- Allows use of unavailable models on startup
- Add has_model method to ModelsRoutingTable for checking pre-registered
models
- Update check_model_availability to check model_store before provider
APIs
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Start llama stack and point unavailable vLLM
```
VLLM_URL=https://my-unavailable-vllm/v1 MILVUS_DB_PATH=./milvus.db INFERENCE_MODEL=vllm uv run --with llama-stack llama stack build --distro starter --image-type venv --run
```
llama stack will start without crashing but only notifying error.
```
- provider_id: rag-runtime
toolgroup_id: builtin::rag
vector_dbs: []
version: 2
INFO 2025-10-07 06:40:41,804 llama_stack.providers.utils.inference.inference_store:74 inference: Write queue disabled for SQLite to avoid concurrency issues
INFO 2025-10-07 06:40:42,066 llama_stack.providers.utils.responses.responses_store:96 openai_responses: Write queue disabled for SQLite to avoid concurrency issues
ERROR 2025-10-07 06:40:58,882 llama_stack.providers.utils.inference.openai_mixin:436 providers::utils: VLLMInferenceAdapter.list_provider_model_ids() failed with: Request timed out.
WARNING 2025-10-07 06:40:58,883 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider vllm: Request timed out.
[...]
INFO 2025-10-07 06:40:59,036 uvicorn.error:216 uncategorized: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
INFO 2025-10-07 06:41:04,064 openai._base_client:1618 uncategorized: Retrying request to /models in 0.398814 seconds
INFO 2025-10-07 06:41:09,497 openai._base_client:1618 uncategorized: Retrying request to /models in 0.781908 seconds
ERROR 2025-10-07 06:41:15,282 llama_stack.providers.utils.inference.openai_mixin:436 providers::utils: VLLMInferenceAdapter.list_provider_model_ids() failed with: Request timed out.
WARNING 2025-10-07 06:41:15,283 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider vllm: Request timed out.
```
# What does this PR do?
inference adapters can now configure `refresh_models: bool` to control
periodic model listing from their providers
BREAKING CHANGE: together inference adapter default changed. previously
always refreshed, now follows config.
addresses "models: refresh" on #3517
## Test Plan
ci w/ new tests
# What does this PR do?
- implement get_api_key instead of relying on
LiteLLMOpenAIMixin.get_api_key
- remove use of LiteLLMOpenAIMixin
- add default initialize/shutdown methods to OpenAIMixin
- remove __init__s to allow proper pydantic construction
- remove dead code from vllm adapter and associated / duplicate unit
tests
- update vllm adapter to use openaimixin for model registration
- remove ModelRegistryHelper from fireworks & together adapters
- remove Inference from nvidia adapter
- complete type hints on embedding_model_metadata
- allow extra fields on OpenAIMixin, for model_store, __provider_id__,
etc
- new recordings for ollama
- enhance the list models error handling
- update cerebras (remove cerebras-cloud-sdk) and anthropic (custom
model listing) inference adapters
- parametrized test_inference_client_caching
- remove cerebras, databricks, fireworks, together from blanket mypy
exclude
- removed unnecessary litellm deps
## Test Plan
ci
# What does this PR do?
add ModelsProtocolPrivate methods to OpenAIMixin
this will allow providers using OpenAIMixin to use a common interface
## Test Plan
ci w/ new tests
This is a sweeping change to clean up some gunk around our "Tool"
definitions.
First, we had two types `Tool` and `ToolDef`. The first of these was a
"Resource" type for the registry but we had stopped registering tools
inside the Registry long back (and only registered ToolGroups.) The
latter was for specifying tools for the Agents API. This PR removes the
former and adds an optional `toolgroup_id` field to the latter.
Secondly, as pointed out by @bbrowning in
https://github.com/llamastack/llama-stack/pull/3003#issuecomment-3245270132,
we were doing a lossy conversion from a full JSON schema from the MCP
tool specification into our ToolDefinition to send it to the model.
There is no necessity to do this -- we ourselves aren't doing any
execution at all but merely passing it to the chat completions API which
supports this. By doing this (and by doing it poorly), we encountered
limitations like not supporting array items, or not resolving $refs,
etc.
To fix this, we replaced the `parameters` field by `{ input_schema,
output_schema }` which can be full blown JSON schemas.
Finally, there were some types in our llama-related chat format
conversion which needed some cleanup. We are taking this opportunity to
clean those up.
This PR is a substantial breaking change to the API. However, given our
window for introducing breaking changes, this suits us just fine. I will
be landing a concurrent `llama-stack-client` change as well since API
shapes are changing.
# What does this PR do?
the LiteLLMOpenAIMixin provides support for reading key from provider
data (headers users send).
this adds the same functionality to the OpenAIMixin.
this is infrastructure for migrating providers.
## Test Plan
ci w/ new tests
# What does this PR do?
simplify Ollama inference adapter by -
- moving image_url download code to OpenAIMixin
- being a ModelRegistryHelper instead of having one (mypy blocks
check_model_availability method assignment)
## Test Plan
- add unit tests for new download feature
- add integration tests for openai_chat_completion w/ image_url (close
test gap)
# What does this PR do?
- remove auto-download of ollama embedding models
- add embedding model metadata to dynamic listing w/ unit test
- add support and tests for allowed_models
- removed inference provider models.py files where dynamic listing is
enabled
- store embedding metadata in embedding_model_metadata field on
inference providers
- make model_entries optional on ModelRegistryHelper and
LiteLLMOpenAIMixin
- make OpenAIMixin a ModelRegistryHelper
- skip base64 embedding test for remote::ollama, always returns floats
- only use OpenAI client for ollama model listing
- remove unused build_model_entry function
- remove unused get_huggingface_repo function
## Test Plan
ci w/ new tests
# What does this PR do?
this replaces the static model listing for any provider using
OpenAIMixin
currently -
- anthropic
- azure openai
- gemini
- groq
- llama-api
- nvidia
- openai
- sambanova
- tgi
- vertexai
- vllm
- not changed: together has its own impl
## Test Plan
- new unit tests
- manual for llama-api, openai, groq, gemini
```
for provider in llama-openai-compat openai groq gemini; do
uv run llama stack build --image-type venv --providers inference=remote::provider --run &
uv run --with llama-stack-client llama-stack-client models list | grep Total
```
results (17 sep 2025):
- llama-api: 4
- openai: 86
- groq: 21
- gemini: 66
closes#3467
The OpenAI compatibility layer was incorrectly importing
ChatCompletionMessageToolCallParam instead of the
ChatCompletionMessageFunctionToolCall class. This caused "Cannot
instantiate typing.Union" errors when processing agent requests with
tool calls.
Closes: #3141
Signed-off-by: Derek Higgins <derekh@redhat.com>
**What:**
- Added OpenAIChatCompletionTextOnlyMessageContent type for text-only
content validation
- Modified OpenAISystemMessageParam, OpenAIAssistantMessageParam,
OpenAIDeveloperMessageParam, and OpenAIToolMessageParam to use text-only
content type instead of mixed content
- OpenAIUserMessageParam unchanged - still accepts both text and images
- Updated OpenAPI spec files to reflect text-only content restrictions
in schemas
closes#2894
**Why:**
- Enforces OpenAI API compatibility by restricting image content to user
messages only
- Prevents API misuse where images might be sent in message types that
don't support them
- Aligns with OpenAI's actual API behavior where only user messages can
contain multimodal content
- Improves type safety and validation at the API boundary
**Test plan:**
- Added comprehensive parametrized tests covering all 5 OpenAI message
types
- Tests verify text string acceptance for all message types
- Tests verify text list acceptance for all message types
- Tests verify image rejection for system/assistant/developer/tool
messages (ValidationError expected)
- Tests verify user messages still accept images (backward compatibility
maintained)
# What does this PR do?
previously, developers who ran `./scripts/unit-tests.sh` would get
`asyncio-mode=auto`, which meant `@pytest.mark.asyncio` and
`@pytest_asyncio.fixture` were redundent. developers who ran `pytest`
directly would get pytest's default (strict mode), would run into errors
leading them to add `@pytest.mark.asyncio` / `@pytest_asyncio.fixture`
to their code.
with this change -
- `asyncio_mode=auto` is included in `pyproject.toml` making behavior
consistent for all invocations of pytest
- removes all redundant `@pytest_asyncio.fixture` and
`@pytest.mark.asyncio`
- for good measure, requires `pytest>=8.4` and `pytest-asyncio>=1.0`
## Test Plan
- `./scripts/unit-tests.sh`
- `uv run pytest tests/unit`
# What does this PR do?
* Given that our API packages use "import *" in `__init.py__` we don't
need to do `from llama_stack.apis.models.models` but simply from
llama_stack.apis.models. The decision to use `import *` is debatable and
should probably be revisited at one point.
* Remove unneeded Ruff F401 rule
* Consolidate Ruff F403 rule in the pyprojectfrom
llama_stack.apis.models.models
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
When converting OpenAI message content for the "system" and "assistant"
roles to Llama Stack inference APIs (used for some providers when
dealing with Llama models via OpenAI API requests to get proper prompt /
tool handling), we were not properly converting any non-string content.
I discovered this while running the new Responses AI verification suite
against the Fireworks provider, but instead of fixing it as part of some
ongoing work there split this out into a separate PR.
This fixes that, by using the `openai_content_to_content` helper we used
elsewhere to ensure content parts were mapped properly.
## Test Plan
I added a couple of new tests to `test_openai_compat` to reproduce this
issue and validate its fix. I ran those as below:
```
python -m pytest -s -v tests/unit/providers/utils/inference/test_openai_compat.py
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
When the result of a ToolCall gets passed back into vLLM for the model
to handle the tool call result (as is often the case in agentic
tool-calling workflows), we forgot to handle the case where BuiltinTool
calls are not string values but instead instances of the BuiltinTool
enum. This fixes that, properly converting those enums to string values
before trying to serialize them into an OpenAI chat completion request
to vLLM.
PR #1931 fixed a bug where we weren't passing these tool calling results
back into vLLM, but as a side-effect it created this serialization bug
when using BuiltinTools.
Closes#2070
## Test Plan
I added a new unit test to the openai_compat unit tests to cover this
scenario, ensured the new test failed before this fix, and all the
existing tests there plus the new one passed with this fix.
```
python -m pytest -s -v tests/unit/providers/utils/inference/test_openai_compat.py
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Include the tool call details with the chat when doing Rag with Remote
vllm
Fixes: #1929
With this PR the tool call is included in the chat returned to vllm, the
model (meta-llama/Llama-3.1-8B-Instruct) the returns the answer as
expected.
Signed-off-by: Derek Higgins <derekh@redhat.com>
