Since we will save MCP resource contents to the artifact service, no need to do post-processing based on mime type. Also fixed the implementation; the correct method to call is session.read_resource(uri).
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 862885513
This allows users to access MCP resources on their own within agent logic / using custom tools. I plan on also later adding it to the agent state.
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 861910520
* The main fix is in eval_metrics.py
* Removed some deprecated paths in the FastAPI server
* Added a unit test to catch future breakages
* Bump fastapi version to be 0.124.1 to capture the fix in https://github.com/fastapi/fastapi/pull/14482
* Removed the upper-bound restriction on fastapi version which was used to temporarily fix the issue
Fixes#3173
Co-authored-by: Liang Wu <wuliang@google.com>
PiperOrigin-RevId: 861867708
This changes the behavior of how we handle stale session, instead of reject the transaction entirely, we aggresively refresh the session.
Removed synchronous inspect(self).session calls within StorageSession.update_timestamp_tz and _dialect_name. This introspection was causing deadlocks/hangs when used with sqlalchemy.ext.asyncio in Python 3.13.
Closes issue: https://github.com/google/adk-python/issues/1733
Co-authored-by: Shangjie Chen <deanchen@google.com>
PiperOrigin-RevId: 861353058
The generated agent deployment script now uses os.path.join and os.path.dirname(__file__) to construct the path to root_agent.yaml. This prevents issues where backslashes in Windows paths could be misinterpreted as Python escape sequences within the f-string, leading to syntax error
Close#4223
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 860236820
Merge https://github.com/google/adk-python/pull/4221
### Link to Issue or Description of Change
N/A
**2. Or, if no issue exists, describe the change:**
Fixing various typos in .py files to improve quality: see commit diffs for details
**Problem:**
See above
**Solution:**
Fixing the typos
### Testing Plan
N/A
**Unit Tests:**
N/A
**Manual End-to-End (E2E) Tests:**
N/A
### Checklist
- [X] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document.
- [X] I have performed a self-review of my own code.
- [N/A] I have commented my code, particularly in hard-to-understand areas.
- [N/A ] I have added tests that prove my fix is effective or that my feature works.
- [N/A] New and existing unit tests pass locally with my changes.
- [N/A] I have manually tested my changes end-to-end.
- [N/A ] Any dependent changes have been merged and published in downstream modules.
### Additional context
N/A
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4221 from didier-durand:fix-typos-e 1bcc8e05cdc116451222dd7fd7b0657745f769c1
PiperOrigin-RevId: 859332402
Merge https://github.com/google/adk-python/pull/4136
**Please ensure you have read the [contribution guide](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) before creating a pull request.**
### Link to Issue or Description of Change
**1. Link to an existing issue (if applicable):**
Allow google search tool to set different model #4135
**2. Or, if no issue exists, describe the change:**
_If applicable, please follow the issue templates to provide as much detail as
possible._
**Problem:**
Currently, the Google Search tool inherits and uses the same LLM model set from the parent agent for processing and summarizing search results. This creates a limitation for users who wish to decouple the agent's reasoning model from the model used for search summarization (e.g., for cost optimization or using a lightweight model for simpler summarization tasks).
**Solution:**
I have updated the Google Search tool to accept an optional LLM model parameter.
Custom Model: Users can now explicitly specify which model should be used for processing search results.
Default Behavior: If no model is specified, the tool defaults to the parent agent's model, ensuring backward compatibility.
```
# If a custom model is specified, use it instead of the original model
if self.model is not None:
llm_request.model = self.model
```
### Testing Plan
Added a new test case test_process_llm_request_with_custom_model in [test_google_search_tool.py] that verifies:
When a custom model parameter is provided to GoogleSearchTool, it overrides the model from the incoming llm_request during process_llm_request
The tool correctly uses the custom model for LLM calls while maintaining other request parameters
**Unit Tests:**
- [X] I have added or updated unit tests for my change.
- [X] All unit tests pass locally.
(base) wanglu2:adk-python/ (feature/allow-google-search-tool-set-different-llm✗) $ uv run pytest ./tests/unittests/tools/test_google_search_tool.py [22:07:32]
======================================================================== test session starts ========================================================================
platform darwin -- Python 3.13.1, pytest-9.0.2, pluggy-1.6.0
rootdir: /Users/wanglu2/Documents/Git/adk-python
configfile: pyproject.toml
plugins: mock-3.15.1, anyio-4.12.0, xdist-3.8.0, asyncio-1.3.0, langsmith-0.6.0
asyncio: mode=Mode.AUTO, debug=False, asyncio_default_fixture_loop_scope=function, asyncio_default_test_loop_scope=function
collected 21 items
tests/unittests/tools/test_google_search_tool.py ..................... [100%]
======================================================================== 21 passed in 7.91s =========================================================================
### Checklist
- [X] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document.
- [X] I have performed a self-review of my own code.
- [X] I have commented my code, particularly in hard-to-understand areas.
- [X] I have added tests that prove my fix is effective or that my feature works.
- [X] New and existing unit tests pass locally with my changes.
- [X] I have manually tested my changes end-to-end.
- [ ] Any dependent changes have been merged and published in downstream modules.
### Additional context
Co-authored-by: Kathy Wu <wukathy@google.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4136 from lwangverizon:feature/allow-google-search-tool-set-different-llm 239ea9577bd7befc9a3574aca48dc8243d55192c
PiperOrigin-RevId: 859265757
This change introduces a `flush` method to the `BigQueryAgentAnalyticsPlugin`. This ensures that all pending log events are written to BigQuery before the agent's run completes.
Key changes:
- Added `flush()` method to `BigQueryAgentAnalyticsPlugin` to force write of pending events.
PiperOrigin-RevId: 859263853
Bug: In live streaming mode, when function_call and function_response events
arrive during active transcription, they are correctly buffered but never
yielded to the caller. This causes callers to miss these events even though
they are saved to the session.
Fix: Add yield buffered_event after appending buffered events to the session
when transcription ends.
Testing:
- Added unit test: test_live_streaming_buffered_function_call_yielded_during_transcription
- Test verifies buffered events are yielded by:
1. Simulating partial transcription (triggers buffering)
2. Sending function_call during transcription (gets buffered)
3. Ending transcription (should yield buffered events)
4. Asserting both function_call and function_response are in yielded events
Test results:
- With fix: PASSED
- Without fix (yield commented out): FAILED with "Buffered function_call event was not yielded"
- Example event flow after fix:
EVENT: partial=True, input_transcription="Show me the weather"
EVENT: function_call=get_weather, args={'location': 'NYC'} <- Now yielded
EVENT: function_response=get_weather, response={...} <- Now yielded
EVENT: partial=False, input_transcription="Show me the weather for today"
PiperOrigin-RevId: 859158546
This refactors the BigQueryAgentAnalyticsPlugin to use the standard OpenTelemetry API for trace and span ID generation and propagation, replacing the custom ContextVar implementation.
Key changes:
- Utilizes `opentelemetry.trace` for starting/ending spans.
- Correctly uses `opentelemetry.context` for context attachment and detachment.
- Span information is now derived from the OpenTelemetry context when available.
- Added a fallback mechanism to ensure span_id and parent_span_id are still populated if the OpenTelemetry SDK is not initialized.
To get standard OpenTelemetry trace information in BigQuery logs, users should install `opentelemetry-sdk` and initialize a global `TracerProvider` in their application *before* initializing ADK components.
Example minimal initialization:
```python
# Install: pip install opentelemetry-sdk
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
trace.set_tracer_provider(TracerProvider())
```
PiperOrigin-RevId: 858965562
LiteLLM defaults to DEV mode, which automatically loads environment variables from a local `.env` file. This change sets LITELLM_MODE to PRODUCTION to prevent LiteLLM from implicitly loading `.env` files when used within ADK.
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 858723362
