The `BaseTool` expects the run_async to return a json-serializable object. By model_dump the McpTool result explicitly can allow what ADK runtime sees is identical to what is persisted in the session event list.
Before the change, runtime sees CallToolResult instance and Session persists its serialized dict.
https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/types.py#L916-L922
PiperOrigin-RevId: 822465432
From
```
You are an agent. Your internal name is "agent".
The description about you is "test description"
```
to
```
You are an agent. Your internal name is "agent". The description about you is "test description".
```
PiperOrigin-RevId: 822358196
To register a custom service:
- Create a factory function that takes a URI and returns an instance of your custom service. This function will parse any details it needs from the URI.
- Register your factory with the global service registry. You need to define a unique URI scheme for your service (e.g., custom).
PiperOrigin-RevId: 822310466
Merge https://github.com/google/adk-python/pull/3170
Addresses Feature Request: #3116
This PR adds a `speech_config` to the **LLM Agent configuration** for the **live use case**. When an **asynchronous LLM** call is made to the **Gemini Live API**, it prioritizes the most specific agent configuration's speech_config. If that is null, it then uses the run configuration's speech_config. Unit tests have been added to verify this behavior.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3170 from qyuo:bidi_agent_speech_config af1bd277d4f95c4a7d9aa0b16828ba3de826ce08
PiperOrigin-RevId: 822305427
Merge https://github.com/google/adk-python/pull/3194
Allow Google API toolsets to accept optional per-request headers
#3105
## Testing Plan
### Unit Tests
- ✅ Added `test_init_with_additional_headers` in `test_google_api_tool.py` to verify headers are passed to RestApiTool
- ✅ Added `test_prepare_request_params_merges_default_headers` in `test_rest_api_tool.py` to verify custom headers are merged into requests
- ✅ Added `test_prepare_request_params_preserves_existing_headers` in `test_rest_api_tool.py` to verify critical headers (Content-Type, User-Agent) are not overridden by additional_headers
- ✅ Updated `test_init` and `test_get_tools` in `test_google_api_toolset.py` to verify the parameter is properly stored and passed through
### Manual Testing
Tested with Google Ads API scenario (the original use case from issue #3105):
```python
import os
from google.adk.tools.google_api_tool import GoogleApiToolset
# Create toolset with developer-token header required by Google Ads API
google_ads_toolset = GoogleApiToolset(
client_id=os.environ["CLIENT_ID"],
client_secret=os.environ["CLIENT_SECRET"],
api_name="googleads",
api_version="v21",
additional_headers={"developer-token": os.environ["GOOGLE_ADS_DEV_TOKEN"]}
)
# Verify headers are included in API requests
tools = await google_ads_toolset.get_tools()
# Successfully made requests with the developer-token header
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3194 from Prhmma:feature/google-api-toolset-additional-headers-3105 e10489e82bfde5cf2bfd3f1bced3e1f5cea1f8b2
PiperOrigin-RevId: 822273582
Previously BuiltInCodeExecutor was missing the logic to save output files from executed code as artifacts, so images/visualizations wouldn't show up in the UI. This fix will iterate through all parts of the LlmResponse, and if any of them are images, it will save the image data using artifact_service (similar to what is done in VertexAICodeExecutor).
This fixes the backend, but there are still UI bugs that should be fixed -- events without content are currently ignored, so the image doesn't appear even though it is saved. We will add the UI fix in a separate change.
PiperOrigin-RevId: 822245140
- let _enforce_app_name_alignment warn instead of raising while caching the hint that now augments the existing “Session not found …” error
- tighten _infer_agent_origin so it ignores hidden folders (like .venv)
- make AgentTool reuse the parent runner’s app_name, stopping internal runners from conflicting in multi-agent setups
PiperOrigin-RevId: 822205860
Details:
- Adds the `StaticUserSimulator` which implements the current functionality of supplying a fixed set of user prompts for an EvalCase.
- Adds the `UserSimulatorProvider` which determines the type of user simulator required for an EvalCase (StaticUserSimulator or LlmBackedUserSimulator).
- Integrates the UserSimulatorProvider and UserSimulator into the CLI and evaluation infrastructure.
- Updates and adds unit tests for the new functionality.
- Miscellaneous updates to lay groundwork for a full implementation of the LlmBackedUserSimulator in the future.
PiperOrigin-RevId: 822198401
Merge https://github.com/google/adk-python/pull/3196
## Summary
Enhances the `AgentLoader` error message to provide clear guidance when users run `adk web` from incorrect directories.
## Motivation
During internal workshops, multiple teams encountered confusion when starting `adk web` from the wrong directory. This often happened when:
- Running `adk web my_agent/` instead of `adk web .`
- Being inside an agent directory when executing the command
- Configuring incorrect start paths during development
## Changes
- **Smart detection**: Checks if `agent.py` or `root_agent.yaml` exists in the current directory
- **Visual diagram**: Shows expected directory structure with actual agent name
- **Explicit command**: Includes `adk web <agents_dir>` usage example
- **Conditional hint**: Provides targeted guidance when user is detected to be inside an agent directory
## Example Error Message
### Before
```
ValueError: No root_agent found for 'my_agent'. Searched in 'my_agent.agent.root_agent', 'my_agent.root_agent' and 'my_agent/root_agent.yaml'. Ensure 'path/my_agent' is structured correctly, an .env file can be loaded if present, and a root_agent is exposed.
```
### After
```
ValueError: No root_agent found for 'my_agent'. Searched in 'my_agent.agent.root_agent', 'my_agent.root_agent' and 'my_agent/root_agent.yaml'.
Expected directory structure:
<agents_dir>/
my_agent/
agent.py (with root_agent) OR
root_agent.yaml
Then run: adk web <agents_dir>
Ensure 'path/my_agent' is structured correctly, an .env file can be loaded if present, and a root_agent is exposed.
HINT: It looks like you might be running 'adk web' from inside an agent directory. Try running 'adk web .' from the parent directory that contains your agent folder, not from within the agent folder itself.
```
## Testing
- ✅ Existing unit tests pass (17/22, with 5 pre-existing failures unrelated to this change)
- ✅ `test_agent_not_found_error` passes, confirming error message enhancement works correctly
- ✅ Code follows ADK contribution guidelines
## Type
- [x] Bug fix (improved error messaging)
- [ ] Feature
- [ ] Breaking change
- [ ] Documentation
## Related
Fixes#3195
---
**Tags**: #non-breaking
🤖 Generated with [Claude Code](https://claude.com/claude-code)
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3196 from jpantsjoha:fix/improve-adk-web-error-message a73b190f5b021dbe0afa8426172696ee9eeae8da
PiperOrigin-RevId: 822186700
Merge https://github.com/google/adk-python/pull/3060
## Description
Fixes#3059
This PR fixes two endpoints in `adk web` that fail when using App objects instead of bare agents.
## Changes
- **Eval execution endpoint** (line ~969): Extract root_agent from App objects before passing to LocalEvalService
- **Graph visualization endpoint** (line ~1308): Extract root_agent from App objects before graph operations
Both endpoints now properly handle both BaseAgent and App objects by checking the type and extracting `.root_agent` when needed.
## Testing Plan
### Manual E2E Testing with ADK Web
Tested with an App object that includes context caching:
```python
from google.adk.apps import App
from google.adk.agents import LlmAgent
root_agent = LlmAgent(name="MyAgent", model="gemini-1.5-pro-002")
app = App(
name="my_agent",
root_agent=root_agent,
context_cache_config=ContextCacheConfig(...)
)
```
**Before fix:**
- Graph visualization failed (tried to call agent methods on App object)
- Eval execution failed (LocalEvalService received App instead of agent)
**After fix:**
- Graph visualization works correctly
- Eval execution works correctly
- Both endpoints properly extract root_agent from App objects
## Checklist
- [x] Code follows project style (autoformat.sh passed)
- [x] Changes are focused and minimal
- [x] Issue #3059 created and referenced
- [x] Manual E2E testing completed
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3060 from ejfn:ejfn/bugfix-app-object-endpoints 01c30191bfd9487a8c8463ccf24b297cb9a4ce37
PiperOrigin-RevId: 821746910
Add a header_provider param which is a callable[ReadonlyContext, Dict[str, Any]] for users to build headers in MCPToolset
fix: https://github.com/google/adk-python/issues/3156
PiperOrigin-RevId: 820412372
This change introduces type aliases for request and event conversion functions:
- `A2ARequestToADKRunArgsConverter`: For converting A2A `RequestContext` to an `ADKRunArgs` Pydantic model.
- `AdkEventToA2AEventsConverter`: For converting ADK `Event` to a list of A2A `A2AEvent` objects.
The `convert_a2a_request_to_adk_run_args` function now returns a structured `ADKRunArgs` model instead of a generic dictionary, improving type safety.
These converter types can now be provided via the `A2aAgentExecutorConfig` to customize the conversion logic used by the `A2aAgentExecutor`. The executor defaults to the existing `convert_a2a_request_to_adk_run_args` and `convert_event_to_a2a_events` functions if no custom converters are specified.
This allows users to inject their own logic for handling request and event conversions, for example, to add custom metadata or transform data types, without modifying the core executor.
PiperOrigin-RevId: 819934960
Merge https://github.com/google/adk-python/pull/2884closes: #2883
# Fix
When put leage data into event and load it. the _pickle.UnpicklingError was occurred.
The root caurse is `DynamicPickleType` mapping `BLOB` as default in case of MySql, not `LONGBLOB`. And learge data will be able to cut off tail of data. And raise pickle error.
# What todo
Defined `LONFBLOB` as default explicitly.
# Question
Where should we code the test code like this case? I cannot found the test code the DB and table was created expectedly.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2884 from Lin-Nikaido:fix/#2883-mysql-datatype-fix 2be9b38fc3f5d5083b0b6715a2bf7b4eff5d947b
PiperOrigin-RevId: 819891727
Merge https://github.com/google/adk-python/pull/2206
### Summary
This PR adds support for `ContextWindowCompressionConfig` in `RunConfig`.
This enables context window compression using a `trigger_tokens` threshold and a sliding window with a `target_tokens` limit.
This feature is useful for managing long-running audio inputs.
### Related Issue
Closes#2188
### Testing Plan
- Added new unit test: `test_streaming_with_context_window_compression_config`
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2206 from ac-machache:support/add-context-compression-config c8a5b15cae2d2b72f331797d07ae0bbaf977ed3c
PiperOrigin-RevId: 819855786
This change removes the `convert_session_to_eval_format` function and its associated unit tests. New tests for `create_gcs_eval_managers_from_uri` are also added.
PiperOrigin-RevId: 819576620
- add a shared --structured_logs flag to adk web and adk api_server so users can opt into JSON-formatted output
- introduce CloudTraceJSONFormatter that emits structured entries and attaches current Cloud Trace/Span IDs when an OpenTelemetry context is active
- update CLI logging setup to clear duplicate stdout handlers when Cloud Logging is enabled and to reconfigure existing handlers (like from Uvicorn) so they also pick up the structured format and requested log level
With the flag disabled the CLIs keep their existing text logs; when enabled, the services now produce Cloud Logging–friendly JSON that can be correlated with distributed traces.
PiperOrigin-RevId: 818823818
Update plugin manager and built-in plugins to prioritize CallbackContext. Keep InvocationContext access for legacy plugins with adapter. Change callback docs/tests to cover the new context.
PiperOrigin-RevId: 818822267
Update plugin manager and built-in plugins to prioritize CallbackContext. Keep InvocationContext access for legacy plugins with adapter. Change callback docs/tests to cover the new context.
PiperOrigin-RevId: 818798087
This is so we don't need to worry about side effect of Loop in all agent type. Custom agent should do the same if there exists loop inside.
PiperOrigin-RevId: 818766305
This change removes the `run_evals` function and its helper `_get_evaluator` from `cli_eval.py`, as they were marked as deprecated. Corresponding test mocks and patches in `test_fast_api.py` are also removed.
PiperOrigin-RevId: 818719422
changed the LiteLLM content conversion so Part.file_data.file_uri (like the gs://…) becomes a file object with file_id, making sure GCS-backed files reach LiteLLM proxies instead of being dropped add unit tests covering both _get_content and _content_to_message_param paths for file URIs
PiperOrigin-RevId: 817658432
This change removes the `evaluate`, `_evaluate_row`, `are_tools_equal`, `_remove_tool_outputs`, `_report_failures`, and `_print_results` static methods from `TrajectoryEvaluator`, along with their corresponding unit tests. These methods were previously marked as deprecated.
PiperOrigin-RevId: 817477494
This CL updates the "What's new" section to include Resumability, ReflectRetryToolPlugin, Context compaction, and Search tool support. It also moves "Agent Config" and "Tool Confirmation" from "What's new" to "Key Features".
PiperOrigin-RevId: 817469210
The added section provides details for the community call on Oct 15, 2025, including the agenda and links to join and add to calendars.
PiperOrigin-RevId: 817457276
Agent developers can now create an eval set and add eval cases through command line itself. Adding an eval case is limited only to specifying conversation scenarios.
Sample comamnds:
- Create an eval set:
adk eval_set create \
contributing/samples/hello_world \
set_01
- Add an eval case with scenario file
Content of scenarios.json file:
'{"scenarios": [{"starting_prompt": "hello", "conversation_plan": "world"}]}'
adk eval_set add_eval_case \
contributing/samples/hello_world \
set_01 \
--scenarios scenarios.json
PiperOrigin-RevId: 817456117
The `agent_loader.load_agent` method can now return an `App` object. This change unwraps the `App` to get its `root_agent` before passing it to the graph builder, makes sure a `BaseAgent` instance is always used
PiperOrigin-RevId: 817209601
Details:
- Introduces a concept of `ConversationScenario` to represent a scenario that user simulator is supposed to follow.
- Introduces a `UserSimulator` interface, that one should implement. UserSimulator interface will be integrated with LocalEvalService in subsequent PRs.
PiperOrigin-RevId: 816883699
When there are multiple intervals and compactions, the original implementation only keep the last one. The right implementation is to keep as many compaction events/summary as the requested internals.
PiperOrigin-RevId: 816516662
This plugin intercepts tool failures, provides structured guidance to the LLM for reflection and correction, and retries the operation up to a configurable limit.
**Key Features:**
- **Concurrency Safe:** Uses locking to safely handle parallel tool
executions
- **Configurable Scope:** Tracks failures per-invocation (default) or globally
using the `TrackingScope` enum.
- **Extensible Scoping:** The `_get_scope_key` method can be overridden to
implement custom tracking logic (e.g., per-user or per-session).
- **Granular Tracking:** Failure counts are tracked per-tool within the
defined scope. A success with one tool resets its counter without affecting
others.
- **Custom Error Extraction:** Supports detecting errors in normal tool
responses
that
don't throw exceptions, by overriding the `extract_error_from_result`
method.
**Example:**
```python
from my_project.plugins import ReflectAndRetryToolPlugin, TrackingScope
# Example 1: (MOST COMMON USAGE):
# Track failures only within the current agent invocation (default).
error_handling_plugin = ReflectAndRetryToolPlugin(max_retries=3)
# Example 2:
# Track failures globally across all turns and users.
global_error_handling_plugin = ReflectAndRetryToolPlugin(max_retries=5,
scope=TrackingScope.GLOBAL)
# Example 3:
# Retry on failures but do not throw exceptions.
error_handling_plugin =
ReflectAndRetryToolPlugin(max_retries=3,
throw_exception_if_retry_exceeded=False)
# Example 4:
# Track failures in successful tool responses that contain errors.
class CustomRetryPlugin(ReflectAndRetryToolPlugin):
async def extract_error_from_result(self, *, tool, tool_args,tool_context,
result):
# Detect error based on response content
if result.get('status') == 'error':
return result
return None # No error detected
error_handling_plugin = CustomRetryPlugin(max_retries=5)
```
PiperOrigin-RevId: 816456549
Merge https://github.com/google/adk-python/pull/2857
Adds support for invoking Gemma models via the Gemini API endpoint. To support agentic function, callbacks are added which can extract and transform function calls and responses into user and model messages in the history.
This change is intended to allow developers to explore the use of Gemma models for agentic purposes without requiring local deployment of the models. This should ease the burden of experimentation and testing for developers.
A basic "hello world" style agent example is provided to demonstrate proper functioning of Gemma 3 models inside an Agent container, using the dice roll + prime check framework of similar examples for other models.
## Testing
### Testing Plan
- add and run integration and unit tests
- manual run of example `multi_tool_agent` from quickstart using new `Gemma` model
- manual run of `hello_world_gemma` agent
### Automated Test Results:
| Test Command | Results |
|----------------|---------|
| pytest ./tests/unittests | 4386 passed, 2849 warnings in 58.43s |
| pytest ./tests/unittests/models/test_google_llm.py | 100 passed, 4 warnings in 5.83s |
| pytest ./tests/integration/models/test_google_llm.py | 5 passed, 2 warnings in 3.73s |
### Manual Testing
Here is a log of `multi_tool_agent` run with locally-built wheel and using Gemma model.
```
❯ adk run multi_tool_agent
Log setup complete: /var/folders/bg/_133c0ds2kb7cn699cpmmh_h0061bp/T/agents_log/agent.20250904_152617.log
To access latest log: tail -F /var/folders/bg/_133c0ds2kb7cn699cpmmh_h0061bp/T/agents_log/agent.latest.log
/Users/<redacted>/venvs/adk-quickstart/lib/python3.11/site-packages/google/adk/cli/cli.py:143: UserWarning: [EXPERIMENTAL] InMemoryCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
credential_service = InMemoryCredentialService()
/Users/<redacted>/venvs/adk-quickstart/lib/python3.11/site-packages/google/adk/auth/credential_service/in_memory_credential_service.py:33: UserWarning: [EXPERIMENTAL] BaseCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
super().__init__()
Running agent weather_time_agent, type exit to exit.
[user]: what's the weather like today?
[weather_time_agent]: Which city are you asking about?
[user]: new york
[weather_time_agent]: OK. The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees Fahrenheit).
```
And here is a snippet of a log generated with DEBUG level logging of the `hello_world_gemma` sample. It demonstrates how function calls are extracted and inserted based on Gemma model interactions:
```
...
2025-09-04 15:32:41,708 - DEBUG - google_llm.py:138 -
LLM Request:
-----------------------------------------------------------
System Instruction:
None
-----------------------------------------------------------
Contents:
{"parts":[{"text":"\n You roll dice and answer questions about the outcome of the dice rolls.\n You can roll dice of different sizes...\n"}],"role":"user"}
{"parts":[{"text":"Hi, introduce yourself."}],"role":"user"}
{"parts":[{"text":"Hello! I am data_processing_agent, a hello world agent that can roll many-sided dice and check if numbers are prime. I'm ready to assist you with those tasks. Let's begin!\n\n\n\n"}],"role":"model"}
{"parts":[{"text":"Roll a die with 100 sides and check if it is prime"}],"role":"user"}
{"parts":[{"text":"{\"args\":{\"sides\":100},\"name\":\"roll_die\"}"}],"role":"model"}
{"parts":[{"text":"Invoking tool `roll_die` produced: `{\"result\": 82}`."}],"role":"user"}
{"parts":[{"text":"{\"args\":{\"nums\":[82]},\"name\":\"check_prime\"}"}],"role":"model"}
{"parts":[{"text":"Invoking tool `check_prime` produced: `{\"result\": \"No prime numbers found.\"}`."}],"role":"user"}
{"parts":[{"text":"The die roll was 82, and it is not a prime number.\n\n\n\n"}],"role":"model"}
{"parts":[{"text":"Roll it again."}],"role":"user"}
-----------------------------------------------------------
Functions:
-----------------------------------------------------------
2025-09-04 15:32:41,708 - INFO - models.py:8165 - AFC is enabled with max remote calls: 10.
2025-09-04 15:32:42,693 - INFO - google_llm.py:180 - Response received from the model.
2025-09-04 15:32:42,693 - DEBUG - google_llm.py:181 -
LLM Response:
-----------------------------------------------------------
Text:
{"args":{"sides":100},"name":"roll_die"}
-----------------------------------------------------------
...
```
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2857 from douglas-reid:add-gemma-via-api e6d015f6a9ccbcf20ef7a7af8e4bbe1e9a5936b6
PiperOrigin-RevId: 816451001
If `EventsCompactionConfig` is provided without a `compactor`, a `SlidingWindowCompactor` is now automatically instantiated using the `root_agent`'s LLM. This simplifies configuration by providing a sensible default.
PiperOrigin-RevId: 816038579
The class is now named `LlmEventSummarizer` to better reflect that its primary function is to use an LLM to summarize events. The docstring has been updated to clarify that this class is responsible *only* for the LLM-based summarization of a given set of events, while the logic for determining *when* and *which* events form the sliding window is handled by an external component, such as an ADK Runner.
PiperOrigin-RevId: 815976264
This change introduces a new `analyze_contribution` function in `query_tool.py` which uses BigQuery ML's `CREATE MODEL` with `CONTRIBUTION_ANALYSIS` type and `ML.GET_INSIGHTS` to analyze the contribution of different dimensions to a given metric. The new function is also added to the `bigquery_toolset`.
PiperOrigin-RevId: 815849281
before this change, we estimate the token count of the contents to cache and use it to compare with the threshold user set. but that's not precise , so we use the actual prompt token count of previous llm request.
We won't create cache for the very initial request
PiperOrigin-RevId: 814484840
We updated the one of the public methods on AgentEvaluator to take in eval metric configurations using a more formal EvalConfig data model.
We also mark "criteria" field on the method as deprecated.
Updated some integration test cases.
PiperOrigin-RevId: 814314134
The root cause is an unsafe in-memory mutation. The `SaveFilesAsArtifactsPlugin` was saving a direct reference to the message part and then modifying the message list in-place. This created a race condition where downstream code could alter the original part *after* it had been saved as an artifact, leading to a corrupted state.
This CL saves a `copy.copy()` of the artifact, which create a snapshot of the data.
Also Changes the plugin to return a new `types.Content` object instead of modifying the original message in-place
PiperOrigin-RevId: 814308070
This is allow user to update session state without running the agent. e.g. if I want to test some case when session has certain state on adk web.
PiperOrigin-RevId: 814252851
Currently, the A2A Task -> ADK event conversion is producing the same events on the last two update events (the last is a status update marking the task complete)
The change here based on A2AClientEvent(task, update):
- if the update == None: handle the non-streaming task case and also streaming case for the initial task creation event
- if the update = TaskStatusUpdateEvent AND a message is set: emit an event with that message
- if a task status update AND no message is set: don't emit event (for example, the final status update)
- if the update is ArtifactUpdateEvent and it's final artifact: emit the event
PiperOrigin-RevId: 812878869
The PR does two main things:
1) Introduces a new rubric based tool use metric
2) Given that we now have two rubric based metric, we refactor and create a new RubricBasedEvaluator interface.
PiperOrigin-RevId: 811983514
mainly because http://github.com/robots.txt disallows `/*/raw/` path. using GCS HTTP URIs is more reliable with Gemini model.
PiperOrigin-RevId: 811409688
Changes include:
- Implementing missing attributes. e.g. 'gen_ai.agent.name'
- Specifying reasons for not filling out some conditionally required attributes. e.g. 'gen_ai.data_source.id'
- Specifying reasons for not including certain attributes which are specified in current semconv. e.g. inference attributes on agent spans
PiperOrigin-RevId: 811379706
this is to allow turning on debug log for debugging if some unexpected behavior observed during running cache analysis experiments.
PiperOrigin-RevId: 811189954
This is to avoid serialization issue for some fields that are not json serializable.
meanwhile restructure the debug logs in context cache manager for better debugging potential issues.
PiperOrigin-RevId: 811182492
AppDetails require two pieces of information:
1) Instructions
2) Tools
Both these pieces of information are gathered using the llm_request that was passed to the model. This approach, slightly invasive, ensures that we capture the "exact" instructions and tools that were given to the model.
PiperOrigin-RevId: 811180648
Details:
1. Data model for storing App Details (the agentic system)
As we move towards LLM as Judge metrics, we see that some of these metrics need information about the Agentic system that was used for inferencing. We add a data model to capture that.
2. Data model for Steps
We refine the concept of intermediate data. Previously it stored data in the form of a multiple lists, thereby losing out on the chronological information. This information is needed for some of the metrics. So we refine the concept of intermediate data as series of logical steps that an Agent Take.
PiperOrigin-RevId: 811122784
Merge https://github.com/google/adk-python/pull/2823
Description
This change introduces a tool_name_prefix attribute to McpToolset and McpToolsetConfig. This allows for adding a prefix to the
names of all tools within the toolset, which can help avoid naming collisions and provide better organization.
The implementation involves updating the McpToolset's __init__ and from_config methods to handle the new tool_name_prefix and
adding the corresponding field to McpToolsetConfig.
Testing Plan
A new unit test file has been added to ensure the functionality works as expected.
- `tests/unittests/tools/test_mcp_toolset.py`:
- The test_mcp_toolset_with_prefix test case verifies that the tool_name_prefix is correctly applied to the tool names
retrieved from the toolset.
- All tests were run via pytest and passed.
Related Issue
- Closes#2814
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2823 from shsha4:fix/issue-2814 e8e5b0d6d5f406d3875faf2229a96701725b7a5e
PiperOrigin-RevId: 810500616
Merge https://github.com/google/adk-python/pull/2458
**Summary**
Verifies that user-provided messages are always passed to the LLM as 'user' role, regardless of whether the role is explicitly set in types.Content. Before the current fix, if the LlmRequest from the user doesn't have the 'user' role, but has the user content, then the text is being replaced with the standard text - "Handle the requests as specified in the System Instruction." and the content from the user is completely ignored and not passed into the LLM.
**Code to replicate the problem**
```
from google.adk.agents import LlmAgent
from google.adk.sessions import InMemorySessionService
from google.adk.runners import Runner
from google.genai.types import Content, Part
from google.adk.models.lite_llm import LiteLlm
from google.adk.models import LlmRequest
from google.genai import types
from pydantic import Field
import litellm
litellm._turn_on_debug()
import warnings
warnings.filterwarnings("ignore", category=UserWarning, message=".*InMemoryCredentialService.*")
import os
from dotenv import load_dotenv
# Load environment variables from the agent directory's .env file
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# Define agent with output_key
root_agent = LlmAgent(
name="name_of_agent",
model=LiteLlm(model="azure/gpt-4o-mini"),
instruction="You are a customer agent to help the users with their concerns."
)
# --- Setup Runner and Session ---
app_name, user_id, session_id = "state_app", "user1", "session1"
session_service = InMemorySessionService()
runner = Runner(
agent=root_agent,
app_name=app_name,
session_service=session_service
)
print(f"Runner created for agent '{runner.agent.name}'.")
session = await session_service.create_session(
app_name=app_name,
user_id=user_id,
session_id=session_id
)
# --- Run the Agent ---
async def call_agent_async(query: str, runner, user_id, session_id):
user_message = Content(parts=[Part(text=query)])
async for event in runner.run_async(
user_id=user_id,
session_id=session_id,
new_message=user_message
):
print("event")
print(f" [Event]\n Author: {event.author}\n Type: {type(event).__name__}",
f"\n Final: {event.is_final_response()}\n Content: {event.content}")
return event
event = await call_agent_async("What is the capital of India.",runner=runner,user_id=user_id,session_id=session_id)
```
**Before the fix (current adk-python code output)**
```
00:29:24 - LiteLLM:DEBUG: utils.py:348 -
00:29:24 - LiteLLM:DEBUG: utils.py:348 - Request to litellm:
00:29:24 - LiteLLM:DEBUG: utils.py:348 - litellm.acompletion(model='azure/gpt-4o-mini', messages=[{'role': 'developer', 'content': 'You are a customer agent to help the users with their concerns.\n\nYou are an agent. Your internal name is "name_of_agent".'}, {'role': 'user', 'content': 'Handle the requests as specified in the System Instruction.'}], tools=None, response_format=None)
```
**After the fix (after resolving the fix)**
```
00:28:46 - LiteLLM:DEBUG: utils.py:349 -
00:28:46 - LiteLLM:DEBUG: utils.py:349 - Request to litellm:
00:28:46 - LiteLLM:DEBUG: utils.py:349 - litellm.acompletion(model='azure/gpt-4o-mini', messages=[{'role': 'developer', 'content': 'You are a customer agent to help the users with their concerns.\n\nYou are an agent. Your internal name is "name_of_agent".'}, {'role': 'user', 'content': 'What is the capital of India.'}], tools=None, response_format=None)
```
**Testing**
Following unit test is created to test the applied changes and added in the location as suggested in the guidelines.
adk-python\tests\unittests\models\test_base_llm.py
```
import pytest
from google.genai import types
from google.adk.models.llm_request import LlmRequest
from google.adk.models.lite_llm import _get_completion_inputs
@pytest.mark.parametrize("content_kwargs", [
# Case 1: Explicit role provided
{"role": "user", "parts": [types.Part(text="This is an input text from user.")]},
# Case 2: Role omitted, should still be treated as 'user'
{"parts": [types.Part(text="This is an input text from user.")]}
])
def test_user_content_role_defaults_to_user(content_kwargs):
"""
Verifies that user-provided messages are always passed to the LLM as 'user' role,
regardless of whether the role is explicitly set in types.Content.
The helper `_get_completion_inputs` should give normalize messages so that
explicit 'user' and implicit (missing role) are equivalent.
"""
llm_request = LlmRequest(
contents=[types.Content(**content_kwargs)],
config=types.GenerateContentConfig()
)
messages, _, _, _ = _get_completion_inputs(llm_request)
assert all(
msg.get("role") == "user" for msg in messages
), f"Expected role 'user' but got {messages}"
assert any(
"This is an input text from user." == (msg.get("content") or "")
for msg in messages
), f"Expected the user text to be preserved, but got {messages}"
```
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2458 from TanejaAnkisetty:bug/agent-user-content 381b01418d249b9e6bd91ebb518ff25339a8e47b
PiperOrigin-RevId: 809281620
Static instructions:
Always added to system instructions for context caching
Dynamic instructions:
Added to system instructions when no static instruction exists (for backward compatibility), OR inserted before last batch of continuous user content when static instructions exist
PiperOrigin-RevId: 809170679
1. add a context cache config in app level which will apply to all agents in the app
2. pass on cache config through invocation context to llm_reqeust
3. store cache metadata in llm_response
4. lookup old cache metadata from latest event for reusing old cache
5. create new cache if old cache cannot be reused
PiperOrigin-RevId: 809158578
Currently there is chance for Cloud Monitoring-related errors in logs during shutdown. Let's disable metrics part until it is fixed.
PiperOrigin-RevId: 808930635
The docstrings for `compaction_range` and `compacted_content` are updated to reflect that compaction is based on timestamp ranges rather than sequence IDs, and to use consistent terminology ("compacted" instead of "summarized").
PiperOrigin-RevId: 808770610
Merge https://github.com/google/adk-python/pull/2960
1. All in one authentication sample (has an IDP, Agent and the application) under `contributing/samples/authn-adk-all-in-one/`
2. Documented for all the steps.
3. OAuth 2.0 Authorization Code Grant type used by the agent.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2960 from nikhilpurwant:main dfcc821602d265c4ae7cc42eb1f5739beaad6f87
PiperOrigin-RevId: 808672120
This add `GoogleMapsGroundingTool`, a built-in tool for Gemini 2 models to ground query results with Google Maps. This tool operates internally within the model and is only available when using the VertexAI Gemini API.
PiperOrigin-RevId: 808650501
Provide a more efficient way to compact LLM context for better agentic performance.
* `app`: the top level abstraction for an ADK application. It contains an root agent, and plugins.
* `content_strategy`: the abstraction for selecting the contents for LLM request.
* `compaction_strategy`: the abstraction for compacting the events.
* Added `sequence_id` and `summary_range` in event class.
PiperOrigin-RevId: 808634224
Merge https://github.com/google/adk-python/pull/2937
**Closes #2936**
This Pull Request addresses the issue where `LlmAgent` outputs, when configured with `output_schema` and `tools`, were presenting escaped Latin characters (e.g., `\xf3` for `ó`) in the final response. This behavior occurred because `json.dumps` was being called with `ensure_ascii=True` (its default), which is not ideal for human-readable output, especially when dealing with non-ASCII characters common in many languages like Portuguese.
**Changes Proposed:**
* Modified the `_OutputSchemaRequestProcessor` in `src/google/adk/flows/llm_flows/_output_schema_processor.py` to explicitly set `ensure_ascii=False` when calling `json.dumps` for the `set_model_response` tool's output.
**Impact:**
This change ensures that all non-ASCII characters in the structured model response are preserved in their natural form, improving the readability and user experience of agent outputs, particularly for users interacting in languages with accented characters or other special symbols.
**Testing:**
The fix was verified locally by running an `LlmAgent` with an `output_schema` and confirming that responses containing Latin characters (e.g., "ação", "caminhão", "ícone") are now correctly displayed without escaping.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2937 from amenegola:fix/issue-2936-escape-chars 6cac00f97aa4cd8d8ccaa97ec5fffc74f57995dc
PiperOrigin-RevId: 808622892
- Add conformance command group with create subcommand
- Implement category/name/spec.yaml with generated-*.yaml files
- Support executing agents with queries and recording sessions
- Create test cases with recorded llm interactions and tool calls/results
Expected folder structure:
```
conformance_repo/
├── agents/ # Agent definitions - contains all config-based agents shared by test cases.
│ ├── single_basic/
│ ├── multi_basic/
│ └── single_tool_builtin/
│
└── tests/ # Test cases
├── core/ # Test category
│ ├── desc_001/ # Individual test case
│ │ ├── spec.yaml # Human-written specification
│ │ ├── generated-session.yaml
│ │ ├── generated-recordings.yaml
│ │ └── ... # Potential future generated files
│ ├── f_001/
│ │ ├── spec.yaml
│ │ ├── generated-session.yaml
│ │ ├── generated-recordings.yaml
│ │ └── ...
```
Help text:
```
-> % adk conformance create --help
Usage: adk conformance create [OPTIONS] [PATHS]...
Generate ADK conformance test YAML files from TestCaseInput specifications.
NOTE: this is work in progress.
This command reads TestCaseInput specifications from input.yaml files, executes the specified test cases against agents, and generates conformance test files with recorded agent interactions as
test.yaml files.
Expected directory structure: category/name/input.yaml (TestCaseInput) -> category/name/test.yaml (TestCase)
PATHS: One or more directories containing test case specifications. If no paths are provided, defaults to 'tests/' directory.
Examples:
Use default directory: adk conformance create
Custom directories: adk conformance create tests/core tests/tools
Options:
--help Show this message and exit.
```
PiperOrigin-RevId: 808609547
Corrected `CountInvocationPlugin` to be a class reference and added `ContextFilterPlugin` to limit the number of tool invocations kept in the context to 3.
PiperOrigin-RevId: 808591608
When start the server with `--extra_plugins=google.adk.cli.plugins.recordings_plugin.RecordingsPlugin`, it will trigger recording with expected state in session.
PiperOrigin-RevId: 808432022
This commit introduces a new ContextFilterPlugin which allows for filtering the LlmRequest contents before they are sent to the LLM. This helps in managing and potentially reducing the size of the LLM context.
The plugin provides two primary filtering mechanisms:
num_invocations_to_keep: Keeps only the specified number of the most recent user-model invocations. An invocation is defined as one or more user messages followed by a model response.
custom_filter: Allows for a user-defined callable to be applied to the contents for more flexible filtering.
Unit tests have been added to cover the different filtering scenarios, including:
Filtering by the last N invocations.
Filtering using a custom function.
Combining both filtering methods.
Handling cases with multiple user turns in a single invocation.
Ensuring no filtering occurs when options are not provided.
Gracefully handling exceptions from custom filter functions."
For example, when num_of_innovacations=2:
-----------------------------------------------------------
Contents:
{"parts":[{"text":"9"}],"role":"user"}
{"parts":[{"text":"I am sorry, I cannot fulfill this request. I need more information on what you would like me to do. I can roll a die or check prime numbers.\n"}],"role":"model"}
{"parts":[{"text":"1"}],"role":"user"}
{"parts":[{"text":"I am sorry, I cannot fulfill this request. I need more information on what you would like me to do. I can roll a die or check prime numbers.\n"}],"role":"model"}
{"parts":[{"text":"10"}],"role":"user"}
-----------------------------------------------------------
PiperOrigin-RevId: 808355316
Right now the bigquery sample agent is configured to run with OAuth, which requires some set up. This change makes it more readily usable, both locally and in AgentEngine, as Application Default Credentials (ADC) is easier to set up, and often local and AgentEngine environment already have it set up.
PiperOrigin-RevId: 808315879
Also moves the `Recordings` pydantic models into this plugins/ package.
Key features:
- Records LLM requests/responses and tool calls/results to YAML files in `generated-recordings.yaml`.
- Use session state to determine where to read and output recordings.
PiperOrigin-RevId: 807969100
Cloud Trace, Cloud Monitoring and Cloud Logging integrations are set up via OTel if otel_to_cloud CLI param/fast_api arg is provided.
This is similar to current Cloud Trace integration via trace_to_cloud, just extended to Monitoring and Logging as well.
PiperOrigin-RevId: 807385680
Cloud Trace, Cloud Monitoring and Cloud Logging integrations are set up via OTel if otel_to_cloud CLI param/fast_api arg is provided.
This is similar to current Cloud Trace integration via trace_to_cloud, just extended to Monitoring and Logging as well.
PiperOrigin-RevId: 807285744
Cloud Trace, Cloud Monitoring and Cloud Logging integrations are set up via OTel if otel_to_cloud CLI param/fast_api arg is provided.
This is similar to current Cloud Trace integration via trace_to_cloud, just extended to Monitoring and Logging as well.
PiperOrigin-RevId: 807230668
The `after_agent_callback` in plugin works similarly as the `after_agent_callback` in `base_agent.py`, e.g. it only append new content, but cannot modify the previous content.
PiperOrigin-RevId: 807162139
Similarity search tool supports similarity search on Spanner data by embedding a text query to a vector and run vector search with the embedded vector.
PiperOrigin-RevId: 806502499
Recent change to the updated A2A Client SDK broke the logging utilities. This updates those logging utilities to work with the new A2A SDK structure.
PiperOrigin-RevId: 806482017
Right now the tolls are always running against multi-region US by default. With this change the agent builder can scope the tools to data and compute in a particular BigQuery location.
PiperOrigin-RevId: 806473857
Update the bug report issue template to request minimal reproducible examples, error/stacktrace, clarify OS options, and include questions about LiteLLM usage and specific model details.
PiperOrigin-RevId: 806435953
The new test verifies that `output_audio_transcription` and `input_audio_transcription` attributes are unique to each `RunConfig` instance, preventing unintended side effects from modifying one instance.
PiperOrigin-RevId: 806405671
Switched the active model from `gemini-live-2.5-flash-preview` (for AI Studio) to `gemini-2.0-flash-live-preview-04-09` (for Vertex).
PiperOrigin-RevId: 806348640
Both are valid YAML, just with indent, it's more visually friend to see the data structure hierarchy.
Before
```
items:
- item1
- item2
- item3
```
After
```
items:
- item1
- item2
- item3
```
PiperOrigin-RevId: 806117290
The old live/bidi agents are using a cache to store context/history during agent transfer etc. As we have added support for session for live/bidi, we are now migrating the context/history cache to it. This improves scalability, efficiency and maintainability.
It introduces several changes:
* AudioTranscriber support is removed as now we are using native transcription from models.
* Transcription is returned as input_transcription/output_transcription fields and no longer as contents.
* We will return a new event with artifact references of file type of audio/pcm.(in addition to existing audio response event. So the users of this api need to do proper filtering here.)
PiperOrigin-RevId: 805997675
For advanced eval use cases, we do expect agent developers to have rubrics that are specific to an Eval Case and in some cases even specific to a single invocation/turn in the eval case conversation.
A separate PR will be created to consume this data model changes in ADK Eval.
PiperOrigin-RevId: 805588808
a. dump the discussion content to a tmp file first to avoid github redaction of environment variable
b. instruct the agent to use get_discussion_and_comments only when discussion content json is not available.
PiperOrigin-RevId: 805581573
Changes references from `gemini-1.5-flash` and `gemini-1.5-pro` to `gemini-2.5-flash` and `gemini-2.5-pro` in docstrings, default values, sample agents, and tests.
PiperOrigin-RevId: 805536434
Details:
- We plan on introducing Rubric based metrics in subsequent changes. This change introduces the data model needed that allows agent developer to provide rubrics.
- We also introduce a data model for the config that the eval system has been using for quite some time. It was loosely and informally described as a dictionary of metric names and expected thresholds. In this change, we actually formalize it using a pydantic data model, and extend it allow developers to specify rubrics as a part of their eval config.
What is a rubric based metric?
A rubric based metric is the assessment of a Agent's response (final or intermediate) along some rubric. This evaluation of agent's response significantly differs from the strategy where one has to provide a golden response.
PiperOrigin-RevId: 805488436
These tests verify that `ValueError` is raised when `Runner` is initialized without providing either an `app` instance or both `app_name` and `agent`.
PiperOrigin-RevId: 805427256
Merge https://github.com/google/adk-python/pull/2864
**Reason for this change:**
Multiple typos were found in comments, docstrings, and code throughout the codebase, which could lead to confusion and reduce code readability.
**Changes made:**
Fixed the following typos across 8 files:
1. contributing/samples/adk_answering_agent/utils.py:130: "extention" → "extension"
2. llms-full.txt:15171: "fuction" → "function"
3. src/google/adk/a2a/converters/part_converter.py:
- Line 96: "Conver" → "Convert", "reponse" → "response"
- Line 99: "suervice" → "service"
- Line 100: "accordinlgy" → "accordingly"
- Line 191: "Conver" → "Convert", "reponse" → "response"
- Line 195: "accordinlgy" → "accordingly"
4. src/google/adk/agents/base_agent.py:568: "custome" → "custom"
5. src/google/adk/evaluation/agent_evaluator.py:572: "Retruns" → "Returns"
6. src/google/adk/flows/llm_flows/basic.py:55: "outoput_schema" → "output_schema"
7. src/google/adk/flows/llm_flows/contents.py:136: "fuction_response" → "function_response"
8. src/google/adk/models/google_llm.py:138: "gemini_llm_connecton.py" → "gemini_llm_connection.py"
**Impact:**
This change will:
- Improve code documentation clarity and professionalism
- Make comments, docstrings, and code more readable and accurate
- Help prevent confusion for developers reading the code
- Ensure consistency in terminology throughout the codebase
This is a non-breaking change that only affects comments, documentation strings, and improves code clarity.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2864 from ammmr:chore-fix-typos 3cea9fcf6f21edb006b63e9258d2b82930dd961d
PiperOrigin-RevId: 805227784
The `agent-triage-pull-request` job will now only run if the pull request does not have the 'bot triaged' or 'google-contributor' labels, avoiding redundant and unnecessary triage actions.
PiperOrigin-RevId: 804732073
Use the A2A Python SDK for client support for A2A Remote clients. This enables A2A based agents that use gRPC or RESTful interfaces, as well as the jsonrpc support. This also simplifies creation of clients and provides simpler mechanisms to inject credentials and observability into the remote agent interactions.
PiperOrigin-RevId: 804711466
Changed default values for `session_service`, `artifact_service`, and `run_config` from instances of mutable classes to `None`. Instances are now created within the function body if the argument is not provided, preventing unexpected shared state across function calls.
PiperOrigin-RevId: 804624564
The system instructions for agent transfer now include a NOTE section that lists all agents available for the `transfer_to_agent` function. This also has the target agents and, if there is one that applies, the parent agent. New unit tests are added to verify the correct generation of this NOTE.
PiperOrigin-RevId: 804569691
Changed default values for `session_service`, `artifact_service`, and `run_config` from instances of mutable classes to `None`. Instances are now created within the function body if the argument is not provided, preventing unexpected shared state across function calls.
PiperOrigin-RevId: 804560641
Merge https://github.com/google/adk-python/pull/1629
close https://github.com/google/adk-python/issues/2170
### Summary
This PR introduces `GkeCodeExecutor`, a new code executor that provides a secure and scalable method for running LLM-generated code by leveraging GKE Sandbox. It serves as a robust alternative to local or standard containerized executors by leveraging the **GKE Sandbox** environment, which uses gVisor for workload isolation.
For each code execution request, it dynamically creates an ephemeral Kubernetes Job with a hardened Pod configuration, offering significant security benefits and ensuring that each code execution runs in a clean, isolated environment.
### Key Features of GkeCodeExecutor
* **Dynamic Job Creation**: Uses the Kubernetes `batch/v1` API to create a new Job for each code snippet.
* **Secure Code Mounting**: Injects code into the Pod via a temporary `ConfigMap`, which is mounted to a read-only file.
* **gVisor Sandboxing**: Enforces execution within a `gvisor` runtime for kernel-level isolation.
* **Hardened Security Context**: Pods run as non-root with all Linux capabilities dropped and a read-only root filesystem.
* **Resource Management**: Applies configurable CPU and memory limits to prevent abuse.
* **Automatic Cleanup**: Uses the `ttl_seconds_after_finished` feature on Jobs for robust, automatic garbage collection of completed Pods and Jobs.
* **Node Scheduling**: The executor uses Kubernetes `tolerations` in its Pod specification. This allows the k8s scheduler to place the execution Pod onto a **_pre-configured_** gVisor-enabled node.
* **Module Integration**: The `GkeCodeExecutor` is registered in the `code_executors/__init__.py`, making it available for use by agents. The `ImportError` handling is configured to check for the required `kubernetes` SDK.
### Execution Flow:
1. Agent invokes `GkeCodeExecutor` with the LLM-generated code.
2. The `GkeCodeExecutor` will `execute_code` – creates a temporary `ConfigMap`, and then create a k8s `Job` to run it.
3. This Job runs a standard `python:3.11-slim` container. The image is pulled once to the node and cached. The Job will mount the ConfigMap as `/app/code.py`
4. The GkeCodeExecutor will monitor the Job to completion, fetch `stdout/stderr` logs from the container, return `CodeExecutionResult` to the LlmAgent, and ensure all temp resources are deleted.
5. The calling agent formats the result and provides a final response to the user. If the result contains error, it will retry up to `error_retry_attempts` times.
PiperOrigin-RevId: 804511467
This includes:
- Test verifying multiple spans are written during E2E runner execution.
- Regression tests for the "ContextVar was created in a different Context" exceptions caused by the interplay of context based instrumentation and async generators getting indeterminately suspended.
PiperOrigin-RevId: 804333483
- Added `tests/unittests/apps/test_apps.py` with basic tests for `App` initialization.
- Modified `tests/unittests/test_runners.py` to include a test that verifies `Runner` raises a `ValueError` when both `app` and `app_name` are provided during initialization.
PiperOrigin-RevId: 803556826
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
The convention:
- If some fields(like plugin) are defined both at root_agent and app, then a error will be raised.
- app code should be located within agent.py.
- an instance named app should be created
PiperOrigin-RevId: 803155804
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
Before this change: `thought` flags was incorrectly removed if the current agent enables BuiltInPlanner.
After this change:
- When it's BuiltInPlanner, keep the thought flag in content history, so that model has full context of its previous thinking.
- When it's PlanReactPlanner, removes the `thought` flag in content history, so that model sees as-is when the content was generated.
PiperOrigin-RevId: 802737130
Merge https://github.com/google/adk-python/pull/2791Fixes#2789
## Summary
Forward `state_delta` from the FastAPI `/run` request to `Runner.run_async(...)`, aligning behavior with the documented
API and the `/run_sse` endpoint.
## Why
The documentation for `/run` explicitly includes:
> `state_delta` (object, optional): A delta of the state to apply before the run.
However, the non‑SSE `/run` handler did not pass this value through, so `Runner.run_async` always received `None`. The
`/run_sse` path already forwarded it correctly.
## Changes
- `src/google/adk/cli/adk_web_server.py`
- Add `state_delta=req.state_delta` to the "/run" handler’s `runner.run_async(...)` call.
- `tests/unittests/cli/test_fast_api.py`
- Add `test_agent_run_passes_state_delta` to test the fix.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2791 from pguerra-ce:fix-state-delta-missing-in-run 83eec8d28b80757e24ae900285eb59530863adbd
PiperOrigin-RevId: 802703072
The convention:
- If some fields(like plugin) are defined both at root_agent and app, then a error will be raised.
- app code should be located within agent.py.
- an instance named app should be created
PiperOrigin-RevId: 801252329
The convention:
- If some fields(like plugin) are defined both at root_agent and app, then a error will be raised.
- app code should be located within agent.py.
- an instance named app should be created
PiperOrigin-RevId: 801103084
The convention:
- If some fields(like plugin) are defined both at root_agent and app, then a error will be raised.
- app code should be located within agent.py.
- an instance named app should be created
PiperOrigin-RevId: 801084463
This will allow restricting BigQuery SQL executions to the specified project. The agent/LLM should resolve the `project_id` param for tools like `execute_sql` and sometimes they can resolve it to an unexpected value due to hallucination or ambiguity. This guardrail will protect against that situation.
PiperOrigin-RevId: 801039685
The existing `LongRunningTool` does not define a programmatic way to provide & validate structured input, also it relies on LLM to reason and parse the user's response.
For a quick start, annotate the function with `FunctionTool(my_function, require_confirmation=True)`. A more advanced flow is shown in the `human_tool_confirmation` sample.
The new flow is similar to the existing Auth flow:
- User request a tool confirmation by calling `tool_context.request_confirmation()` in the tool or `before_tool_callback`, or just using the `require_confirmation` shortcut in FunctionTool.
- User can provide custom validation logic before tool call proceeds.
- ADK creates corresponding RequestConfirmation FunctionCall Event to ask user for confirmation
- User needs to provide the expected tool confirmation to a RequestConfirmation FunctionResponse Event.
- ADK then checks the response and continues the tool call.
PiperOrigin-RevId: 801019917
Use full media types (image/jpeg, video/mp4, application/pdf) instead of suffixes (jpeg/mp4/pdf) when constructing LiteLLM payloads
This fxes compatibility with providers that validate media types (Anthropic)
Updated and added unit tests to assert full MIME types for image/video/pdf
PiperOrigin-RevId: 800685204
original tests assert too strict time boundary, now we only assert the parallel execution time should be less than sequential execution time
PiperOrigin-RevId: 800563929
The transcription change breaks the multi-agent transfer during live/bidi.
Updates `GeminiLlmConnection` to populate the `content` field of `LlmResponse` with `types.Content` and `types.Part` objects for both input and output transcriptions, instead of using dedicated transcription fields. Also removes a debug print from `audio_cache_manager.py`.
the transcription is not fully ready to be used yet so roll back the transcription change.
PiperOrigin-RevId: 799851950
- Keep original class names for backward-compatibility.
- Log warning if user instantiate the classes with original names.
- New names are more aligned with MCP SDKs convention.
PiperOrigin-RevId: 799777320
Merge https://github.com/google/adk-python/pull/2563
Currently in adk deploy cloud_run or gke, the dockerfile copies the agent code after the file permission is set. This can lead to file permission not being set correctly for the container to open and read the file.
This PR will make sure the files are copied with the permission of the user that is set in the container.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2563 from moficodes:deploy-docker d7f6df4d893af75b360e6d96ffd2640ce6076ca2
PiperOrigin-RevId: 799371070
Merge https://github.com/google/adk-python/pull/2544
The command `adk deploy cloud_run` supports limited `gcloud run deploy` args 😢.
Which makes the command fine for simple deployments...
It should support all current and future Cloud Run deployment args for the command to be widely adopted.
This can easily be done by passing through all extra args passed to `adk deploy cloud_run` to gcloud...
This PR assumes any extra args/flags passed after `AGENT_PATH` are gcloud flags.
## Example
```sh
# ADK flags
adk deploy cloud_run \
--project=$GOOGLE_CLOUD_PROJECT \
--region=$GOOGLE_CLOUD_LOCATION \
$AGENT_PATH \
# Use the -- separator for gcloud args
-- \
--min-instances=2 \
--no-allow-unauthenticated
```
This gives full Cloud Run feature support to ADK users 🤖🚀
## Test Plan
To test you can just build locally or pip install feature branch directly:
```
uv venv
uv pip install git+https://github.com/jackwotherspoon/adk-python.git
```
Deploy to Cloud Run using additional arguments following `AGENT_PATH`, such as `--min-instance=2` or `--description="Cloud Run test"`:
```sh
uv run adk deploy cloud_run \
--project=$GOOGLE_CLOUD_PROJECT \
--region=$GOOGLE_CLOUD_LOCATION \
--with_ui \
$AGENT_PATH \
-- \
--labels=test-label=adk \
--min-instances=2
```
You can click on the Cloud Run service after deployment and check the service yaml, you should see the additional label etc.
<img width="1612" height="622" alt="image" src="https://github.com/user-attachments/assets/596a260a-0052-460b-9642-c18900ccf7c9" />
Fixes https://github.com/google/adk-python/issues/2351
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2544 from jackwotherspoon:main 184a4d73f8dbe6f565ff92cf1c1fe69bb163de5e
PiperOrigin-RevId: 799252544
Merge https://github.com/google/adk-python/pull/2409
Description:
This PR Fixes: #2407
The AgentTool in /google/adk/tools/agent_tool.py uses a hardcoded user_id='tmp_user' when creating a new session for the agent it wraps. This happens within the run_async method.
code snippet
... @override async def run_async( self, *, args: dict[str, Any], tool_context: ToolContext, ) -> Any: ... session = await runner.session_service.create_session( app_name=self.agent.name, user_id='tmp_user', # <-- This is hardcoded state=tool_context.state.to_dict(), ) ...
Why is this a problem?
This hardcoding breaks the chain of user identity. When a parent agent calls a sub-agent via the AgentTool, the original user_id is lost. Any tool or logic inside the sub-agent that needs to perform user-specific actions (e.g., accessing user data from a database, retrieving user-specific memory, checking permissions) will fail or operate on the wrong context because it receives 'tmp_user' instead of the actual user's ID.
Impact:
This prevents the creation of robust, multi-agent applications where user context must be maintained across different agents and tools. It limits the utility of AgentTool to only stateless sub-agents that do not require user-specific information.
Suggested Fix:
The user_id should be retrieved from the parent context, which is available via the tool_context parameter passed into run_async. The create_session call should be updated to use the dynamic user_id from the parent session.For example, the fix might involve accessing the user ID from the tool_context.
code-snippet
session = await runner.session_service.create_session( app_name=self.agent.name, user_id=tool_context._invocation_context.user_id, state=tool_context.state.to_dict(), )
To Reproduce
Steps to reproduce the behavior:
To reproduce this bug, we need to set up a two-agent system: a ParentAgent that calls a ChildAgent using the AgentTool. The ChildAgent will have a tool designed to simply return the user_id it receives from its context.
Expected behavior
It should return the user_id of the user calling the agent,
but, in current situation we are getting tmp_user
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2409 from akshaypachpute-1998:fix-issue-2407-agent-tool-context-propogation 0c3e8656fdf11386e3ab13a3a1f2df99a396dbd1
PiperOrigin-RevId: 798315832
Merge https://github.com/google/adk-python/pull/2641
This PR adds a custom `User-Agent` header to all requests made via `RestApiTool`. This allows backend services to identify traffic originating from the ADK. The header format is `google-adk/<version> (tool: <tool_name>)`, where `<version>` is the current version of the `google-adk` package, fetched dynamically from `google.adk.version`, and `<tool_name>` is the name of the specific `RestApiTool` instance.
**Associated Issue**
Fixes#2676
**Testing Plan**
**Unit Tests**
I ran the full suite of unit tests locally to ensure the changes did not introduce any regressions. All tests passed successfully.
```bash
$ pytest ./tests/unittests
================================= 4202 passed, 2459 warnings in 44.68s ====================
```
The warnings are related to existing experimental features and are not affected by this change.
**Manual End-to-End (E2E) Test**
I performed a manual test to ensure the integrated flow works as expected.
* **Setup:** Created a clean virtual environment (`~/venvs/adk-e2e-test`) and installed the locally built `google-adk` package using `uv venv --seed` and `pip install dist/google_adk-*.whl`. Ran a custom `mock_server.py` script (using Flask) in one terminal to listen for requests and print headers. Ran a custom `run_test_tool.py` script in a second terminal to send a request using the modified `RestApiTool`.
* **Execution:** The `run_test_tool.py` script successfully sent a POST request to the mock server's `/test` endpoint. The mock server received the request and printed the headers.
**`run_test_tool.py` Output:**
```json
{
"message": "Request received successfully!"
}
```
**`mock_server.py` Output (Headers):**
```text
--- Request Received ---
Headers:
Host: 127.0.0.1:8000
User-Agent: google-adk/1.12.0 (tool: TestTool)
Accept-Encoding: gzip, deflate
Accept: */*
Connection: keep-alive
Content-Type: application/json
Content-Length: 2
------------------------
```
The `User-Agent: google-adk/1.12.0 (tool: TestTool)` header confirms the change is working as intended, dynamically fetching the package version and including the tool name.
**Checklist**
* [x] Associated issue linked
* [x] Unit tests passed
* [x] End-to-end test performed and results documented
* [x] Code formatted with `./autoformat.sh`
---
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2641 from rcsantana777:feat/add-user-agent a9a9375306c18bb7ba501276cbf76693e70a87ad
PiperOrigin-RevId: 798232385
So far we had a default docstring for the `execute-sql` tool and for non-default write modes we were concatenating more content to it. This was working fine in Python 3.9-3.12 but broke in Python 3.13 because of a nuanced difference in the string concatenation to the `__doc__` property of a function b/433914562#comment4. This change makes the docstring management more robust and readable.
PiperOrigin-RevId: 797843736
This can help to provide more context and information about the table, like parent-child relationship, and row deletion policy etc.
PiperOrigin-RevId: 797562858
Introduce `DynamicPickleType` to handle session actions, using sqlalchemy-spanner `SpannerPickleType` when the database dialect is Spanner.
Connects to a Spanner database to store session data persistently in tables.
# Example using Spanner database:
`session_service = DatabaseSessionService(db_url="spanner+spanner:///projects/project-id/instances/instance-id/databases/database-id")`
# Example adk web command:
`adk web --session_service_uri="spanner+spanner:///projects/project-id/instances/instance-id/databases/database-id"`
PiperOrigin-RevId: 797416610
Merge https://github.com/google/adk-python/pull/2212
This PR closes issue #2202
ADK was not parsing the required attribute when using LiteLLM, letting the LLM decide what is required vs not, not respecting function definitions.
## Test Plan
There's a fork of adk-python that is being running live for over 2 weeks in our production environment with millions of requests per day.
Below you can find a screenshot of the unit tests passing. I've also added one change to the test cases to cover this scenario
<img width="1904" height="483" alt="image" src="https://github.com/user-attachments/assets/5a6eb069-63ae-45a3-baca-6b01543f56fb" />
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2212 from thiagosalvatore:main 7de4037d8016389313f3fb22df40c12bac578523
PiperOrigin-RevId: 797393698
VertexAiCodeExecutor and ContainerCodeExecutor request additional dependencies, lazy load them so that when users import other names, they won't get impacted if they don't had those additional dependencies installed.
Original codes swallow the exception and log a debug log is wrong and awkward, should follow the same style as what we did in https://github.com/google/adk-python/blob/main/src/google/adk/tools/retrieval/__init__.py
PiperOrigin-RevId: 796969332
Checked with local wheel and worked as intended. The harness shows suppression works: 0 warnings for all true-like values.
This CL adds ADK_DISABLE_EXPERIMENTAL_WARNING to let the users to suppress warning messages from features decorated with @experimental.
Previously, using experimental features would always trigger a UserWarning. This change creates a way to disable these warnings, which can be good to stop flooding logs.
The warning is suppressed if ADK_DISABLE_EXPERIMENTAL_WARNING is set to a truthy value such as "true", "1", "yes", or "on" (case-insensitive).
Added unit tests to make sure:
Warning suppression for functions and classes when the env var is set.
Case-insensitivity and various truthy values for the env var.
Loading the env var from a .env file.
PiperOrigin-RevId: 796649404
* feat: adding build image to deploy cloud_run options
Gives the ability for a user to set the build image for the deployment step to Cloud Run. Currently it is hard coded to python:3.11-slim, and this is still the default, but this allows that value to be overriden.
* fix: applied formatting scripts
testing:
Added tests to ensure the behavior of the cli remains consistent with when used or omitted.
* chore: next time run the formatter before you commit.
---------
Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com>
This change keeps dependencies to their major versions, but for the pre 1.0.0 releases pinned to minor releases as they could have breaking changes.
PyYAML: Version 7.0 is in development and could have breaking changes
absolufy-imports: The package is archived and no longer maintained.
anyio: Follows SemVer, so version 5.0 could have breaking changes.
authlib: Follows a SemVer-like pattern, so version 2.0 could have breaking changes.
click: A mature library, version 9.0 is the expected boundary for breaking changes.
fastapi: As a pre-1.0 library, the next minor release could have breaking changes.
google-api-python-client: Although in maintenance, version 3.0 could still have breaking changes.
google-cloud-aiplatform: Follows SemVer, so breaking changes are expected in version 2.0.
google-cloud-secret-manager: A stable Google library, version 3.0 could lead to breaking changes.
google-cloud-speech: A stable Google library, version 3.0 could lead to breaking changes.
google-cloud-storage: A stable Google library.
google-genai: As an official Google SDK, version 2.0 is the expected boundary for breaking changes.
graphviz: As a pre-1.0 library, the next minor release could have breaking changes.
mcp: As an SDK, version 2.0 is the boundary for potential breaking changes.
opentelemetry-api: Follows SemVer; version 2.0 could have breaking changes.
opentelemetry-exporter-gcp-trace: It's tied to the v1 OpenTelemetry API, so version 2.0 would likely be a breaking change.
opentelemetry-sdk: It's coupled to the v1 API, so version 2.0 would likely be a breaking change.
pydantic: This stops upgrades to the incompatible version 3.0 after its major 2.0 rewrite.
python-dateutil: Follows to SemVer, making version 3.0 the boundary for potential breaking changes.
python-dotenv: Locks to stable version 1.0 major release.
requests: As a more mature library, version 3.0 is boundary for breaking changes.
sqlalchemy: This locks the dependency to the stable version 2.0 API after its major rewrite.
starlette: As a pre-1.0 library, the next minor release could have breaking changes.
tenacity: This is a mature library, version 9.0 is boundary for breaking changes.
typing-extensions: Major versions are tied to large typing changes in Python itself.
tzlocal: The library has a history of introducing breaking API changes between major versions.
uvicorn: As a pre-1.0 library, the next minor release could have breaking changes.
watchdog: As a stable library, version 7.0 could have breaking changes.
websockets: Follows SemVer, so version 16.0 is the boundary for breaking changes.
PiperOrigin-RevId: 794677571
Verified with uv on Python 3.12 and 3.13 using the same ignores as CI.
3.12:
uv python install 3.12 && uv venv --python 3.12 .venv && source .venv/bin/activate
uv sync --extra test --extra eval --extra a2a
python -m pytest tests/unittests --ignore=tests/unittests/artifacts/test_artifact_service.py --ignore=tests/unittests/tools/google_api_tool/test_googleapi_to_openapi_converter.py
3.13: repeated the above with 3.13 (separate env)
Result: All unit tests passed
PiperOrigin-RevId: 794669437
Corrects a typo in the `StreamableHTTPConnectionParams` docstring, changing "SSE" to "Streamable HTTP" to accurately reflect the referenced client.
PiperOrigin-RevId: 794424727
For Vertex model backend, we send response back. This doesn't work for streaming tools that the return type is AsyncGenerator. So the fix here is to ignore the return type when it's AsyncGenerator.
We can't distinguish streaming vs non-streaming tool with AsyncGenerator though as LiveRequestQueue is optional in streaming tool.
Adds an `ignore_response` option to `build_function_declaration` to skip including the return type in the function declaration. This is enabled for tools that return `AsyncGenerator`, as the model does not yet support understanding these return types, while streaming tools can still handle them. Also, removes redundant return statements in `_get_mandatory_params`.
PiperOrigin-RevId: 794392846
This is to address the name conflict issue of tools returned by different toolset. Mainly it's to give each toolset a namespace.
We have a flag `add_tool_name_prefix` to decide whether to apply this behavior
We have a `tool_name_prefix` to let client specify a custom prefix, if not set , toolset name will be used as prefix.
PiperOrigin-RevId: 794306796
Merge https://github.com/google/adk-python/pull/1815
fix: path parameter extraction for complex Google API endpoints
- Fix GoogleApiToOpenApiConverter to handle path parameters in complex endpoints like /v1/documents/{documentId}:batchUpdate
- Use Google Discovery Document 'location' field
- Add comprehensive test suite for Google Docs batchUpdate functionality
- Verify parameter location handling for complex endpoint patterns
- Test schema validation for BatchUpdateDocumentRequest/Response
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/1815 from goldylocks87:fix-issue-1814-path-parameter-extraction af5508ec6975b1ccbc34931a0041e422ee259c16
PiperOrigin-RevId: 794301898
Spanner toolset support basic operations to interact with Spanner table metadata and query results.
Consolidate BigQueryTool into generic GoogleTool, so that BigQueryToolset and SpannerToolset can share.
PiperOrigin-RevId: 794259782
The issue was caused by a breaking change in Python 3.13's inspect.cleandoc() function that made it more conservative about whitespace handling:
Root Cause:
- Python 3.12-: inspect.cleandoc() used line.lstrip() - strips all whitespace (spaces, tabs, etc.)
- Python 3.13+: inspect.cleandoc() uses line.lstrip(' ') - strips only space characters
PiperOrigin-RevId: 793921360
Demonstrates intelligent triage system with root planning agent and parallel
execution agents.
Use session state to store the execution plan and coordinate with other specialized agents.
Check out README.md for more details.
PiperOrigin-RevId: 793884758
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
The connection_params argument in the constructor is split into four arguments in the config class because some of them have identical fields. In order to identify which is which, a separate name is more convenient.
PiperOrigin-RevId: 791965995
original codes try to eagerly import VertexAiRagRetrieval while it doesn't want to raise error if client try to import other names in this package and dependencies of VertexAiRagRetrieval is missing. so it swallow the import error which doesn't make sense, given vertex sdk is a must have for VertexAiRagRetrieval, we should fail fast.
this fix achieve the same purpose but fail fast if client try to import VertexAiRagRetrieval from this package and miss certain dependencies (e.g. vertex sdk)
PiperOrigin-RevId: 791759776
Previous implementation doesn't pass the actual handle to server. Now we cache the handle and pass it over when reconnection happens.
To enable:
run_config = RunConfig(
session_resumption=types.SessionResumptionConfig(transparent=True)
)
PiperOrigin-RevId: 791308462
1. given we are running parallel functions in one event loop (one thread) , we should use async lock instead of thread lock
2. test three kind of functions:
a. sync function
b. async function that doesn't yield
c. async function that yield
PiperOrigin-RevId: 791255012
Merge https://github.com/google/adk-python/pull/2327
`adk run --help` (adk 1.9.0)
```
--replay FILE The json file that contains the initial state of the
session and user queries. A new session will be created
using this state. And user queries are run againt the
newly created session. Users cannot continue to interact
with the agent.
```
```
$ git grep againt
src/google/adk/cli/cli_tools_click.py: " queries are run againt the newly created session. Users cannot"
```
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2327 from ftnext:fix-typo-run-replay-help 77cae65a235d9119810fe3d209910562672713c8
PiperOrigin-RevId: 790872246
1. if a function has no return type annotation, we should treat it as returning any type
2. we use empty schema (with `type` as None) to indicate no type constraints and this is already supported by model server
PiperOrigin-RevId: 789808104
@@ -17,6 +17,8 @@ Please refer to [ADK Project Overview and Architecture](https://github.com/googl
- ADK live related configs are in [run_config.py](https://github.com/google/adk-python/blob/main/src/google/adk/agents/run_config.py).
- ADK live under multi-agent scenario: we convert the audio into text. This text will be passed to next agent as context.
- Most logics are in [base_llm_flow.py](https://github.com/google/adk-python/blob/main/src/google/adk/flows/llm_flows/base_llm_flow.py) and [gemini_llm_connection.py](https://github.com/google/adk-python/blob/main/src/google/adk/models/gemini_llm_connection.py).
- Input transcription and output transcription should be added to session as Event.
- User audio or model audio should be saved into artifacts with a reference in Event to it.
- Tests are in [tests/unittests/streaming](https://github.com/google/adk-python/tree/main/tests/unittests/streaming).
* Support Oauth2 client credentials grant type ([5c6cdcd](https://github.com/google/adk-python/commit/5c6cdcd197a6780fc86d9183fa208f78c8a975d9))
* Add `ReflectRetryToolPlugin` to reflect from errors and retry with different arguments when tool errors ([e55b894](https://github.com/google/adk-python/commit/e55b8946d6a2e01aaf018d6a79d11d13c5286152))
* Support using `VertexAiSearchTool` built-in tool with other tools in the same agent ([4485379](https://github.com/google/adk-python/commit/4485379a049a5c84583a43c85d444ea1f1ba6f12))
* Support using google search built-in tool with other tools in the same agent ([d3148da](https://github.com/google/adk-python/commit/d3148dacc97f0a9a39b6d7a9640f7b7b0d6f9a6c))
* Add Rubric based tool use metric ([c984b9e](https://github.com/google/adk-python/commit/c984b9e5529b48fff64865a8b805e7e93942ea53))
* **[UI]**
* Adds `adk web` options for custom logo ([822efe0](https://github.com/google/adk-python/commit/822efe00659607bad2d19ec9a2d14c649fca2d8d))
* **[Observability]**
* **otel:** Switch CloudTraceSpanExporter to telemetry.googleapis.com ([bd76b46](https://github.com/google/adk-python/commit/bd76b46ce296409d929ae69c5c43347c73e7b365))
### Bug Fixes
* Adapt to new computer use tool name in genai sdk 1.41.0 ([c6dd444](https://github.com/google/adk-python/commit/c6dd444fc947571d089b784fde3a81e17b10cf28))
* Add AuthConfig json serialization in vertex ai session service ([636def3](https://github.com/google/adk-python/commit/636def3687a85e274e3ab44d906f6d92d49e84c0))
* Added more agent instructions for doc content changes ([7459962](https://github.com/google/adk-python/commit/745996212db156878554386be34f58658482e687))
* Convert argument to pydantic model when tool declares it accepts pydantic model as argument ([571c802](https://github.com/google/adk-python/commit/571c802fbaa80b3e65f9ce2db772b9db5a13dbc4))
* Do not re-create `App` object when loader returns an `App` ([d5c46e4](https://github.com/google/adk-python/commit/d5c46e496009eb55d78637f47162df7fcaf3a7ac))
* Fix the instruction in workflow_triage example agent ([8f3ca03](https://github.com/google/adk-python/commit/8f3ca0359e5b1306c1395770759a74aa48a52347))
* Fixes a bug that causes intermittent `pydantic` validation errors when uploading files ([e680063](https://github.com/google/adk-python/commit/e68006386fdd0da98feb9c3dce9322e44a9c914d))
* Handle A2A Task Status Update Event when streaming in remote_a2a_agent ([a5cf80b](https://github.com/google/adk-python/commit/a5cf80b952887c07bb1d56b7bdec28808edcc4a9))
* Make compactor optional in Events Compaction Config and add a default ([3f4bd67](https://github.com/google/adk-python/commit/3f4bd67b49cd60e6a2e43ccd5192efe450a6e009))
* Rename SlidingWindowCompactor to LlmEventSummarizer and refine its docstring ([f1abdb1](https://github.com/google/adk-python/commit/f1abdb1938e474564a3a76279a1a0a511f74a750))
* Rollback compaction handling from _get_contents ([84f2f41](https://github.com/google/adk-python/commit/84f2f417f77ead3748c5bbeac7f144164b9a9416))
* Set `max_output_tokens` for the agent builder ([2e2d61b](https://github.com/google/adk-python/commit/2e2d61b6fecb90cd474d6f51255678ff74b67a9b))
* Set default response modality to AUDIO in run_session ([68402bd](https://github.com/google/adk-python/commit/68402bda49083f2d56f8e8488fe13aa58b3bc18c))
* Update remote_a2a_agent to better handle streaming events and avoid duplicate responses ([8e5f361](https://github.com/google/adk-python/commit/8e5f36126498f751171bb2639c7f5a9e7dca2558))
* Update the load_artifacts tool so that the model can reliably call it for follow up questions about the same artifact ([238472d](https://github.com/google/adk-python/commit/238472d083b5aa67551bde733fc47826ff062679))
* Handle `App` instances returned by `agent_loader.load_agent` ([847df16](https://github.com/google/adk-python/commit/847df1638cbf1686aa43e8e094121d4e23e40245))
### Improvements
* Migrate VertexAiSessionService to use Agent Engine SDK ([90d4c19](https://github.com/google/adk-python/commit/90d4c19c5115c7af361effa8e12c248225ccf6ab))
* Migrate VertexAiMemoryBankService to use Agent Engine SDK ([d1efc84](https://github.com/google/adk-python/commit/d1efc8461e82fc31df940b701f1d1b5422214296), [97b950b](https://github.com/google/adk-python/commit/97b950b36b9c16467f0f42216b2dc8395346d7fe), [83fd045](https://github.com/google/adk-python/commit/83fd0457188decdabeae58b4e8be25daa89f2943))
* Add support for resolving $ref and $defs in OpenAPI schemas ([a239716](https://github.com/google/adk-python/commit/a239716930c72a0dbd2ccabeea69be46110ca48d))
* Adding the ContextFilterPlugin ([a06bf27](https://github.com/google/adk-python/commit/a06bf278cbc89f521c187ed51b032d82ffdafe2d))
* Adds plugin to save artifacts for issue [#2176](https://github.com/google/adk-python/issues/2176) ([657369c](https://github.com/google/adk-python/commit/657369cffe142ef3745cd5950d0d24a49f42f7fd))
* Expose log probs of candidates in LlmResponse ([f7bd3c1](https://github.com/google/adk-python/commit/f7bd3c111c211e880d7c1954dd4508b952704c68))
* **[Context Caching]**
* Support context caching ([c66245a](https://github.com/google/adk-python/commit/c66245a3b80192c16cb67ee3194f82c9a7c901e5))
- Support explicit context caching auto creation and lifecycle management.
* Add --otel_to_cloud experimental support ([1ae0b82](https://github.com/google/adk-python/commit/1ae0b82f5602a57ad1ca975ca0b7c85003d1a28a), [b131268](https://github.com/google/adk-python/commit/b1312680f4ea9f21c3246a1d24392619643d71f5), [7870480](https://github.com/google/adk-python/commit/7870480c63bb4fc08cfb3cabc0e1f0458f0e85bd))
* Add GenAI Instrumentation if --otel_to_cloud is enabled ([cee365a](https://github.com/google/adk-python/commit/cee365a13d0d1b1f2be046c1cc29e24a8d1fdbcc))
* Support standard OTel env variables for exporter endpoints ([f157b2e](https://github.com/google/adk-python/commit/f157b2ee4caf4055e78f4657254e45913895f5de))
* Temporarily disable Cloud Monitoring integration in --otel_to_cloud ([3b80337](https://github.com/google/adk-python/commit/3b80337faf427460e4743e25dbb92578f823513f))
* **[Services]**
* Add endpoint to generate memory from session ([2595824](https://github.com/google/adk-python/commit/25958242db890b4d2aac8612f7f7cfbb561727fa))
* **[Tools]**
* Add Google Maps Grounding Tool to ADK ([6b49391](https://github.com/google/adk-python/commit/6b493915469ecb42068e24818ab547b0856e4709))
* **MCP:** Initialize tool_name_prefix in MCPToolse ([86dea5b](https://github.com/google/adk-python/commit/86dea5b53ac305367283b7e353b60d0f4515be3b))
* **[Evals]**
* Data model for storing App Details and data model for steps ([01923a9](https://github.com/google/adk-python/commit/01923a9227895906ca8ae32712d65b178e2cd7d5))
* Adds Rubric based final response evaluator ([5a485b0](https://github.com/google/adk-python/commit/5a485b01cd64cb49735e13ebd5e7fa3da02cd85f))
* Populate AppDetails to each Invocation ([d486795](https://github.com/google/adk-python/commit/d48679582de91050ca9c5106402319be9a8ae7e8))
* **[Samples]**
* Make the bigquery sample agent run with ADC out-of-the-box ([10cf377](https://github.com/google/adk-python/commit/10cf37749417856e394e62896231e41b13420f18))
### Bug Fixes
* Close runners after running eval ([86ee6e3](https://github.com/google/adk-python/commit/86ee6e3fa3690148d60358fc3dacb0e0ab40942b))
* Filter out thought parts when saving agent output to state ([632bf8b](https://github.com/google/adk-python/commit/632bf8b0bcf18ff4e4505e4e5f4c626510f366a2))
* Ignore empty function chunk in LiteLlm streaming response ([8a92fd1](https://github.com/google/adk-python/commit/8a92fd18b600da596c22fd80c6148511a136dfd0))
* Introduces a `raw_mcp_tool` method in `McpTool` to provide direct access to the underlying MCP tool ([6158075](https://github.com/google/adk-python/commit/6158075a657f8fe0835679e509face6191905403))
* Make a copy of the `columns` instead of modifying it in place ([aef1ee9](https://github.com/google/adk-python/commit/aef1ee97a55a310f3959d475b8d7d6bc3915ae48))
* Prevent escaping of Latin characters in LLM response ([c9ea80a](https://github.com/google/adk-python/commit/c9ea80af28e586c9cc1f643b365cdba82f80c700))
* Retain the consumers and transport registry when recreating the ClientFactory in remote_a2a_agent.py ([6bd33e1](https://github.com/google/adk-python/commit/6bd33e1be36f741a6ed0514197550f9f336262ed))
* Remove unsupported 'type': 'unknown' in test_common.py for fastapi 0.117.1 ([3745221](https://github.com/google/adk-python/commit/374522197fa6843f786bfd12d17ce0fc20461dfd))
### Documentation
* Correct the documentation of `after_agent_callback` ([b9735b2](https://github.com/google/adk-python/commit/b9735b2193267645781b268231d63c23c6fec654))
* Allow users to pass their own agent card to to_a2a method [a1679da](https://github.com/google/adk-python/commit/a1679dae3fef70f1231afba3e97d45b59c314ae3)
* Allow custom part converters in A2A classes [b05fef9](https://github.com/google/adk-python/commit/b05fef9ba71f95ab2658eb4eb5608c141d49f82f)
* **[Tools]**
* Allow setting agent/application name and compute project for BigQuery tools [11a2ffe](https://github.com/google/adk-python/commit/11a2ffe35adbae977b49ceccf0e76e20c6dc90b6)
* Add GkeCodeExecutor for sandboxed code execution on GKE [72ff9c6](https://github.com/google/adk-python/commit/72ff9c64a291aebb50b07446378f375e58882c4e)
* Add a tool confirmation flow that can guard tool execution with explicit confirmation and custom input [a17bcbb](https://github.com/google/adk-python/commit/a17bcbb2aa0f5c6aca460db96ed1cb7dd86fef84)
* Add audience and prompt as configurable for OAuth flows [edda922](https://github.com/google/adk-python/commit/edda922791f15ac37830ed95ebf76b9f836d9db4)
* Allow user specify embedding model for file retrieval [67f23df](https://github.com/google/adk-python/commit/67f23df25ad47aff3cb36d0fc9ce2c9b97bde09b)
* **[Core]**
* Allow all possible values for `agent_class` field in all Agent Configs [3bc2d77](https://github.com/google/adk-python/commit/3bc2d77b4d180e9c42b30d4d1ce580aa75abe501)
* Allow agent loader to load built-in agents from special directories in adk folder [578fad7](https://github.com/google/adk-python/commit/578fad7034a7b369a490ad0afa4dd2820463c22d)
* Upgrade ADK runner to use App in addition to root_agent [4df79dd](https://github.com/google/adk-python/commit/4df79dd5c92d96096d031b26470458d0bca79a79)
* Allow inject artifact into instructions [bb4cfde](https://github.com/google/adk-python/commit/bb4cfdec12370955d4038d6d8c86e04691f2308e)
* **[Misc]** Create an initial ADK release analyzer agent to find the doc updates needed between releases [e3422c6](https://github.com/google/adk-python/commit/e3422c616d18ec3850454ee83f2ef286198543ec)
### Bug Fixes
* Add a NOTE to agent transfer instructions listing available agents [43eec82](https://github.com/google/adk-python/commit/43eec82f8444c19455089655ee288200ec966577)
* Fix pagination of list_sessions in VertexAiSessionService [e63fe0c](https://github.com/google/adk-python/commit/e63fe0c0eb73ac6e22d975387dd2df3d2ba3f521)
* Fix AttributeError and indentation in parameter processing of LiteLlm [1e23652](https://github.com/google/adk-python/commit/1e23652968164c5fdfa5564e966e78799237d94b)
* Allow AgentTool to inherit/use plugins from its invocation context when running [1979dcf](https://github.com/google/adk-python/commit/1979dcf496be3fb75fa2063fc96f480bedeb5de2)
* Enforce foreign key constraint for SQLite DB [0c87907](https://github.com/google/adk-python/commit/0c87907bcb2e5687a4ad08bab450fc888a5b5233)
* Add back installing requirements.txt to Dockerfile template for cloud run [8e43f0d](https://github.com/google/adk-python/commit/8e43f0dd8321ea31d6ad970ad4402feb48cdbd3d)
* Only process the auth responses in the last event with content (if applicable i.e. it's authored by user) [3b922a2](https://github.com/google/adk-python/commit/3b922a2f6da373b0de78b022db5d5bcb5453379f)
* Extract a utility for aggregating partial streaming responses and emitting LlmResponses for them as needed [7975e8e](https://github.com/google/adk-python/commit/7975e8e1961c8e375e2af3506ea546580ff7e45d)
* Support saving text artifacts in GCS artifact service [cecf7e8](https://github.com/google/adk-python/commit/cecf7e805d19d20e940319a6e16bfc9015ead202)
* Fixes `thought` handling in contents.py and refactors its unit tests [a30851e](https://github.com/google/adk-python/commit/a30851ee16114103dca7b9736e79cb31e82ee4d8)
* Fixes the `thought` field handling in _planning.py [fe8b37b](https://github.com/google/adk-python/commit/fe8b37b0d3046a9c0dd90e8ddca2940c28d1a93f)
* Pass state_delta to runner in /run endpoint [a3410fa](https://github.com/google/adk-python/commit/a3410fab7b25cc0e9c5908e23a087b501466df76)
* Fix discussion answering github action workflow to escape the quote in the discussion content JSON [43c9681](https://github.com/google/adk-python/commit/43c96811da891a5b0c9cf1be525665e65f346a13)
* Send full MIME types for image/video/pdf in get_content [e45c3be](https://github.com/google/adk-python/commit/e45c3be23895b5ec68908ad9ee19bd622dcbd003)
* Fix flaky unit tests: tests/unittests/flows/llm_flows/test_functions_simple.py [b92b288](https://github.com/google/adk-python/commit/b92b288c978a9b3d1a76c8bcb96cc8f439ce610b)
* Make UT of a2a consistent about how tests should be skipped when python verison < 3.10 [98b0426](https://github.com/google/adk-python/commit/98b0426cd2dc5e28014ead22b22dbf50d42d0a9a)
* Skip PR triage for already triaged or Google-contributor PRs [78eea1a](https://github.com/google/adk-python/commit/78eea1aa550790097a1005237acaec56309cd61e)
* Avoid mutable default arguments in `local_eval_service` and `runners` [64f11a6](https://github.com/google/adk-python/commit/64f11a6a67e7042768270c5587e87528c358bd06)
* Avoid mutable default arguments in `local_eval_service` and `runners` [5b465fd](https://github.com/google/adk-python/commit/5b465fd71b601a2a1ab95a74f7c9ddafe09085e5)
* Reorder dependencies in `pyproject.toml` [ca5f7f1](https://github.com/google/adk-python/commit/ca5f7f1ff0afb2b3c2457fb9efdf029dcf7494b7)
* Follow pydantic convention to make field_validator a public method [1448406](https://github.com/google/adk-python/commit/14484065c64396cebc4a1dde84d6b8b51439b990)
* Update comment to clarify `after_run` callbacks [7720616](https://github.com/google/adk-python/commit/7720616c5f1dc302f019c348a6dfa70d1cf0b135)
* Tune instructions to not ask root directory if it's already provided in the context [25df6c2](https://github.com/google/adk-python/commit/25df6c22d5942ead3a329f90ed2c10b374051ae6)
* Load discussion data from event content to avoid additional GraphQL API call [a503a0c](https://github.com/google/adk-python/commit/a503a0c807e50ec9dde7d5095f8e020861d1375d)
* Refactor discussion answering agent to merge answer_discussions.py into main.py [408d3df](https://github.com/google/adk-python/commit/408d3dfeb1475da343a15ae13e9b128985460a5d)
* Add community repo dependency group to pyproject toml [7b077ac](https://github.com/google/adk-python/commit/7b077ac3517f2b88d1bc4b732815ca766c791168)
* Add warning for using Gemini models via LiteLLM [9291daa](https://github.com/google/adk-python/commit/9291daaa8e399ca052f5a52dbb600d719dcc9fa8)
### Documentation
* Update root_agent description for clarity [467df1a](https://github.com/google/adk-python/commit/467df1a36f3ded1a0e324defcd94c557871c9190)
* Update the ask_data_insights docstring [aad1533](https://github.com/google/adk-python/commit/aad153322e54cc39c97e3e0bc71cbed72bcab477)
* Add unit tests for the `App` class and improve `Runner` initialization tests [fc90ce9](https://github.com/google/adk-python/commit/fc90ce968f114f84b14829f8117797a4c256d710)
### Chores
* Use lazy % formatting in logging functions to fix pylint warnings [b431072](https://github.com/google/adk-python/commit/b4310727d90421a81a8afc47e3c344646ee7aee8)
* Update release cadence in README [decc19b](https://github.com/google/adk-python/commit/decc19b188fbf097995824f9ad7b7be1263b6338)
* Add `custom_metadata` to DatabaseSessionService [fb009d8](https://github.com/google/adk-python/commit/fb009d8ea672bbbef4753e4cd25229dbebd0ff8d)
* Update create_session endpoint to use Request message as post body [219815d](https://github.com/google/adk-python/commit/219815d2d7f45ac0cff28265f23fbf4f4e77163f)
## 1.13.0 (2025-08-27)
### Features
* [Tools] Add the ask_data_insights tool for natural language queries on BigQuery data [47b88d2](https://github.com/google/adk-python/commit/47b88d2b06d247a698915ebf74564dbb5d81153e)
### Bug Fixes
* Add the missing `from_config` class method in BaseToolset [2dd432c](https://github.com/google/adk-python/commit/2dd432cc1fe265a79986a28e2afb59ee2c83abb3)
* Change LlmResponse to use Content for transcriptions [3b997a0](https://github.com/google/adk-python/commit/3b997a0a07d1a2915bc64d64355f4dbabb7e0ba0)
* AgentTool returns last content, instead of the content in the last event [bcf0dda](https://github.com/google/adk-python/commit/bcf0dda8bcc221974098f3077007c9e84c63021a)
* Updating BaseAgent.clone() and LlmAgent.clone() to properly clone fields that are lists [29bb75f](https://github.com/google/adk-python/commit/29bb75f975fe0c9c9d9a7e534a9c20158e1cbe1e)
* Make tool description for bigquery `execute_sql` for various write modes self contained [167182b](https://github.com/google/adk-python/commit/167182be0163117f814c70f453d5b2e19bf474df)
* Set invocation_id and branch for event generated when both output_schema and tools are used [3f3aa7b](https://github.com/google/adk-python/commit/3f3aa7b32d63cae5750d71bc586c088427c979ea)
* Rework parallel_agent.py to always aclose async generators [826f554](https://github.com/google/adk-python/commit/826f5547890dc02e707be33a3d6a58b527dac223)
* Add table metadata info into Spanner tool `get_table_schema` and fix the key usage info [81a53b5](https://github.com/google/adk-python/commit/81a53b53d6336011187a50ae8f1544de9b2764a8)
* Fix Spanner DatabaseSessionService support [54ed079](https://github.com/google/adk-python/commit/54ed0791005350542708eb2c38f32ce8b92356bc)
* Add support for required params [c144b53](https://github.com/google/adk-python/commit/c144b5347cc459496d4fd41e0c63715ffffb4952)
* Replaced hard coded value for user_id to the value from the tool context from parent agent. [0b89f18](https://github.com/google/adk-python/commit/0b89f1882dccc1acd0ee109832053edecec04850)
### Improvements
* Allow user to specify protocol for A2A RPC URL in to_a2a utility [157f731](https://github.com/google/adk-python/commit/157f73181d123b0fddc34205dc74434fcbc43b2a)
* Passthrough extra args for `adk deploy cloud_run` as Cloud Run args [6806dea](https://github.com/google/adk-python/commit/6806deaf8811eb7f02ed958648886323aba16adb)
* Renames MCPTool and MCPToolset to McpTool and McpToolset [4c70606](https://github.com/google/adk-python/commit/4c7060612967253dae824a14c5c3f853a547469b)
* Ignore hidden files in autoformat.sh [0eb65c0](https://github.com/google/adk-python/commit/0eb65c07d52f71cf555f0c32dc34b2e4ac8cf2a2)
### Documentation
* Clean up docs in sample [a360bc2](https://github.com/google/adk-python/commit/a360bc25429bf4bef6a80da59afe30d6933a844b)
* Fixes root_agent.yaml in tool_mcp_stdio_notion_config for Agent Config sample and add README.md [2c088ac](https://github.com/google/adk-python/commit/2c088acc9b34f030537b02b45a4afd458445d15b)
* Add What's new section to README.md [ccab076](https://github.com/google/adk-python/commit/ccab076aceff917591eb3a3cc89a9f85226b832a)
## 1.12.0 (2025-08-21)
### Features
**[Agent Config]** 🌟 **NEW FEATURE**: Support using config file (YAML) to author agents in addition to python code. See the [documentation](https://google.github.io/adk-docs/agents/config/) for details.
* [Agent Config] Support deploying config agent to Agent Engine in CLI ([b3b7003](https://github.com/google/adk-python/commit/b3b70035c432670a5f0b5cdd1e9467f43b80495c))
* [Tools] Add a dedicated Bigtable toolset to provide an easier, integrated way to interact
with Bigtable for building AI Agent applications(experimental feature) ([a953807](https://github.com/google/adk-python/commit/a953807cce341425ba23e3f0a85eae58d6b0630f))
* [Tools] Support custom tool_name_prefix in auto-generated GoogleApiToolset ([a2832d5](https://github.com/google/adk-python/commit/a2832d5ac7ba5264ee91f6d5a6a0058cfe4c9e8a)) See [oauth_calendar_agent](https://github.com/google/adk-python/tree/main/contributing/samples/oauth_calendar_agent) as an example.
* [Services] Add setdefault method to the ADK State object ([77ed1f5](https://github.com/google/adk-python/commit/77ed1f5f15ed3f009547ed0e20f86d949de12ec2))
### Bug Fixes
* Lazy load VertexAiCodeExecutor and ContainerCodeExecutor ([018db79](https://github.com/google/adk-python/commit/018db79d1354f93b8328abb8416f63070b25f9f1))
* Fix the path for agent card in A2A demo ([fa64545](https://github.com/google/adk-python/commit/fa64545a9de216312a69f93126cfd37f1016c14b))
* Fix the path for agent card in A2A demo ([a117cf0](https://github.com/google/adk-python/commit/a117cf0af335c5e316ae9d61336a433052316462))
* litellm-test due to breaking change in dep library of extension extra ([004a0a0](https://github.com/google/adk-python/commit/004a0a0f2d9a4f7ae6bff42a7cad96c11a99acaf))
* Using base event's invocation id when merge multiple function response event ([279e4fe](https://github.com/google/adk-python/commit/279e4fedd0b1c0d1499c0f9a4454357af7da490e))
* Avoid crash when there is no candidates_token_count, which is Optional ([22f34e9](https://github.com/google/adk-python/commit/22f34e9d2c552fbcfa15a672ef6ff0c36fa32619))
* Fix the packaging version comparison logic in adk cli ([a2b7909](https://github.com/google/adk-python/commit/a2b7909fc36e7786a721f28e2bf75a1e86ad230d))
* Fixes SequentialAgent.config_type type hint ([8a9a271](https://github.com/google/adk-python/commit/8a9a271141678996c9b84b8c55d4b539d011391c))
* Fixes the host in the ansi bracket of adk web ([cd357bf](https://github.com/google/adk-python/commit/cd357bf5aeb01f1a6ae2a72349a73700ca9f1ed2))
* Add spanner tool name prefix ([a27927d](https://github.com/google/adk-python/commit/a27927dc8197c391c80acb8b2c23d610fba2f887))
### Improvements
* Support `ADK_SUPPRESS_EXPERIMENTAL_FEATURE_WARNINGS` as environment variable to suppress experimental warnings ([4afc9b2](https://github.com/google/adk-python/commit/4afc9b2f33d63381583cea328f97c02213611529))
* Uses pydantic `Field` for Agent configs, so that the generated AgentConfig.json json schema can carry field description ([5b999ed](https://github.com/google/adk-python/commit/5b999ed6fd23a0fc1da56ccff4c09621f433846b))
* Update `openai` dependency version, based on correct OPENAI release ([bb8ebd1](https://github.com/google/adk-python/commit/bb8ebd15f90768b518cd0e21a59b269e30d6d944))
* Add the missing license header for core_callback_config init file ([f8fd6a4](https://github.com/google/adk-python/commit/f8fd6a4f09ab520b8ecdbd8f9fe48228dbff7ebe))
* Creates yaml_utils.py in utils to allow adk dump yaml in the same style ([1fd58cb](https://github.com/google/adk-python/commit/1fd58cb3633992cd88fa7e09ca6eda0f9b34236f))
* Return explict None type for DELETE endpoints ([f03f167](https://github.com/google/adk-python/commit/f03f1677790c0a9e59b6ba6f46010d0b7b64be50))
* Add _config suffix to all yaml-based agent examples ([43f302c](https://github.com/google/adk-python/commit/43f302ce1ab53077ee8f1486d5294540678921e6))
* Rename run related method and request to align with the conventions ([ecaa7b4](https://github.com/google/adk-python/commit/ecaa7b4c9847b478c7cdc37185b1525f733bb403))
* Update models in samples/ folder to be gemini 2.0+ ([6c217ba](https://github.com/google/adk-python/commit/6c217bad828edf62b41ec06b168f8a6cb7ece2ed))
* Remove the "one commit" requirement from the contributing guide ([c32cb6e](https://github.com/google/adk-python/commit/c32cb6eef9ce320ea5a1f3845fc57b83762c237e))
* Bump version to 1.11.0 ([8005270](https://github.com/google/adk-python/commit/80052700f6cee947322080ae6c415d3a428b6c91))
* Fix core_callback example ([ba6e85e](https://github.com/google/adk-python/commit/ba6e85eb3fb06f58ce9077574eac193298e18bea))
* Adds a minimal sample to demo how to use Agent Config to create a multi-agent setup ([1328e6e](https://github.com/google/adk-python/commit/1328e6ef62e9e6260048c0078579edb85a0440bc))
* [Tools] Support adding prefix to tool names returned by toolset ([ebd726f](https://github.com/google/adk-python/commit/ebd726f1f5e0a76f383192cace4a80a83204325b))
* [Eval] Expose `print_detailed_results` param to `AgentEvaluator.evaluate` ([7e08808](https://github.com/google/adk-python/commit/7e0880869b340e9a5e0d68d6936219e64ab41212))
* [Tools] Add Spanner toolset (breaking change to BigQueryTool, consolidating into generic GoogleTool) ([1fc8d20](https://github.com/google/adk-python/commit/1fc8d20ae88451b7ed764aa86c17c3cdfaffa1cf))
* [Core] Support both output_schema and tools at the same time in LlmAgent([sample](https://github.com/google/adk-python/tree/main/contributing/samples/output_schema_with_tools)) ([af63567](https://github.com/google/adk-python/commit/af635674b5d3c128cf21737056e091646283aeb7))
### Bug Fixes
* A2A RPC URL got overriden by host and port param of adk api server ([52284b1](https://github.com/google/adk-python/commit/52284b1bae561e0d6c93c9d3240a09f210551b97))
* Aclose all async generators to fix OTel tracing context ([a30c63c](https://github.com/google/adk-python/commit/a30c63c5933a770b960b08a6e2f8bf13eece8a22))
* Use PreciseTimestamp for create and update time in database session service to improve precision ([585141e](https://github.com/google/adk-python/commit/585141e0b7dda20abb024c7164073862c8eea7ae))
* Ignore AsyncGenerator return types in function declarations ([e2518dc](https://github.com/google/adk-python/commit/e2518dc371fe77d7b30328d8d6f5f864176edeac))
* Make all subclass of BaseToolset to call parent constructor ([8c65967](https://github.com/google/adk-python/commit/8c65967cdc2dc79fa925ff49a2a8d67c2a248fa9))
* Path parameter extraction for complex Google API endpoints ([54680ed](https://github.com/google/adk-python/commit/54680edf3cac7477c281680ec988c0a207c0915d))
* Docstring concatenation in 3.13 ([88f759a](https://github.com/google/adk-python/commit/88f759a941c95beef0571f36f8e7a34f27971ba8))
* Lazy load retrieval tools and prompt users to install extensions if import failed ([9478a31](https://github.com/google/adk-python/commit/9478a31bf2257f0b668ae7eb91a10863e87c7bed))
* Incorrect logic in LlmRequest.append_tools and make BaseTool to call it ([b4ce3b1](https://github.com/google/adk-python/commit/b4ce3b12d109dd0386f4985fc4b27d5b93787532))
* Creates an InMemoryMemoryService within the EvaluationGenerator ([e4d54b6](https://github.com/google/adk-python/commit/e4d54b66b38ed334ca92c3bb1a83aca01b19e490))
* Uncomment OTel tracing in base_llm_flow.py ([9cfe433](https://github.com/google/adk-python/commit/9cfe43334ae50f814fed663cca7cbe330e663b8c))
### Improvements
* Added upper version bounds to dependencies in "pyproject.toml" ([a74d334](https://github.com/google/adk-python/commit/a74d3344bc19e587c5e9f55f3c90fa9d22c478d8))
* Update python-version in .github/workflows/python-unit-tests.yml to \["3.9", "3.10", "3.11", "3.12", "3.13"] ([ddf2e21](https://github.com/google/adk-python/commit/ddf2e2194b49667c8e91b4a6afde694474674250))
* Update comment to reference "Streamable HTTP Client" ([c52f956](https://github.com/google/adk-python/commit/c52f9564330f0c00d82338cc58df28cb22400b6f))
* Remove logging that contains full event data from DatabaseSessionService ([bb3735c](https://github.com/google/adk-python/commit/bb3735c9cab1baa1af2cc22981af3b3984ddfe15))
* Add the missing env variables in discussion_answering.yml ([a09a5e6](https://github.com/google/adk-python/commit/a09a5e67aa95cf71b51732ab445232dc4815d83d))
* Add Gemini API docs as a new datastore for the ADK Answering Agent ([5fba196](https://github.com/google/adk-python/commit/5fba1963c31eec512558325c480812ccb919a7bb))
* Add the missing license header for some sample agents' files ([7d2cb65](https://github.com/google/adk-python/commit/7d2cb654f0d64728741b5de733e572c44c8a5b04))
* Add docstring to clarify the behavior of preload memory tool ([88114d7](https://github.com/google/adk-python/commit/88114d7c739ca6a1b9bd19d40ed7160e53054a89))
* Add experimental messages for a2a related API ([d0b3b5d](https://github.com/google/adk-python/commit/d0b3b5d857d8105c689bd64204e367102a67eded))
* Make all FastAPI endpoints async ([7f12387](https://github.com/google/adk-python/commit/7f12387eb19b9335a64b80df00609c3c765480e7))
* Group FastAPI endpoints with tags ([c323de5](https://github.com/google/adk-python/commit/c323de5c692223e55372c3797e62d4752835774d))
* Allow implementations to skip defining a close method on Toolset ([944e39e](https://github.com/google/adk-python/commit/944e39ec2a7c9ad7f20c08fd66bf544de94a23d7))
* Add sample agent to test support of output_schema and tools at the same time for gemini model ([f2005a2](https://github.com/google/adk-python/commit/f2005a20267e1ee8581cb79c37aa55dc8e18c0ea))
* Add Github workflow config for uploading ADK docs to knowledge store ([5900273](https://github.com/google/adk-python/commit/59002734559d49a46940db9822b9c5f490220a8c))
* Update ADK Answering agent to reference doc site instead of adk-docs repo ([b5a8bad](https://github.com/google/adk-python/commit/b5a8bad170e271b475385dac440c7983ed207df8))
### Documentation
* Fixes tool_functions, which is a config-based sample for using tools ([c5af44c](https://github.com/google/adk-python/commit/c5af44cfc0224e2f07ddc7a649a8561e7141fcdc))
* Add workflow_triage sample for multi-agent request orchestration ([e295feb](https://github.com/google/adk-python/commit/e295feb4c67cbe8ac4425d9ae230210840378b2e))
* Add examples for config agents ([d87feb8](https://github.com/google/adk-python/commit/d87feb8ddb6a5e402c63bd3c35625160eb94e132))
* Adds pypi badge to README.md ([dc26aad](https://github.com/google/adk-python/commit/dc26aad663b6ae72223cfec9b91eaf73a636402d))
* Update StreamableHTTPConnectionParams docstring to remove SSE references ([8f937b5](https://github.com/google/adk-python/commit/8f937b517548a1ce0569f9698ea55c0a130ef221))
* [Live] Implement Live Session Resumption ([71fbc92](https://github.com/google/adk-python/commit/71fbc9275b3d74700ec410cb4155ba0cb18580b7))
* [Tool] Support parallel execution of parallel function calls ([57cd41f](https://github.com/google/adk-python/commit/57cd41f424b469fb834bb8f2777b5f7be9aa6cdf))
* [Models] Allow max tokens to be customizable in Claude ([7556ebc](https://github.com/google/adk-python/commit/7556ebc76abd3c776922c2803aed831661cf7f82))
* [Tool] Create enterprise_web_search_tool as a tool instance ([0e28d64](https://github.com/google/adk-python/commit/0e28d64712e481cfd3b964be0166f529657024f6))
### Bug Fixes
* Fix shared default plugin manager and cost manager instances among multiple invocations ([423542a](https://github.com/google/adk-python/commit/423542a43fb8316195e9f79d97f87593751bebd3))
* Correct the type annotation in anthropic_llm implementation ([97318bc](https://github.com/google/adk-python/commit/97318bcd199acdacadfe8664da3fbfc3c806cdd2))
* Fix adk deploy cloud_run cli, which was broken in v1.9.0 ([e41dbcc](https://github.com/google/adk-python/commit/e41dbccf7f610e249108f9321f60f71fe2cc10f4))
* Remove thoughts from contents in llm requests from history contents ([d620bcb](https://github.com/google/adk-python/commit/d620bcb384d3068228ea2059fb70274e68e69682))
* Annotate response type as None for transfer_to_agent tool ([86a4487](https://github.com/google/adk-python/commit/86a44873e9b2dfc7e62fa31a9ac3be57c0bbff7b))
* Fix adk cli options and method parameters mismatching ([8ef2177](https://github.com/google/adk-python/commit/8ef2177658fbfc74b1a74b0c3ea8150bae866796))
### Improvements
* Add Github workflow config for the ADK Answering agent ([8dc0c94](https://github.com/google/adk-python/commit/8dc0c949afb9024738ff7ac1b2c19282175c3200))
* Import AGENT_CARD_WELL_KNOWN_PATH from adk instead of from a2a directly ([37dae9b](https://github.com/google/adk-python/commit/37dae9b631db5060770b66fce0e25cf0ffb56948))
* Make `LlmRequest.LiveConnectConfig` field default to a factory ([74589a1](https://github.com/google/adk-python/commit/74589a1db7df65e319d1ad2f0676ee0cf5d6ec1d))
* Update the prompt to make the ADK Answering Agent more objective ([2833030](https://github.com/google/adk-python/commit/283303032a174d51b8d72f14df83c794d66cb605))
* Add sample agent for testing parallel functions execution ([90b9193](https://github.com/google/adk-python/commit/90b9193a20499b8dd7f57d119cda4c534fcfda10))
* Hide the ask_data_insights tool until the API is publicly available ([bead607](https://github.com/google/adk-python/commit/bead607364be7ac8109357c9d3076d9b345e9e8a))
* Change `LlmRequest.config`'s default value to be `types.GenerateContentConfig()` ([041f04e](https://github.com/google/adk-python/commit/041f04e89cee30532facccce4900d10f1b8c69ce))
* Prevent triggering of _load_from_yaml_config in AgentLoader ([db975df](https://github.com/google/adk-python/commit/db975dfe2a09a6d056d02bc03c1247ac10f6da7d))
@@ -34,74 +33,102 @@ was for a different project), you probably don't need to do it again.
Visit <https://cla.developers.google.com/> to see your current agreements or to
sign a new one.
## Review our community guidelines
### Review our community guidelines
This project follows
[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
# Contribution workflow
### Code reviews
## Finding Issues to Work On
All submissions, including submissions by project members, require review. We
use GitHub pull requests for this purpose. Consult
[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
information on using pull requests.
- Browse issues labeled **`good first issue`** (newcomer-friendly) or **`help wanted`** (general contributions).
- For other issues, please kindly ask before contributing to avoid duplication.
## Contribution workflow
### Finding Issues to Work On
## Requirement for PRs
- Browse issues labeled **`good first issue`** (newcomer-friendly) or **`help
wanted`** (general contributions).
- For other issues, please kindly ask before contributing to avoid
duplication.
- Each PR should only have one commit. Please squash it if there are multiple PRs.
- All PRs, other than small documentation or typo fixes, should have a Issue assoicated. If not, please create one.
- Small, focused PRs. Keep changes minimal—one concern per PR.
- For bug fixes or features, please provide logs or screenshot after the fix is applied to help reviewers better understand the fix.
- Please include a `testing plan` section in your PR to talk about how you will test. This will save time for PR review. See`Testing Requirements` section for more details.
### Requirement for PRs
- All PRs, other than small documentation or typo fixes, should have a Issue
associated. If not, please create one.
- Small, focused PRs. Keep changes minimal—one concern per PR.
- For bug fixes or features, please provide logs or screenshot after the fix
is applied to help reviewers better understand the fix.
- Please include a `testing plan` section in your PR to talk about how you
will test. This will save time for PR review. See `Testing Requirements`
section for more details.
### Large or Complex Changes
## Large or Complex Changes
For substantial features or architectural revisions:
- Open an Issue First: Outline your proposal, including design considerations and impact.
- Gather Feedback: Discuss with maintainers and the community to ensure alignment and avoid duplicate work
- Open an Issue First: Outline your proposal, including design considerations
and impact.
- Gather Feedback: Discuss with maintainers and the community to ensure
alignment and avoid duplicate work
## Testing Requirements
### Testing Requirements
To maintain code quality and prevent regressions, all code changes must include comprehensive tests and verifiable end-to-end (E2E) evidence.
To maintain code quality and prevent regressions, all code changes must include
comprehensive tests and verifiable end-to-end (E2E) evidence.
#### Unit Tests
### Unit Tests
Please add or update unit tests for your change. Please include a summary of passed `pytest` results.
Please add or update unit tests for your change. Please include a summary of
passed `pytest` results.
Requirements for unit tests:
- **Coverage:** Cover new features, edge cases, error conditions, and typical use cases.
- **Location:** Add or update tests under `tests/unittests/`, following existing naming conventions (e.g., `test_<module>_<feature>.py`).
- **Framework:** Use `pytest`. Tests should be:
- Fast and isolated.
- Written clearly with descriptive names.
- Free of external dependencies (use mocks or fixtures as needed).
- **Quality:** Aim for high readability and maintainability; include docstrings or comments for complex scenarios.
- **Coverage:** Cover new features, edge cases, error conditions, and typical
use cases.
- **Location:** Add or update tests under `tests/unittests/`, following
- Free of external dependencies (use mocks or fixtures as needed).
- **Quality:** Aim for high readability and maintainability; include
docstrings or comments for complex scenarios.
### Manual End-to-End (E2E) Tests
#### Manual End-to-End (E2E) Tests
Manual E2E tests ensure integrated flows work as intended. Your tests should cover all scenarios. Sometimes, it's also good to ensure relevant functionality is not impacted.
Manual E2E tests ensure integrated flows work as intended. Your tests should
cover all scenarios. Sometimes, it's also good to ensure relevant functionality
is not impacted.
Depending on your change:
- **ADK Web:**
- Use the `adk web` to verify functionality.
- Capture and attach relevant screenshots demonstrating the UI/UX changes or outputs.
- Label screenshots clearly in your PR description.
- **ADK Web:**
- **Runner:**
- Provide the testing setup. For example, the agent definition, and the runner setup.
- Execute the `runner` tool to reproduce workflows.
- Include the command used and console output showing test results.
- Highlight sections of the log that directly relate to your change.
- Use the `adk web` to verify functionality.
- Capture and attach relevant screenshots demonstrating the UI/UX changes
or outputs.
- Label screenshots clearly in your PR description.
## Documentation
- **Runner:**
For any changes that impact user-facing documentation (guides, API reference, tutorials), please open a PR in the [adk-docs](https://github.com/google/adk-docs) repository to update relevant part before or alongside your code PR.
- Provide the testing setup. For example, the agent definition, and the
runner setup.
- Execute the `runner` tool to reproduce workflows.
- Include the command used and console output showing test results.
- Highlight sections of the log that directly relate to your change.
### Documentation
For any changes that impact user-facing documentation (guides, API reference,
tutorials), please open a PR in the
[adk-docs](https://github.com/google/adk-docs) repository to update relevant
part before or alongside your code PR.
## Development Setup
1. **Clone the repository:**
```shell
@@ -111,11 +138,13 @@ For any changes that impact user-facing documentation (guides, API reference, tu
2. **Install uv:**
Check out [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/).
All submissions, including submissions by project members, require review. We
use GitHub pull requests for this purpose. Consult
[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
information on using pull requests.
# Vibe Coding
If you want to contribute by leveraging viber coding, the AGENTS.md (https://github.com/google/adk-python/tree/main/AGENTS.md) could be used as context to your LLM.
If you want to contribute by leveraging viber coding, the AGENTS.md
(https://github.com/google/adk-python/tree/main/AGENTS.md) could be used as
[](https://github.com/google/adk-python/actions/workflows/python-unit-tests.yml)
@@ -25,6 +26,43 @@ Agent Development Kit (ADK) is a flexible and modular framework for developing a
---
## 🔥 ADK's very first community call on Oct 15
Join our ADK Community Call! Our first virtual community call is on Oct 15!
Meet our team, and talk with us about our roadmap and how to contribute.
First Call Details:
Topic: ADK Roadmap
Date: October 15, 2025
Time: 9:30-10:30am PST
Meeting link:
[Join the call](http://meet.google.com/gjm-gfim-ctz)
Add to your calendar
[Event calendar invite](https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=MDUydWo1dHV1dHFtNzJuM3E0bmEyMW12ZnZfMjAyNTEwMTVUMTYzMDAwWiBjXzljNWVjODhhMmQyYWU5YjY5Mzk4ODU1MGZkNDA5MjVmYjgxYjM4MTI1NGNjYTgzNmRkMjMwNzRiMjNmYzcyZDVAZw&tmsrc=c_9c5ec88a2d2ae9b693988550fd40925fb81b381254cca836dd23074b23fc72d5%40group.calendar.google.com), [.ics file](https://calendar.google.com/calendar/ical/c_9c5ec88a2d2ae9b693988550fd40925fb81b381254cca836dd23074b23fc72d5%40group.calendar.google.com/public/basic.ics), [ADK community calendar](https://calendar.google.com/calendar/embed?src=c_9c5ec88a2d2ae9b693988550fd40925fb81b381254cca836dd23074b23fc72d5%40group.calendar.google.com&ctz=America%2FLos_Angeles), [ADK Community Call RSVP](https://google.qualtrics.com/jfe/form/SV_3K0RJZ64H1BexqS)
Agenda:
[Julia] ADK Roadmap
[ Bo & Hangfei] Eng Deep Dive: Context Caching
[Kris] How to Contribute
[Shubham] Upcoming Events
---
## 🔥 What's new
- **Context compaction**: Supports context compaction to reduce context length. Here is a [sample](https://github.com/google/adk-python/blob/main/contributing/samples/hello_world_app/agent.py#L156) and [compaction config](https://github.com/google/adk-python/blob/main/src/google/adk/apps/app.py#L51).
- **Resumability**: Support pause and resume an invocation in ADK.
- **ReflectRetryToolPlugin**: Add [`ReflectRetryToolPlugin`](https://github.com/google/adk-python/blob/main/src/google/adk/plugins/reflect_retry_tool_plugin.py) to reflect from errors and retry with different arguments when tool errors.
- **Search tool**: Support using Google built-in search and built-in `VertexAiSearchTool` with other tools in the same agent.
## ✨ Key Features
@@ -35,6 +73,11 @@ Agent Development Kit (ADK) is a flexible and modular framework for developing a
- **Code-First Development**: Define agent logic, tools, and orchestration
directly in Python for ultimate flexibility, testability, and versioning.
- **Agent Config**: Build agents without code. Check out the
- **Tool Confirmation**: A [tool confirmation flow(HITL)](https://google.github.io/adk-docs/tools/confirmation/) that can guard tool execution with explicit confirmation and custom input.
- **Modular Multi-Agent Systems**: Design scalable applications by composing
multiple specialized agents into flexible hierarchies.
@@ -58,7 +101,7 @@ You can install the latest stable version of ADK using `pip`:
pip install google-adk
```
The release cadence is weekly.
The release cadence is roughly bi-weekly.
This version is recommended for most users as it represents the most recent official release.
@@ -88,7 +131,7 @@ from google.adk.tools import google_search
root_agent=Agent(
name="search_assistant",
model="gemini-2.0-flash",# Or your preferred Gemini model
model="gemini-2.5-flash",# Or your preferred Gemini model
instruction="You are a helpful assistant. Answer user questions using Google Search when needed.",
description="An assistant that can search the web.",
tools=[google_search]
@@ -103,13 +146,13 @@ Define a multi-agent system with coordinator agent, greeter agent, and task exec
When deploying the remote BigQuery A2A agent to different environments (e.g., Cloud Run, different hosts/ports), you **must** update the `url` field in the agent card JSON file:
**Important:** The `url` field in `remote_a2a/bigquery_agent/agent.json` must point to the actual RPC endpoint where your remote BigQuery A2A agent is deployed and accessible.
## Troubleshooting
**Connection Issues:**
- Ensure the local ADK web server is running on port 8000
- Ensure the remote A2A server is running on port 8001
- Check that no firewall is blocking localhost connections
- **Verify the `url` field in `remote_a2a/bigquery_agent/agent.json` matches the actual deployed location of your remote A2A server**
- Verify the agent card URL passed to RemoteA2AAgent constructor matches the running A2A server
@@ -182,3 +213,4 @@ You can extend this sample by:
- Check the logs for both the local ADK web server and remote A2A server
- Verify OAuth tokens are properly passed between agents
- Ensure agent instructions are clear about authentication requirements
- **Double-check that the RPC URL in the agent.json file is correct and accessible**
@@ -107,15 +107,47 @@ You can extend this sample by:
- Adding persistent state management
- Integrating with external APIs or databases
## Deployment to Other Environments
When deploying the remote A2A agent to different environments (e.g., Cloud Run, different hosts/ports), you **must** update the `url` field in the agent card JSON file:
**Important:** The `url` field in `remote_a2a/check_prime_agent/agent.json` must point to the actual RPC endpoint where your remote A2A agent is deployed and accessible.
## Troubleshooting
**Connection Issues:**
- Ensure the local ADK web server is running on port 8000
- Ensure the remote A2A server is running on port 8001
- Check that no firewall is blocking localhost connections
- **Verify the `url` field in `remote_a2a/check_prime_agent/agent.json` matches the actual deployed location of your remote A2A server**
- Verify the agent card URL passed to RemoteA2AAgent constructor matches the running A2A server
**Agent Not Responding:**
- Check the logs for both the local ADK web server on port 8000 and remote A2A server on port 8001
- Verify the agent instructions are clear and unambiguous
- **Double-check that the RPC URL in the agent.json file is correct and accessible**
@@ -116,18 +116,50 @@ You can extend this sample by:
- Integrating with external approval systems or databases
- Implementing approval timeouts and escalation procedures
## Deployment to Other Environments
When deploying the remote approval A2A agent to different environments (e.g., Cloud Run, different hosts/ports), you **must** update the `url` field in the agent card JSON file:
**Important:** The `url` field in `remote_a2a/human_in_loop/agent.json` must point to the actual RPC endpoint where your remote approval A2A agent is deployed and accessible.
## Troubleshooting
**Connection Issues:**
- Ensure the local ADK web server is running on port 8000
- Ensure the remote A2A server is running on port 8001
- Check that no firewall is blocking localhost connections
- **Verify the `url` field in `remote_a2a/human_in_loop/agent.json` matches the actual deployed location of your remote A2A server**
- Verify the agent card URL passed to RemoteA2AAgent constructor matches the running A2A server
**Agent Not Responding:**
- Check the logs for both the local ADK web server on port 8000 and remote A2A server on port 8001
- Verify the agent instructions are clear and unambiguous
- Ensure long-running tool responses are properly formatted with matching IDs
- **Double-check that the RPC URL in the agent.json file is correct and accessible**
**Approval Workflow Issues:**
- Verify that updated tool responses use the same `id` and `name` as the original function call
Create an agent that can roll n-sided number and check whether the rolled number is prime.
```
### Add Capabilities to Existing Agent
```
Could you make the agent under `./config_based/roll_and_check` a multi agent system : root_agent only for request routing and two sub agents responsible for two functions respectively ?
```
### Project Structure Analysis
```
Please analyze my existing project structure at './config_based/roll_and_check' and suggest improvements for better organization.
```
## Tool Ecosystem
### Core File Operations
- **`read_config_files`** - Read multiple YAML configurations with analysis
- **`write_config_files`** - Write multiple YAML files with validation
- **`read_files`** - Read multiple files of any type
- **`write_files`** - Write multiple files with backup options
- **`delete_files`** - Delete multiple files with backup options
### Project Analysis
- **`explore_project`** - Analyze project structure and suggest paths
- **`resolve_root_directory`** - Resolve paths with working directory context
### ADK knowledge Context
- **`google_search`** - Search for ADK examples and documentation
- **`url_context`** - Fetch content from URLs (GitHub, docs, etc.)
- **`search_adk_source`** - Search ADK source code with regex patterns
- **Session Context**: Persistent directory binding across conversations
### Memory Management
- **Lazy Loading**: Schema loaded only when needed
- **Cache Control**: Manual cache clearing for testing/development
- **Resource Cleanup**: Automatic cleanup of temporary files
## Error Handling
### Comprehensive Validation
- **Path Validation**: All paths validated before file operations
- **Schema Compliance**: AgentConfig validation with detailed error reporting
- **Python Syntax**: Syntax validation for generated Python code
- **Backup Creation**: Automatic backups before overwriting files
### Recovery Mechanisms
- **Retry Suggestions**: Clear guidance for fixing validation errors
- **Backup Restoration**: Easy recovery from automatic backups
- **Error Context**: Detailed error messages with file locations and suggestions
This comprehensive assistant provides everything needed for intelligent, efficient ADK agent system creation with proper validation, file management, and project organization.
'agent',# Make agent module available for adk web discovery
]
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