Google Agent ADK: Google’s Multi AI Agent framework

How to use Google Agent ADK?

Google is trying to regain its crown as the most tech-savvy firm. First, they released Gemini 2.5 Pro, the best LLM so far. Now, they have come up with a Google Agent SDK which can help you create multi-agent orchestration pipelines with ease in production.

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Data Science in Your Pocket – No Rocket Science

Key Features of Google’s Agent ADK:

1. Core Purpose

• Open-source framework for building autonomous multi-agent systems.
• Simplifies end-to-end development of production-ready agentic applications.
• Powers Google’s own agents (e.g., Agentspace, Google Customer Engagement Suite).

2. Core Pillars

• Build: Modular, scalable multi-agent systems.
• Interact: Human-like conversations with multimodal (audio/video) streaming.
• Evaluate: Systematic performance assessment (step-by-step execution & response quality).
• Deploy: Containerized deployment anywhere (optimized for Google Cloud).

3. Multi-Agent Capabilities

• Hierarchical Composition: Agents can delegate tasks to specialized sub-agents.
• Dynamic Orchestration:

Workflow agents (sequential, parallel, loop).

LLM-driven dynamic routing (e.g., LlmAgent transfer).

• Auto-Delegation: Agents intelligently route tasks based on descriptions.

4. Model & Tool Ecosystem

Model Agnostic:

Supports Gemini, Vertex AI Model Garden, and third-party models via LiteLLM (Anthropic, Meta, Mistral, etc.).

Rich Tool Integration: Pre-built tools (Search, Code Execution).

Model Context Protocol (MCP) tools.

Third-party libraries (LangChain, LlamaIndex).

Other agents as tools (LangGraph, CrewAI).

5. Developer Experience

• Pythonic API: Simple agent definition with LlmAgent and tools.
• CLI & Web UI: Local testing, debugging, and visualization.
• Bidirectional Streaming: Real-time multimodal (audio/video) interactions.

6. Evaluation Framework

• Test Case Validation: Evaluate agent responses & execution paths.
• Programmatic & CLI Testing: AgentEvaluator.evaluate() or adk eval.

7. Deployment Flexibility

• Containerized: Deploy anywhere (Docker, Kubernetes).
• Google Cloud Optimized:

Native Vertex AI Agent Engine integration.

Enterprise-grade runtime with Gemini models (e.g., Gemini 2.5 Pro Experimental).

Pre-built connectors (AlloyDB, BigQuery, Apigee, NetApp).

How does it compare with Crew.AI, Langraph, and Autogen?

The framework is looking good and compares well with existing stable multi-agent orchestration frameworks.

Summarizing

Key Takeaways:

1. Google ADK excels in production-ready, cloud-integrated deployments with strong evaluation tools
2. LangGraph is ideal for complex, stateful workflows requiring precise control (e.g., error recovery, cycles)
3. CrewAI simplifies role-based collaboration but lacks advanced orchestration
4. AutoGen shines in conversational/code-generation tasks but struggles with scalability.

How to use Google’s Agent SDK?

1. The codes are simple. Pip install ‘google-adk’ and create a file ‘temp.py’

#pip install google-adk

# my_agent/agent.py
from google.adk.agents import Agent
from google.adk.tools import google_search

root_agent = Agent(
    name="search_assistant",
    model="gemini-2.0-flash-exp", # 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])

2. Create the below file and run cmd command

You can explore more examples here:

GitHub – google/adk-python: An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

Concluding,

Google’s open-source Agent SDK lets developers build multi-agent systems for real-world tasks. Key features include modular design, human-like interactions (text/audio/video), task delegation between agents, and seamless deployment on Google Cloud. It supports Gemini and other AI models, pre-built tools (search, code execution), and integrates with LangChain/LlamaIndex.

vs. Competitors

Google ADK: Best for scalable cloud production with strong testing tools.

LangGraph: Ideal for complex workflows needing error handling.

CrewAI: Simplifies role-based teamwork but lacks scalability.

AutoGen: Great for code/chat generation, not large-scale systems.

Quick Start
Install via pip install google-adk, create an agent with tools (e.g., Google Search), and deploy. Example agents handle web searches or workflows. See GitHub for guides.

Hope you try out the framework.

Google Agent ADK: Google’s Multi AI Agent framework was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.