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Google Agent Development Kit (ADK)

active

4.3M PyPI downloads/month for a framework under 10 months old. v1.27.2 (2026-03-17), multi-language (Python, TypeScript, Go, Java), model-agnostic (Gemini, Claude, Ollama, vLLM, LiteLLM). Native Cloud Run + Vertex AI Agent Engine deployment. ADK 2.0 Alpha adds graph-based workflows. Best for GCP/Vertex AI teams.

Expertise
Composite
Complexity
agentsorchestration

68/100

Trust

18K+

Stars

2

Evidence

Product screenshot

Google Agent Development Kit (ADK) in action

Repo health

68/100

11h ago

Last push

599

Open issues

3,093

Forks

259

Contributors

Editorial verdict

Strong download velocity for its age — GCP-native deployment and multi-language commitment give it a longer runway than single-language frameworks. Best for teams already on GCP/Vertex AI. No independently-verified production case studies outside Google-controlled publications.

Public evidence

How does this compare?

See side-by-side metrics against other skills in the same category.

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Where it wins

4.3M PyPI downloads/month — significant for under-10-month-old framework

Multi-language: Python, TypeScript, Go, Java — widest language breadth in category

Model-agnostic despite Google origins: Gemini, Claude, Ollama, vLLM, LiteLLM

Native Cloud Run + Vertex AI Agent Engine deployment — unique GCP advantage

v1.27.2 released 2026-03-17; bi-weekly release cadence

ADK 2.0 Alpha: graph-based workflows converging toward LangGraph primitives

Where to be skeptical

No independently-verified production case studies outside Google-controlled publications

GCP lock-in tradeoff unfavorable vs LangGraph for non-GCP teams

Ranking in categories

Teams of Agents / Multi-Agent Orchestration

#F05of 16

Teams committed to GCP/Vertex AI deployment — native Cloud Run + Vertex AI Agent Engine integration

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Raw GitHub source

GitHub README peek

Constrained peek so you can sanity-check the source material without leaving the site.

Agent Development Kit (ADK)

Python Unit Tests <a href="https://codewiki.google/github.com/google/adk-python"><img src="https://www.gstatic.com/_/boq-sdlc-agents-ui/_/r/Mvosg4klCA4.svg" alt="Ask Code Wiki" height="20"></a>

<html> <h2 align="center"> <img src="https://raw.githubusercontent.com/google/adk-python/main/assets/agent-development-kit.png" width="256"/> </h2> <h3 align="center"> An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control. </h3> <h3 align="center"> Important Links: <a href="https://google.github.io/adk-docs/">Docs</a>, <a href="https://github.com/google/adk-samples">Samples</a>, <a href="https://github.com/google/adk-java">Java ADK</a>, <a href="https://github.com/google/adk-go">Go ADK</a> & <a href="https://github.com/google/adk-web">ADK Web</a>. </h3> </html>

Agent Development Kit (ADK) is a flexible and modular framework that applies software development principles to AI agent creation. It is designed to simplify building, deploying, and orchestrating agent workflows, from simple tasks to complex systems. While optimized for Gemini, ADK is model-agnostic, deployment-agnostic, and compatible with other frameworks.

🔥 What's new

  • Custom Service Registration: Add a service registry to provide a generic way to register custom service implementations to be used in FastAPI server. See short instruction. (391628f)

  • Rewind: Add the ability to rewind a session to before a previous invocation (9dce06f).

  • New CodeExecutor: Introduces a new AgentEngineSandboxCodeExecutor class that supports executing agent-generated code using the Vertex AI Code Execution Sandbox API (ee39a89)

✨ Key Features

  • Rich Tool Ecosystem: Utilize pre-built tools, custom functions, OpenAPI specs, MCP tools 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.

🚀 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.

🤖 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.

📚 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-2.5-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 to work 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).

<img src="https://raw.githubusercontent.com/google/adk-python/main/assets/adk-web-dev-ui-function-call.png"/>
View on GitHub →