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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