This commit refactors the `BigQueryAgentAnalyticsPlugin` to leverage the modern BigQuery Storage Write API, replacing the previous implementation that used the legacy `insert_rows_json` method (based on `tabledata.insertAll`).
**Key Changes:**
* **Switched to Storage Write API:** Event logs are now ingested using the `BigQueryWriteClient` from the `google-cloud-bigquery-storage` library.
* **Utilizes Default Stream:** We are using the `_default` stream for sending data, which is an efficient method for streaming in data without needing to manage stream lifecycles. This is ideal for continuous event logging.
* **Apache Arrow Format:** Log entries are converted to the Apache Arrow format using `pyarrow` before being sent. The BigQuery table schema is dynamically converted to an Arrow schema. This binary format is more efficient than JSON.
* **Updated Initialization:** The plugin now initializes both the standard `bigquery.Client` (for table management) and the `BigQueryWriteClient`.
* **Test Updates:** Unit tests in `test_bigquery_logging_plugin.py` have been comprehensively updated to mock the new `BigQueryWriteClient`, `bq_schema_utils`, and `pyarrow` components. Tests now verify calls to `append_rows` and the data structure passed to create the Arrow RecordBatch.
**Benefits of this change:**
* **Improved Performance:** The Storage Write API is designed for high-throughput streaming and offers better performance compared to the legacy API.
* **Reduced Cost:** Ingesting data via the Storage Write API is generally more cost-effective.
* **Enhanced Reliability:** The Storage Write API provides more robust streaming capabilities.
* **Modernization:** Aligns the plugin with the recommended best practices for BigQuery data ingestion.
This change enhances the efficiency and scalability of the BigQuery logging plugin.
PiperOrigin-RevId: 828655496
This change introduces BigQueryLoggerConfig to allow customization of the BigQueryAgentAnalyticsPlugin. Users can now enable/disable the plugin, specify event type allowlists and denylists, and provide a custom function to format or redact the content field before logging to BigQuery. The content logged for model and tool errors has also been enhanced.
PiperOrigin-RevId: 828172241
ARIMA supports both historical data and future data anomaly detection. This CL add how the tool support future table anomaly detection.
PiperOrigin-RevId: 827803748
Removes the dataset_id field from the BigQuery table schema and from each log entry created by the BigQueryAgentAnalyticsPlugin. This field is redundant, as all rows logged to a specific table belong to the same dataset.
To ensure the plugin can still target the correct dataset, dataset_id is now a required argument in the BigQueryAgentAnalyticsPlugin constructor, and its default value has been removed.
The BigQuery client user_agent is also updated with plugin version info to help identify traffic originating from this plugin. Unit tests have been updated to reflect the removal of dataset_id from log entries.
PiperOrigin-RevId: 826596499
We also change VertexAiSessionService and VertexAiMemoryBankService to both use keyword arguments for project, location, agent engine id, and express mode api key
PiperOrigin-RevId: 825719331
Previously this will return true for events yielded from before_agent_callback when there are state changes.
Note with this change, it will also return false for state delta only callbacks even after main response, but this is fine as long as the actual final response event has it to be true.
Closes#2992
PiperOrigin-RevId: 825313208
This change introduces a new `detect_anomalies` tool in `query_tool.py` which uses BigQuery ML's `CREATE MODEL` with `ARIMA_PLUS` type and `ML.DETECT_ANOMALIES` to detect anomalies. The new function is also added to the `bigquery_toolset`.
PiperOrigin-RevId: 825181489
Introduces the `BigQueryLoggingPlugin` for capturing and sending ADK lifecycle events to Google BigQuery. This allows for persistent storage and analysis of agent and tool interactions. The plugin supports asynchronous logging, automatic dataset/table creation, and comprehensive event capture.
Also refactors common formatting utilities (_format_content, _format_args) for shared use.
PiperOrigin-RevId: 824703739
The computer_use sample now supports launching with a `user_data_dir` to maintain browser state across runs. The sample agent is updated to use a shared temporary directory for the browser profile, preserving login sessions and other data.
PiperOrigin-RevId: 823749082
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
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
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
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
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
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
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
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
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
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
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
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
