Agent Development Kit (ADK)

Agent Development Kit (ADK) is designed for developers seeking fine-grained control and flexibility when building advanced AI agents that are tightly integrated with services in Google Cloud. It allows you to define agent behavior, orchestration, and tool use directly in code, enabling robust debugging, versioning, and deployment anywhere – from your laptop to the cloud.

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✨ 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.

🚀 Installation

You can install the ADK using pip:

pip install google-adk

📚 Documentation

Explore the full documentation for detailed guides on building, evaluating, and deploying agents:

• Documentation

🏁 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

🤝 Contributing

We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our Contributing Guidelines to get started.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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Happy Agent Building!