mainly because http://github.com/robots.txt disallows `/*/raw/` path. using GCS HTTP URIs is more reliable with Gemini model.
PiperOrigin-RevId: 811409688
this is to allow turning on debug log for debugging if some unexpected behavior observed during running cache analysis experiments.
PiperOrigin-RevId: 811189954
Merge https://github.com/google/adk-python/pull/2960
1. All in one authentication sample (has an IDP, Agent and the application) under `contributing/samples/authn-adk-all-in-one/`
2. Documented for all the steps.
3. OAuth 2.0 Authorization Code Grant type used by the agent.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2960 from nikhilpurwant:main dfcc821602d265c4ae7cc42eb1f5739beaad6f87
PiperOrigin-RevId: 808672120
Corrected `CountInvocationPlugin` to be a class reference and added `ContextFilterPlugin` to limit the number of tool invocations kept in the context to 3.
PiperOrigin-RevId: 808591608
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
Switched the active model from `gemini-live-2.5-flash-preview` (for AI Studio) to `gemini-2.0-flash-live-preview-04-09` (for Vertex).
PiperOrigin-RevId: 806348640
The old live/bidi agents are using a cache to store context/history during agent transfer etc. As we have added support for session for live/bidi, we are now migrating the context/history cache to it. This improves scalability, efficiency and maintainability.
It introduces several changes:
* AudioTranscriber support is removed as now we are using native transcription from models.
* Transcription is returned as input_transcription/output_transcription fields and no longer as contents.
* We will return a new event with artifact references of file type of audio/pcm.(in addition to existing audio response event. So the users of this api need to do proper filtering here.)
PiperOrigin-RevId: 805997675
a. dump the discussion content to a tmp file first to avoid github redaction of environment variable
b. instruct the agent to use get_discussion_and_comments only when discussion content json is not available.
PiperOrigin-RevId: 805581573
Changes references from `gemini-1.5-flash` and `gemini-1.5-pro` to `gemini-2.5-flash` and `gemini-2.5-pro` in docstrings, default values, sample agents, and tests.
PiperOrigin-RevId: 805536434
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