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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
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""A Python coding agent using the GkeCodeExecutor for secure execution."""
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from google.adk.agents import LlmAgent
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from google.adk.code_executors import GkeCodeExecutor
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def gke_agent_system_instruction():
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"""Returns: The system instruction for the GKE-based coding agent."""
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return """You are a helpful and capable AI agent that can write and execute Python code to answer questions and perform tasks.
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When a user asks a question, follow these steps:
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1. Analyze the request.
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2. Write a complete, self-contained Python script to accomplish the task.
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3. Your code will be executed in a secure, sandboxed environment.
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4. Return the full and complete output from the code execution, including any text, results, or error messages."""
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gke_executor = GkeCodeExecutor(
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# This must match the namespace in your deployment_rbac.yaml where the
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# agent's ServiceAccount and Role have permissions.
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namespace="agent-sandbox",
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# Setting an explicit timeout prevents a stuck job from running forever.
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timeout_seconds=600,
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)
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root_agent = LlmAgent(
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name="gke_coding_agent",
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model="gemini-2.0-flash",
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description=(
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"A general-purpose agent that executes Python code in a secure GKE"
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" Sandbox."
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),
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instruction=gke_agent_system_instruction(),
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code_executor=gke_executor,
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)
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