Build AI Agents with Google Agent Development Kit in Python

Google has introduced the Agent Development Kit (ADK), a Python and Java library designed to simplify the creation of AI agents. These agents, which interact with users via natural language, can handle tasks ranging from simple queries to complex multi-step workflows. While optimized for Google's Gemini model, the ADK can be adapted for other AI APIs.

Setting Up the ADK

To begin, install the ADK using pip install google-adk. Be prepared for a significant footprint—84 dependencies totaling ~285MB. Store API keys in an .env file for automatic loading.

Creating a Basic Agent

1. Directory Structure: Organize agents in subdirectories (e.g., searchagent) with __init__.py and agent.py.
2. Agent Configuration: Define agents using the Agent class, specifying:
   • model (e.g., gemini-2.0-flash)
   • instructions (prompts guiding agent behavior)
   • tools (e.g., google_search for web augmentation)

Example agent.py: python from google.adk.agents import Agent from google.adk.tools import google_search

root_agent = Agent( name="search_assistant", description="An agent that answers questions with web searches.", model="gemini-2.0-flash", instruction="Answer user questions using Google searches.", tools=[google_search] )

3. Testing: Run adk web to launch a local web interface at http://localhost:8000. The UI includes:
   • Chat functionality
   • Debugging panels showing agent metadata
   • Token streaming for real-time responses

Advanced Architectures

The ADK supports workflow agents for multi-agent systems:

• Sequential Agents: Chain outputs between agents (e.g., generate text → rewrite in a specific style).
• Loop Agents: Iterate until conditions are met (e.g., condense text → verify completeness → repeat if needed).
• Parallel Agents: Execute tasks concurrently (e.g., fact-checking across multiple search engines).

Extending Functionality with Tools

Agents can integrate non-LLM actions via:

1. Function Tools: Wrap existing Python/Java code (must return JSON).
2. Built-in Tools: Use prebuilt utilities like Google Search or code execution.
3. Third-party Tools: Leverage integrations like LangChain.

Example Projects

Google provides sample projects including:

• LLM Auditor: Fact-checking with sequential agents
• Customer Service: Mock integrations with back-end systems
• Image Scoring: Loop-based image generation and validation

For developers exploring AI agents, the ADK offers a structured approach to building scalable, interactive systems with minimal boilerplate.