MCPHub LabRegistrygoogle/adk-python
google

google/adk python

Built by google 18,628 stars

What is google/adk python?

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

How to use google/adk python?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add google/adk python using the following command: npx @modelcontextprotocol/google-adk-python 4. Restart the client and verify the new tools are active.
🛡️ Scoped (Restricted)
npx @modelcontextprotocol/google-adk-python --scope restricted
🔓 Unrestricted Access
npx @modelcontextprotocol/google-adk-python

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

google/adk python FAQ

Q

Is google/adk python safe?

Yes, google/adk python follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is google/adk python up to date?

google/adk python is currently active in the registry with 18,628 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for google/adk python?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub

Agent Development Kit (ADK)

License PyPI Python Unit Tests r/agentdevelopmentkit <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:

🏁 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"/>

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

Community Repo

We have adk-python-community repo that is home to a growing ecosystem of community-contributed tools, third-party service integrations, and deployment scripts that extend the core capabilities of the ADK.

Vibe Coding

If you want 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.

Community Events

  • [Completed] ADK's 1st community meeting on Wednesday, October 15, 2025. Remember to join our group to get access to the recording, and deck.

📄 License

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


Happy Agent Building!

Global Ranking

8.5
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "google-adk-python": { "command": "npx", "args": ["google-adk-python"] } } }