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
Corrects a typo in the `StreamableHTTPConnectionParams` docstring, changing "SSE" to "Streamable HTTP" to accurately reflect the referenced client.
PiperOrigin-RevId: 794424727
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
This change takes cares of SQL results containing values that are not json serializable (e.g. datetime, bignumeric) by converting them to their string representation.
PiperOrigin-RevId: 785719997
When get session is being called on a session with more than 1 page of events (100+), the response of the subsequent listevents calls fails due to missing parsing logic. This change fixes the processing of the listevents responses.
PiperOrigin-RevId: 783166959
Even though InMemoryMemoryService is intended only for testing and local development, we eliminate a potential source of bugs during prototyping by providing a thread-safe InMemoryMemoryService.
PiperOrigin-RevId: 781554006
This change adds a new enum value which the agent builder can pass in the `BigQueryToolConfig` to allow limited writes to the `execute_sql` tool.
PiperOrigin-RevId: 776661744
This change renames the sample agent based on the Google API based tools to reflect the larger purpose and avoid confusion with the built-in BigQuery tools. In addition, it also renames the root agent in the BigQuery sample agent to "bigquery_agent"
PiperOrigin-RevId: 775655226
This change accepts the `google.auth.credentials.Credentials` type for `BigQueryCredentialsConfig`, so any subclass of that, including `google.oauth2.credentials.Credentials` would work to integrate with BigQuery service. This opens up a whole range of possibilities, such as using service account credentials to deploy an agent using these tools.
PiperOrigin-RevId: 773190440
This change sets an explicit project id in the BigQuery client from the conversation context. Without this the client was trying to set a project from the environment's application default credentials and running into issues where application default credentials is not available.
PiperOrigin-RevId: 772695883
#non-breaking
The correct conversion from 25 degrees Celsius is 77 degrees Fahrenheit. The previous value of 41 was wrong.
PiperOrigin-RevId: 772528757
This change introduces unit tests in which the behavior of the tool is asserted for various query types in various write modes through a mocked BigQuery client.
PiperOrigin-RevId: 770653117
This allows to protect against any write operations (e.g. update or delete a table), useful for some agents that must only be used in a read-only mode, while the user may have write permissions.
PiperOrigin-RevId: 769803741
