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
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
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
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
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
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
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
