Master Google Agent Development Kit (ADK) with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install ADK Install via pip: `pip install google
adk`. Requires Python
Get a Gemini API Key Visit aistudio.google.com and generate a free API key. Set it as an environment variable: `export GOOGLE_API_KEY=your_key`. ##
Create Your First Agent Create a Python file with a basic agent definition using `Agent` class. Set name, instructions, and model. ##
Add Tools Define Python functions as tools using the `@tool` decorator. ADK handles function calling automatically. ##
Run the Agent Use `Runner` to execute your agent with user input. The framework handles the agent loop, tool calls, and response generation. ##
Add Evaluation Write evaluation test cases using ADK's built
in evaluation framework. Define expected behaviors and measure agent performance. ##
Build Multi
Agent Systems Create multiple agents with different specialties and use ADK's orchestration to coordinate them on complex tasks. ##
Deploy to Production Deploy to Vertex AI Agent Builder for managed hosting, or self
host using standard Python deployment practices.
💡 Quick Start: Follow these 12 steps in order to get up and running with Google Agent Development Kit (ADK) quickly.
Explore the key features that make Google Agent Development Kit (ADK) powerful for ai agent framework workflows.
Build agents using clean Python APIs. Define agents with instructions, tools, and behaviors in straightforward code without complex abstraction layers.
Developers who prefer writing Python over navigating visual builders or complex framework hierarchies.
First-class support for systems where multiple agents collaborate. Built-in patterns for delegation, sequential processing, and parallel execution.
Complex workflows where different agents handle research, analysis, writing, and review as a coordinated team.
Systematic tools for testing agent performance, comparing outputs, and tracking quality metrics across iterations.
Production teams that need to measure and improve agent quality over time with repeatable test suites.
While optimized for Gemini, ADK supports integration with other LLMs including OpenAI, Anthropic, and open-source models.
Teams that want to experiment with different models or maintain flexibility to switch providers.
Define Python functions as agent tools with automatic schema generation and execution handling.
Agents that need to interact with APIs, databases, file systems, or external services.
Deploy agents as managed services on Google Cloud with built-in monitoring, scaling, and management.
Production deployments requiring enterprise-grade infrastructure, scaling, and operational tools.
No. ADK is model-agnostic and works with other LLMs, though it provides deepest integration with Gemini models and Google Cloud infrastructure.
LangChain has a larger ecosystem and community. ADK offers tighter Google Cloud integration and a more opinionated agent architecture. Choose ADK for Google-centric stacks, LangChain for model flexibility.
ADK is newer than LangChain but backed by Google. The evaluation framework and deployment tools support production use. Community and third-party tooling are still maturing.
Now that you know how to use Google Agent Development Kit (ADK), it's time to put this knowledge into practice.
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Tutorial updated March 2026