Merge https://github.com/google/adk-python/pull/2864
**Reason for this change:**
Multiple typos were found in comments, docstrings, and code throughout the codebase, which could lead to confusion and reduce code readability.
**Changes made:**
Fixed the following typos across 8 files:
1. contributing/samples/adk_answering_agent/utils.py:130: "extention" → "extension"
2. llms-full.txt:15171: "fuction" → "function"
3. src/google/adk/a2a/converters/part_converter.py:
- Line 96: "Conver" → "Convert", "reponse" → "response"
- Line 99: "suervice" → "service"
- Line 100: "accordinlgy" → "accordingly"
- Line 191: "Conver" → "Convert", "reponse" → "response"
- Line 195: "accordinlgy" → "accordingly"
4. src/google/adk/agents/base_agent.py:568: "custome" → "custom"
5. src/google/adk/evaluation/agent_evaluator.py:572: "Retruns" → "Returns"
6. src/google/adk/flows/llm_flows/basic.py:55: "outoput_schema" → "output_schema"
7. src/google/adk/flows/llm_flows/contents.py:136: "fuction_response" → "function_response"
8. src/google/adk/models/google_llm.py:138: "gemini_llm_connecton.py" → "gemini_llm_connection.py"
**Impact:**
This change will:
- Improve code documentation clarity and professionalism
- Make comments, docstrings, and code more readable and accurate
- Help prevent confusion for developers reading the code
- Ensure consistency in terminology throughout the codebase
This is a non-breaking change that only affects comments, documentation strings, and improves code clarity.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2864 from ammmr:chore-fix-typos 3cea9fcf6f21edb006b63e9258d2b82930dd961d
PiperOrigin-RevId: 805227784
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
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: 803155804
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: 801252329
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
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: 801084463
The existing `LongRunningTool` does not define a programmatic way to provide & validate structured input, also it relies on LLM to reason and parse the user's response.
For a quick start, annotate the function with `FunctionTool(my_function, require_confirmation=True)`. A more advanced flow is shown in the `human_tool_confirmation` sample.
The new flow is similar to the existing Auth flow:
- User request a tool confirmation by calling `tool_context.request_confirmation()` in the tool or `before_tool_callback`, or just using the `require_confirmation` shortcut in FunctionTool.
- User can provide custom validation logic before tool call proceeds.
- ADK creates corresponding RequestConfirmation FunctionCall Event to ask user for confirmation
- User needs to provide the expected tool confirmation to a RequestConfirmation FunctionResponse Event.
- ADK then checks the response and continues the tool call.
PiperOrigin-RevId: 801019917
Xuan Yang
Google Team Member
GenkiNoguchi
Shangjie Chen
Yujun Zou
Xiang (Sean) Zhou
Hangfei Lin
Wei Sun (Jack)