# What does this PR do?
Improved bedrock provider config to read from environment variables like
AWS_ACCESS_KEY_ID. Updated all
fields to use default_factory with lambda patterns like the nvidia
provider does.
Now the environment variables work as documented.
Closes#3305
## Test Plan
Ran the new bedrock config tests:
```bash
python -m pytest tests/unit/providers/inference/bedrock/test_config.py
-v
Verified existing provider tests still work:
python -m pytest tests/unit/providers/test_configs.py -v
# What does this PR do?
The inference store writes were moved to asyncio.create_task and not
await anymore
## Test Plan
❯ OLLAMA_URL=http://localhost:11434 LLAMA_STACK_CONFIG=server:starter uv
run --with pytest-repeat pytest tests/integration/inference
--text-model="ollama/llama3.2:3b-instruct-fp16" -vvs -k
"test_inference_store_tool_calls and 3b-instruct-fp16-True" --count=10
Uninstalled 2 packages in 102ms
Installed 2 packages in 138ms
INFO 2025-09-04 14:10:17,775 tests.integration.conftest:66 tests:
Setting DISABLE_CODE_SANDBOX=1 for macOS
==========================================================================================================
test session starts
===========================================================================================================
platform darwin -- Python 3.12.3, pytest-8.4.1, pluggy-1.6.0 --
/Users/erichuang/.cache/uv/builds-v0/.tmpSGMlgt/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.12.3', 'Platform':
'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.1',
'pluggy': '1.6.0'}, 'Plugins': {'repeat': '0.9.4', 'anyio': '4.9.0',
'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report':
'1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1',
'nbval': '0.11.0'}}
rootdir: /Users/erichuang/projects/llama-stack-git
configfile: pyproject.toml
plugins: repeat-0.9.4, anyio-4.9.0, html-4.1.1, socket-0.7.0,
asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1,
cov-6.2.1, nbval-0.11.0
asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None,
asyncio_default_test_loop_scope=function
collected 970 items / 950 deselected / 20 selected
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10]
instantiating llama_stack_client
Starting llama stack server with config 'starter' on port 8321...
Waiting for server at http://localhost:8321... (0.0s elapsed)
Waiting for server at http://localhost:8321... (0.5s elapsed)
Waiting for server at http://localhost:8321... (5.1s elapsed)
Waiting for server at http://localhost:8321... (5.6s elapsed)
Waiting for server at http://localhost:8321... (10.1s elapsed)
Waiting for server at http://localhost:8321... (10.6s elapsed)
Waiting for server at http://localhost:8321... (15.2s elapsed)
Waiting for server at http://localhost:8321... (15.7s elapsed)
Server is ready at http://localhost:8321
llama_stack_client instantiated in 20.583s
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10]
PASSED
tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10]
PASSEDTerminating llama stack server process...
Terminating process 53307 and its group...
Server process and children terminated gracefully
What does this PR do?
Fixes error handling when MCP server connections fail. Instead of
returning generic 500 errors, now provides
descriptive error messages with proper HTTP status codes.
Closes#3107
Test Plan
Before fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"detail": "Internal server error: An unexpected error
occurred."} (500)
After fix:
curl -X GET
"http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server"
Returns: {"error": {"detail": "Failed to connect to MCP server at
http://localhost:9999/sse: Connection
refused"}} (502)
Tests:
- Added unit test for ConnectionError → 502 translation
- Manually tested with unreachable MCP servers (connection refused)
One needed to specify record-replay related environment variables for
running integration tests. We could not use defaults because integration
tests could be run against Ollama instances which could be running
different models. For example, text vs vision tests needed separate
instances of Ollama because a single instance typically cannot serve
both of these models if you assume the standard CI worker configuration
on Github. As a result, `client.list()` as returned by the Ollama client
would be different between these runs and we'd end up overwriting
responses.
This PR "solves" it by adding a small amount of complexity -- we store
model list responses specially, keyed by the hashes of the models they
return. At replay time, we merge all of them and pretend that we have
the union of all models available.
## Test Plan
Re-recorded all the tests using `scripts/integration-tests.sh
--inference-mode record`, including the vision tests.
# What does this PR do?
add the ability to use inequalities in the where clause of the sqlstore.
this is infrastructure for files expiration.
## Test Plan
unit tests
# What does this PR do?
During env var replacement, we're implicitly converting all config types
to their apparent types (e.g., "true" to True, "123" to 123). This may
be arguably useful for when doing an env var substitution, as those are
always strings, but we should definitely avoid touching config values
that have explicit types and are uninvolved in env var substitution.
## Test Plan
Unit
Recording files use a predictable naming format, making the SQLite index
redundant. The binary SQLite file was causing frequent git conflicts.
Simplify by calculating file paths directly from request hashes.
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
As described in #3134 a langchain example works against openai's
responses impl, but not against llama stack's. This turned out to be due
to the order of the inputs. The langchain example has the two function
call outputs first, followed by each call result in turn. This seems to
be valid as it is accepted by openai's impl. However in llama stack,
these inputs are converted to chat completion inputs and the resulting
order for that api is not accpeted by openai.
This PR fixes the issue by ensuring that the converted chat completions
inputs are in the expected order.
Closes#3134
## Test Plan
Added unit and integration tests. Verified this fixes original issue as
reported.
---------
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
Currently the embedding integration test cases fail due to a
misalignment in the error type. This PR fixes the embedding integration
test by fixing the error type.
## Test Plan
```
pytest -s -v tests/integration/inference/test_embedding.py --stack-config="inference=nvidia" --embedding-model="nvidia/llama-3.2-nv-embedqa-1b-v2" --env NVIDIA_API_KEY={nvidia_api_key} --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com"
```
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR removes `init()` from `LlamaStackAsLibrary`
Currently client.initialize() had to be invoked by user.
To improve dev experience and to avoid runtime errors, this PR init
LlamaStackAsLibrary implicitly upon using the client.
It prevents also multiple init of the same client, while maintaining
backward ccompatibility.
This PR does the following
- Automatic Initialization: Constructor calls initialize_impl()
automatically.
- Client is fully initialized after __init__ completes.
- Prevents consecutive initialization after the client has been
successfully initialized.
- initialize() method still exists but is now a no-op.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
fixes https://github.com/meta-llama/llama-stack/issues/2946
---------
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
Adds flexible CORS (Cross-Origin Resource Sharing) configuration support
to the FastAPI
server with both local development and explicit configuration modes:
- **Local development mode**: `cors: true` enables localhost-only access
with regex
pattern `https?://localhost:\d+`
- **Explicit configuration mode**: Specific origins configuration with
credential support
and validation
- Prevents insecure combinations (wildcards with credentials)
- FastAPI CORSMiddleware integration via `model_dump()`
Addresses the need for configurable CORS policies to support web
frontends and
cross-origin API access while maintaining security.
Closes#2119
## Test Plan
1. Ran Unit Tests.
2. Manual tests: FastAPI middleware integration with actual HTTP
requests
- Local development mode localhost access validation
- Explicit configuration mode origins validation
- Preflight OPTIONS request handling
Some screenshots of manual tests.
<img width="1920" height="927" alt="image"
src="https://github.com/user-attachments/assets/79322338-40c7-45c9-a9ea-e3e8d8e2f849"
/>
<img width="1911" height="1037" alt="image"
src="https://github.com/user-attachments/assets/1683524e-b0c9-48c9-a0a5-782e949cde01"
/>
cc: @leseb @rhuss @franciscojavierarceo
# What does this PR do?
Handles MCP tool calls in a previous response
Closes#3105
## Test Plan
Made call to create response with tool call, then made second call with
the first linked through previous_response_id. Did not get error.
Also added unit test.
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
This PR adds a step in pre-commit to enforce using `llama_stack` logger.
Currently, various parts of the code base uses different loggers. As a
custom `llama_stack` logger exist and used in the codebase, it is better
to standardize its utilization.
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
I started this PR trying to unbreak a newly broken test
`test_agent_name`. This test was broken all along but did not show up
because during testing we were pulling the "non-updated" llama stack
client. See this comment:
https://github.com/llamastack/llama-stack/pull/3119#discussion_r2270988205
While fixing this, I encountered a large amount of badness in our CI
workflow definitions.
- We weren't passing `LLAMA_STACK_DIR` or `LLAMA_STACK_CLIENT_DIR`
overrides to `llama stack build` at all in some cases.
- Even when we did, we used `uv run` liberally. The first thing `uv run`
does is "syncs" the project environment. This means, it is going to undo
any mutations we might have done ourselves. But we make many mutations
in our CI runners to these environments. The most important of which is
why `llama stack build` where we install distro dependencies. As a
result, when you tried to run the integration tests, you would see old,
strange versions.
## Test Plan
Re-record using:
```
sh scripts/integration-tests.sh --stack-config ci-tests \
--provider ollama --test-pattern test_agent_name --inference-mode record
```
Then re-run with `--inference-mode replay`. But:
Eventually, this test turned out to be quite flaky for telemetry
reasons. I haven't investigated it for now and just disabled it sadly
since we have a release to push out.
# What does this PR do?
Add CodeScanner implementations
## Test Plan
`SAFETY_MODEL=CodeScanner LLAMA_STACK_CONFIG=starter uv run pytest -v
tests/integration/safety/test_safety.py
--text-model=llama3.2:3b-instruct-fp16
--embedding-model=all-MiniLM-L6-v2 --safety-shield=ollama`
This PR need to land after this
https://github.com/meta-llama/llama-stack/pull/3098
See comment here:
https://github.com/llamastack/llama-stack/pull/3162#issuecomment-3192859097
-- TL;DR it is quite complex to invoke the recording workflow correctly
for an end developer writing tests. This script simplifies the work.
No more manual GitHub UI navigation!
## Script Functionality
- Auto-detects your current branch and associated PR
- Finds the right repository context (works from forks!)
- Runs the workflow where it can actually commit back
- Validates prerequisites and provides helpful error messages
## How to Use
First ensure you are on the branch which introduced a new test and want
it recorded. **Make sure you have pushed this branch remotely, easiest
is to create a PR.**
```
# Record tests for current branch
./scripts/github/schedule-record-workflow.sh
# Record specific test subdirectories
./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference"
# Record with vision tests enabled
./scripts/github/schedule-record-workflow.sh --run-vision-tests
# Record tests matching a pattern
./scripts/github/schedule-record-workflow.sh --test-pattern "test_streaming"
```
## Test Plan
Ran `./scripts/github/schedule-record-workflow.sh -s inference -k
tool_choice` which started
4820409329
which successfully committed recorded outputs.
# What does this PR do?
Recording tests has become a nightmare. This is the first part of making
that process simpler by making it _less_ automatic. I tried to be too
clever earlier.
It simplifies the record-integration-tests workflow to use workflow
dispatch inputs instead of PR labels. No more opaque stuff. Just go to
the GitHub UI and run the workflow with inputs. I will soon add a helper
script for this also.
Other things to aid re-running just the small set of things you need to
re-record:
- Replaces the `test-types` JSON array parameter with a more intuitive
`test-subdirs` comma-separated list. The whole JSON array crap was for
matrix.
- Adds a new `test-pattern` parameter to allow filtering tests using
pytest's `-k` option
## Test Plan
Note that this PR is in a fork not the source repository.
- Replay tests on this PR are green
- Manually
[ran](1699856292)
the replay workflow with a test-subdir and test-pattern filter, worked
- Manually
[ran](4819508034)
the **record** workflow with a simple pattern, it has worked and updated
_this_ PR.
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
Creates a structured testing documentation section with multiple detailed pages:
- Testing overview explaining the record-replay architecture
- Integration testing guide with practical usage examples
- Record-replay system technical documentation
- Guide for writing effective tests
- Troubleshooting guide for common testing issues
Hopefully this makes things a bit easier.
# What does this PR do?
Updates test recordings.
## Test Plan
Started ollama serving the 3.2:3b model. Then ran the server:
```
LLAMA_STACK_TEST_INFERENCE_MODE=record \
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings/ \
SQLITE_STORE_DIR=$(mktemp -d) \
OLLAMA_URL=http://localhost:11434 \
llama stack build --template starter --image-type venv --run
```
Then ran the tests which needed recording:
```
pytest -sv tests/integration/agents/test_openai_responses.py \
--stack-config=server:starter \
--text-model ollama/llama3.2:3b-instruct-fp16 -k test_responses_store
```
Then, restarted the server with `LLAMA_STACK_TEST_INFERENCE_MODE=replay`, re-ran the tests and verified they passed.
# What does this PR do?
A _bunch_ on cleanup for the Responses tests.
- Got rid of YAML test cases, moved them to just use simple pydantic models
- Splitting the large monolithic test file into multiple focused test files:
- `test_basic_responses.py` for basic and image response tests
- `test_tool_responses.py` for tool-related tests
- `test_file_search.py` for file search specific tests
- Adding a `StreamingValidator` helper class to standardize streaming response validation
## Test Plan
Run the tests:
```
pytest -s -v tests/integration/non_ci/responses/ \
--stack-config=starter \
--text-model openai/gpt-4o \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2 \
-k "client_with_models"
```
# What does this PR do?
Adds proper streaming events for MCP tool listing (`mcp_list_tools.in_progress` and `mcp_list_tools.completed`). Also refactors things a bit more.
## Test Plan
Verified existing integration tests pass with the refactored code. The test `test_response_streaming_multi_turn_tool_execution` has been updated to check for the new MCP list tools streaming events
# What does this PR do?
Refactors the OpenAI response conversion utilities by moving helper functions from `openai_responses.py` to `utils.py`. Adds unit tests.
# What does this PR do?
Refactors the OpenAI responses implementation by extracting streaming and tool execution logic into separate modules. This improves code organization by:
1. Creating a new `StreamingResponseOrchestrator` class in `streaming.py` to handle the streaming response generation logic
2. Moving tool execution functionality to a dedicated `ToolExecutor` class in `tool_executor.py`
## Test Plan
Existing tests
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 does this PR do?
Adds content part streaming events to the OpenAI-compatible Responses API to support more granular streaming of response content. This introduces:
1. New schema types for content parts: `OpenAIResponseContentPart` with variants for text output and refusals
2. New streaming event types:
- `OpenAIResponseObjectStreamResponseContentPartAdded` for when content parts begin
- `OpenAIResponseObjectStreamResponseContentPartDone` for when content parts complete
3. Implementation in the reference provider to emit these events during streaming responses. Also emits MCP arguments just like function call ones.
## Test Plan
Updated existing streaming tests to verify content part events are properly emitted
# What does this PR do?
Enhances tool execution streaming by adding support for real-time progress events during tool calls. This implementation adds streaming events for MCP and web search tools, including in-progress, searching, completed, and failed states.
The refactored `_execute_tool_call` method now returns an async iterator that yields streaming events throughout the tool execution lifecycle.
## Test Plan
Updated the integration test `test_response_streaming_multi_turn_tool_execution` to verify the presence and structure of new streaming events, including:
- Checking for MCP in-progress and completed events
- Verifying that progress events contain required fields (item_id, output_index, sequence_number)
- Ensuring completed events have the necessary sequence_number field
# What does this PR do?
1. Updates `AgentPersistence.list_sessions()` to properly filter out
`Turn` keys from `Session` keys.
2. Adds a suite of unit tests to confirm the `list_sessions()` behavior
and tests the failed sample in
https://github.com/meta-llama/llama-stack/issues/3048
## Fixes https://github.com/meta-llama/llama-stack/issues/3048
## Test Plan
Unit tests added.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Some fixes to MCP tests. And a bunch of fixes for Vector providers.
I also enabled a bunch of Vector IO tests to be used with
`LlamaStackLibraryClient`
## Test Plan
Run Responses tests with llama stack library client:
```
pytest -s -v tests/integration/non_ci/responses/ --stack-config=server:starter \
--text-model openai/gpt-4o \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2 \
-k "client_with_models"
```
Do the same with `-k openai_client`
The rest should be taken care of by CI.
# What does this PR do?
the minimum python version for the project was bumped to 3.12 a couple
months ago, but there remains some artifacts in the repo suggesting we
support >=3.10
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
- Add new Vertex AI remote inference provider with litellm integration
- Support for Gemini models through Google Cloud Vertex AI platform
- Uses Google Cloud Application Default Credentials (ADC) for
authentication
- Added VertexAI models: gemini-2.5-flash, gemini-2.5-pro,
gemini-2.0-flash.
- Updated provider registry to include vertexai provider
- Updated starter template to support Vertex AI configuration
- Added comprehensive documentation and sample configuration
<!-- If resolving an issue, uncomment and update the line below -->
relates to https://github.com/meta-llama/llama-stack/issues/2747
## 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.* -->
Signed-off-by: Eran Cohen <eranco@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
This PR kills the verifications infrastructure which is no longer used.
It was relocated to the `llama-stack-evals`
(https://github.com/meta-llama/llama-stack-evals) repository previously.
Responses tests used this infrastructure but that wasn't quite
necessary, just a little useful back when @bbrownin introduced the
tests. On Discord, we agreed that tests can be moved to our regular
integrations test infra.
## Test Plan
Some tests currently do fail (although they run!) I will send a
follow-up PR which makes them all pass.
# What does this PR do?
This PR implements hybrid search for Milvus DB based on the inbuilt
milvus support.
To test:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s
--tb=long --disable-warnings --asyncio-mode=auto
```
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
This PR adds Open AI Compatible moderations api. Currently only
implementing for llama guard safety provider
Image support, expand to other safety providers and Deprecation of
run_shield will be next steps.
## Test Plan
Added 2 new tests for safe/ unsafe text prompt examples for the new open
ai compatible moderations api usage
`SAFETY_MODEL=llama-guard3:8b LLAMA_STACK_CONFIG=starter uv run pytest
-v tests/integration/safety/test_safety.py
--text-model=llama3.2:3b-instruct-fp16
--embedding-model=all-MiniLM-L6-v2 --safety-shield=ollama`
(Had some issue with previous PR
https://github.com/meta-llama/llama-stack/pull/2994 while updating and
accidentally close it , reopened new one )
