Please set --log_level to DEBUG, if you are interested in having those API request and responses in logs.
NOTE: Generally it is not recommended to have DEBUG log level for services that run in a production setting. It is our recommendation to only use DEBUG log level in a debug or development setting.
PiperOrigin-RevId: 785972338
This change takes cares of SQL results containing values that are not json serializable (e.g. datetime, bignumeric) by converting them to their string representation.
PiperOrigin-RevId: 785719997
We update both adk web run eval endpoint and adk eval cli to use the LocalService. The old method is marked as deprecated and will be removed in later PRs.
PiperOrigin-RevId: 785612708
Fixes#423
Related to #1670
- This avoids the `GeneratorExit` error thrown, which would crash OTel metric collection and cause `Failed to detach context` error.
- This also allows all function calls are processed when exit_loop is called together with other tools in the same LLmResponse.
A sample agent for testing:
```
from google.adk import Agent
from google.adk.agents.loop_agent import LoopAgent
from google.adk.tools.exit_loop_tool import exit_loop
worker_1 = Agent(
name='worker_1',
description='Worker 1',
instruction="""\
Just say job #1 is done.
If job #1 is said to be done. Call exit_loop tool.""",
tools=[exit_loop],
)
worker_2 = Agent(
name='worker_2',
description='Worker 2',
instruction="""\
Just say job #2 is done.
If job #2 is said to be done. Call exit_loop tool.""",
tools=[exit_loop],
)
work_agent = LoopAgent(
name='work_agent',
description='Do all work.',
sub_agents=[worker_1, worker_2],
max_iterations=5,
)
root_agent = Agent(
model='gemini-2.0-flash',
name='hello_world_agent',
description='hello world agent that can roll a check prime',
instruction="""Hand off works to sub agents.""",
sub_agents=[work_agent],
)
```
PiperOrigin-RevId: 785538101
Merge https://github.com/google/adk-python/pull/1130
This enables the use of the `model-optimizer-*` family of models in vertex, as per the [documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/vertex-ai-model-optimizer#using-vertex-ai-model-optimizer).
To use this, ensure your location is set to `global` and pass a model optimizer model to an agent:
```python
root_agent = Agent(
model="model-optimizer-exp-04-09",
name="fast_and_slow_agent",
instruction="Answer any question the user gives you - easy or hard.",
generate_content_config=types.GenerateContentConfig(
temperature=0.01,
model_selection_config=ModelSelectionConfig(
feature_selection_preference=FeatureSelectionPreference.BALANCED
# Options: PRIORITIZE_QUALITY, BALANCED, PRIORITIZE_COST
)
),
)
```
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/1130 from calvingiles:feat-model-optimizer 1a76bfa22420edb07d83415dcea6dd0114084e8e
PiperOrigin-RevId: 784921913
Now the LangchainTool can wrap:
* Langchain StructuredTool (sync and async).
* Langchain @Tool (sync and async).
This enhance the flexibility for user and enables async functionalities.
PiperOrigin-RevId: 784728061
