjsonschema- For JSON schema utilities
Versioning
This package follows semantic versioning independently from the main llama-stack package:
- Patch versions (0.1.x): Documentation, internal improvements
- Minor versions (0.x.0): New APIs, backward-compatible changes
- Major versions (x.0.0): Breaking changes to existing APIs
Current version: 0.4.0.dev0
Usage Example
from llama_stack_api.inference import Inference, ChatCompletionRequest
from llama_stack_api.providers.datatypes import ProviderSpec, InlineProviderSpec
from llama_stack_api.datatypes import Api
# Use protocol definitions for type checking
class MyInferenceProvider(Inference):
async def chat_completion(self, request: ChatCompletionRequest):
# Your implementation
pass
# Define provider specifications
my_provider_spec = InlineProviderSpec(
api=Api.inference,
provider_type="inline::my-provider",
pip_packages=["my-dependencies"],
module="my_package.providers.inference",
config_class="my_package.providers.inference.MyConfig",
)
Relationship to llama-stack
The main llama-stack package depends on llama-stack-api and provides:
- Full server implementation
- Built-in provider implementations
- CLI tools for running and managing stacks
- Runtime provider resolution and orchestration
Contributing
See the main Llama Stack repository for contribution guidelines.
License
MIT License - see LICENSE file for details.