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An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

google.github.io/adk-docs/

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Apache-2.0 license
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Agent Development Kit (ADK)

License PyPI Python Unit Tests r/agentdevelopmentkit Ask DeepWiki

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.

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.

🔥 ADK's very first community call on Oct 15

Join our ADK Community Call! Our first virtual community call is on Oct 15! Meet our team, and talk with us about our roadmap and how to contribute.

First Call Details:

Topic: ADK Roadmap

Date: October 15, 2025

Time: 9:30-10:30am PST

Meeting link: Join the call

Add to your calendar Event calendar invite, .ics file, ADK community calendar

Agenda: [Julia] ADK Roadmap [ Bo & Hangfei] Eng Deep Dive: Context Caching [Kris] How to Contribute

[Shubham] Upcoming Events

🔥 What's new

  • Context compaction: Supports context compaction to reduce context length. Here is a sample and compaction config.

  • Resumability: Support pause and resume an invocation in ADK.

  • ReflectRetryToolPlugin: Add ReflectRetryToolPlugin to reflect from errors and retry with different arguments when tool errors.

  • Search tool: Support using Google built-in search and built-in VertexAiSearchTool with other tools in the same agent.

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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.

  • Agent Config: Build agents without code. Check out the Agent Config feature.

  • Tool Confirmation: A tool confirmation flow(HITL) that can guard tool execution with explicit confirmation and custom input.

  • 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.

🤖 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.

🚀 Installation

Stable Release (Recommended)

You can install the latest stable version of ADK using pip:

pip install google-adk

The release cadence is roughly bi-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.

📚 Documentation

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

🏁 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-2.5-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-2.5-flash", ...)
task_executor = LlmAgent(name="task_executor", model="gemini-2.5-flash", ...)

# Create parent agent and assign children via sub_agents
coordinator = LlmAgent(
    name="Coordinator",
    model="gemini-2.5-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

Vibe Coding

If you are to develop agent via vibe coding the llms.txt and the llms-full.txt can be used as context to LLM. While the former one is a summarized one and the later one has the full information in case your LLM has big enough context window.

📄 License

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

Happy Agent Building!

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An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

google.github.io/adk-docs/

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