Hangfei LinandCopybara-Service
6071b34650
This change adds activity start and end signals to the LiveRequestQueue,
allowing clients to manually control the start and end of user input in
streaming sessions when automatic voice activity detection is disabled.
The LiveRequestQueue allows users to send messages to the model with the following semantics:
- `content`: sends turn-by-turn content.
- `blob`: sends a media blob for realtime streaming (e.g., audio).
- `activity_start`: indicates the beginning of an activity.
- `activity_end`: indicates the end of an activity.
- `close`: closes the connection.
GeminiLLMConnection has been updated to send the new activity signals to the backend.
This change is a necessary to support clients (e.g. voice assistants) that do not want to use automatic voice activity detection. In this case, the client will be responsible to send the `activity_start` signal when the user starts talking, and `activity_end` when the user finishes talking.
To test the change:
run_config = RunConfig(
realtime_input_config=types.RealtimeInputConfig(
automatic_activity_detection=types.AutomaticActivityDetection(
disabled=True,
),
)
)
import threading # Add this import
def thread_target():
# Define the async operations to run in the background.
async def background_task():
live_request_queue.send_activity_start()
# live_request_queue.send_content(
# content=types.Content(
# role='user',
# parts=[types.Part.from_text(text="hi, what's the time?")],
# )
# )
await asyncio.sleep(3)
live_request_queue.send_activity_end()
PiperOrigin-RevId: 783882447
Agent Development Kit (ADK)
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Important Links: Docs, Samples, Java ADK & ADK Web.
</html>Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
โจ Key Features
-
Rich Tool Ecosystem: Utilize pre-built tools, custom functions, OpenAPI specs, or integrate existing tools to give agents diverse capabilities, all for tight integration with the Google ecosystem.
-
Code-First Development: Define agent logic, tools, and orchestration directly in Python for ultimate flexibility, testability, and versioning.
-
Modular Multi-Agent Systems: Design scalable applications by composing multiple specialized agents into flexible hierarchies.
-
Deploy Anywhere: Easily containerize and deploy agents on Cloud Run or scale seamlessly with Vertex AI Agent Engine.
๐ค Agent2Agent (A2A) Protocol and ADK Integration
For remote agent-to-agent communication, ADK integrates with the A2A protocol. See this example for how they can work together.
๐ Installation
Stable Release (Recommended)
You can install the latest stable version of ADK using pip:
pip install google-adk
The release cadence is weekly.
This version is recommended for most users as it represents the most recent official release.
Development Version
Bug fixes and new features are merged into the main branch on GitHub first. If you need access to changes that haven't been included in an official PyPI release yet, you can install directly from the main branch:
pip install git+https://github.com/google/adk-python.git@main
Note: The development version is built directly from the latest code commits. While it includes the newest fixes and features, it may also contain experimental changes or bugs not present in the stable release. Use it primarily for testing upcoming changes or accessing critical fixes before they are officially released.
๐ Documentation
Explore the full documentation for detailed guides on building, evaluating, and deploying agents:
๐ Feature Highlight
Define a single agent:
from google.adk.agents import Agent
from google.adk.tools import google_search
root_agent = Agent(
name="search_assistant",
model="gemini-2.0-flash", # Or your preferred Gemini model
instruction="You are a helpful assistant. Answer user questions using Google Search when needed.",
description="An assistant that can search the web.",
tools=[google_search]
)
Define a multi-agent system:
Define a multi-agent system with coordinator agent, greeter agent, and task execution agent. Then ADK engine and the model will guide the agents works together to accomplish the task.
from google.adk.agents import LlmAgent, BaseAgent
# Define individual agents
greeter = LlmAgent(name="greeter", model="gemini-2.0-flash", ...)
task_executor = LlmAgent(name="task_executor", model="gemini-2.0-flash", ...)
# Create parent agent and assign children via sub_agents
coordinator = LlmAgent(
name="Coordinator",
model="gemini-2.0-flash",
description="I coordinate greetings and tasks.",
sub_agents=[ # Assign sub_agents here
greeter,
task_executor
]
)
Development UI
A built-in development UI to help you test, evaluate, debug, and showcase your agent(s).
Evaluate Agents
adk eval \
samples_for_testing/hello_world \
samples_for_testing/hello_world/hello_world_eval_set_001.evalset.json
๐ค Contributing
We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our
- General contribution guideline and flow.
- Then if you want to contribute code, please read Code Contributing Guidelines to get started.
๐ License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
Happy Agent Building!