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
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
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
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 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
