This change introduces type descriptions for the functions which convert between A2A and GenAI `Part`s. It then allows passing instances of those functions to the various A2A-related functions/classes, effectively allowing users to inject their own logic for how part conversion should occur.
The benefit of this pattern is that users can create decorators around the core `Part` conversion logic, which allows them to intercept the cases they care about while delegating the ones they do not to the core converter. This is a pattern we use a lot in the A2A Python SDK.
One example where this type of logic is useful is for extensions: this allows extension logic to, for example, interpret an A2A DataPart into a FunctionResponse using extension-specific logic.
PiperOrigin-RevId: 803186799
Before this change, other agent's reply with thought will still be inserted in the outgoing LlmRequest due to the wrong `else` statement for calling all other type of part.
This commit also refactors test_contents.py to be behavior-oriented tests, instead of implementation-oriented, and add more test cases to cover expected scenarios.
The tests are divided into the following files with different focus:
- test_contents.py: covers the basic logic of event filter;
- test_contents_branch.py: covers the behavior related to branch, which takes effect when ParallelAgent is used.
- test_contents_other_agent.py: covers the retelling behavior to include other agents' reply as context for the current agent.
- test_contents_function.py: covers the function_call/function_response rearrangement logic mainly for `LongRunningFunctionTool`.
PiperOrigin-RevId: 802759821
1. Allow developers to specify output schema and tools together.
2. If both are specified, do the following:
2.1 Do not set output schema on the model config
2.2 Add a special tool called set_model_response(result)
2.3 `result` has the same schema as the requested output_schema
2.4 Instruct the model to use set_model_response() to output its final result, rather than output text directly.
2.5 When the set_model_response() is called, ADK will extract its content and put it in a text part, so the client would treat it as the model response.
PiperOrigin-RevId: 792686011
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
This change integrates the plugin system with ADK. PluginManager is attached to the invocation context similar to session/artifact/memory.
It includes integrations with following ADK internal callbacks:
* App callbacks: Integrated in the BaseRunner class, in run_async and run_live
* On Message callbacks: Integrated in the BaseRunner class, triggers on run_async.
* Agent callbacks: Integrated in the BaseAgent class. Leveraging the existing *callback functions
* Model callbacks: Integrating in the base_llm_flow.
* Tool callbacks: Integrated in functions.py, wrapped around the code for agent tool_callbacks
Sample code to use plugins:
```python
# Add plugins to Runner
runner = Runner(
app_name="my-app",
agent=root_agent,
artifact_service=artifact_service,
session_service=session_service,
memory_service=memory_service,
plugins=[
MySamplePlugin(),
LoggingPlugin(),
],
)
```
PiperOrigin-RevId: 781746586
This would allow users to easily make a copy of the agents they built without having to add too much boilerplates. This promotes code reuse, modularity and testability of agents.
PiperOrigin-RevId: 781214379
Bug: When a model emits a stream of tokens, it sometimes emits a final chunk of whitespace or no content. The agent was trying to parse that content into JSON, causing a validation error.
Fix: If a model is expected to return JSON and the last streamed token is empty/whitespace, the agent will no longer try to parse it, and exit gracefully.
New unit tests confirm the scenario and the fix.
PiperOrigin-RevId: 777609415
