Google Agent Development Kit (ADK): Build Multi-Agent Systems in Python

TL;DR:

• Google ADK is an open-source Python framework for building multi-agent systems, with first-class support for Gemini models and Google Cloud deployment
• It introduces a structured Agent → Tool → Runner model that’s more opinionated than LangChain but easier to deploy to production
• Best for teams already in the Google Cloud ecosystem or building agents that need to interoperate with other ADK-based agents

Google released the Agent Development Kit (ADK) as open source in April 2025, and it has quietly become one of the more production-ready frameworks for building multi-agent systems in Python. While LangChain and LlamaIndex remain dominant in the research and prototyping space, ADK takes a different approach: it’s designed from the ground up for deployment, with built-in support for agent-to-agent communication, streaming, and integration with Google Cloud’s managed infrastructure.

What ADK Actually Is

ADK is a Python SDK (google-adk) that gives you three core primitives:

Agent — the unit of intelligence. An Agent has a model (Gemini by default, but any model via the LiteLLM integration), a set of tools, and optionally a set of sub-agents it can delegate to. You define agents as Python classes or using ADK’s declarative configuration format.

Tool — a callable action an agent can take. Tools can be Python functions, API calls, or other agents. ADK handles the function-calling protocol automatically — you define the function signature and docstring, and the model figures out when to call it.

Runner — the execution engine. The Runner manages the event loop, streaming output, session state, and agent lifecycle. For production, you deploy your Runner to Cloud Run or Agent Engine (Google’s managed agent hosting service), and ADK handles the infrastructure.

from google.adk.agents import Agent
from google.adk.tools import google_search

research_agent = Agent(
    name="researcher",
    model="gemini-2.0-flash",
    description="Searches the web and summarises findings",
    tools=[google_search],
    instruction="You are a research assistant. Use web search to find current information."
)

Multi-Agent Architecture with ADK

Where ADK differentiates itself is in agent-to-agent delegation. Rather than building custom orchestration logic, you define a coordinator agent and pass specialist agents as tools:

from google.adk.agents import Agent

writer_agent = Agent(
    name="writer",
    model="gemini-2.0-flash",
    description="Writes polished content from research briefs",
    instruction="Write clear, structured content based on the brief provided."
)

coordinator = Agent(
    name="coordinator",
    model="gemini-2.0-pro",
    description="Orchestrates research and writing tasks",
    agents=[research_agent, writer_agent],
    instruction="Break down the user's request, delegate research and writing to specialist agents, and combine the outputs."
)

The coordinator can call research_agent and writer_agent as if they were tools, passing context and receiving structured responses. ADK handles the message routing, state passing, and streaming between agents.

How It Compares to LangChain and CrewAI

LangChain’s LangGraph is more flexible for complex stateful workflows — if you need custom graph topologies or fine-grained control over execution order, LangGraph is still the better choice. CrewAI’s role-based model is faster to get started with for team-style orchestration. ADK sits in the middle: more structured than CrewAI, more deployment-ready than LangChain, but less flexible than LangGraph for complex custom graphs.

When to Choose ADK

ADK is a strong choice when:

• You’re on Google Cloud — Agent Engine, Cloud Run, and Vertex AI integrations work out of the box; you get managed scaling, logging, and monitoring without extra configuration
• You need streaming multi-agent outputs — ADK’s streaming support is solid and works end-to-end through the agent delegation chain
• You want opinionated structure — ADK’s constraints (defined tool signatures, structured agent config) make code easier to maintain and review than ad-hoc LangChain chains
• You’re building agent ecosystems — ADK supports the Agent-to-Agent (A2A) protocol, meaning ADK agents can communicate with agents built on other frameworks that implement A2A

It’s a weaker choice if you’re using non-Google models exclusively, need complex branching logic (use LangGraph), or are building primarily in JavaScript (no official JS SDK yet).

Getting Started

pip install google-adk
adk create my-agent  # scaffolds a project
adk run              # runs locally with web UI
adk deploy cloud-run # deploys to Cloud Run

The local runner includes a web UI at localhost:8000 where you can chat with your agent, inspect tool calls, and trace the execution flow across sub-agents. For teams evaluating the framework, this is the fastest path to seeing multi-agent behaviour in action.

ADK won’t replace LangChain for every use case, but for Python teams building production multi-agent systems on Google Cloud, it’s now the most frictionless path from prototype to deployment.