Master Google Agent Development Kit (ADK) with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install ADK
: `pip install google
kit` and set up Python development environment with required dependencies
Configure models
: Set up Gemini API access or configure LiteLLM for your preferred models (Claude, GPT
Explore local UI
: Run the built
in web interface to understand agent debugging and interaction patterns before building custom agents
Build simple agent
: Start with a single
agent example using built
in tools (Google Search, code execution) to learn the framework patterns
Plan production deployment
: Evaluate Vertex AI Agent Engine for managed deployment or containerization for other cloud platforms
💡 Quick Start: Follow these 14 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 builders workflows.
Build hierarchical agent teams using sequential, parallel, and loop workflow agents alongside LLM agents and custom agents. Agents can delegate to sub-agents through agent routing, and the new Python 2.0 Beta introduces agent teams for coordinated multi-agent execution with shared state and task delegation patterns.
Test agent performance with criteria-based scoring, user simulation, environment simulation, custom metrics, and an optimization module for iterative improvement. Score trajectory accuracy (correct steps taken), tool usage quality, and final response relevance — capabilities that most competing frameworks require third-party integrations to achieve.
ADK natively supports the Model Context Protocol, allowing agents to consume any MCP-compatible tool server without custom integration code. Combined with OpenAPI tool generation and traditional function tools with action confirmations, ADK provides one of the most flexible tooling ecosystems among agent frameworks.
Ships with three runtime modes: a web-based UI for visual debugging of agent interactions and tool calls, a CLI for terminal-driven workflows, and an API server for production integrations. The web UI provides real-time inspection of agent decision-making, making it significantly easier to debug multi-agent coordination than log-based approaches.
Available in four official SDKs — Python 2.0 Beta, TypeScript 1.0 (new in 2026), Go, and Java — all maintained under github.com/google. Supports Gemini, Gemma, Claude, Ollama, vLLM, LiteLLM, LiteRT-LM, and Apigee AI Gateway for flexible model selection across providers.
Now that you know how to use Google Agent Development Kit (ADK), it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful ai agent builders tool in minutes.
Tutorial updated March 2026