Official Resources

Docs Repository Python SDK Java SDK Sample Agents Developer UI Launch Blog Vertex Integration GitHub Tutorial

Key Features

  • Multi-agent Orchestration: Hierarchical, sequential, parallel, or loop workflows for complex agent coordination.
  • Rich Tool Ecosystem: Pre-built tools (Search, Code Exec), custom Python functions, OpenAPI endpoints, MCP servers, or other agents as tools.
  • Streaming Support: Bidirectional SSE, WebSocket, audio, and video streaming for interactive agents.
  • Built-in Evaluation: End-to-end response and step-level evaluation tooling for agent performance.
  • Deploy Anywhere: Container-ready with native integration for Vertex AI Agent Engine and Cloud Run.
  • Developer UI (adk-web): Angular-based UI for real-time debugging, tracing, and workflow visualization.
  • Open Protocols: Supports Agent2Agent (A2A) and Model Context Protocol (MCP) for interoperability.

Code Examples

Installation

bash 
pip install google-adk

Single Agent Setup

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

root_agent = Agent(
    name="search_assistant",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant. Answer questions using Google Search when needed.",
    tools=[google_search]
)

Multi-Agent Coordination

python 
from google.adk.agents import LlmAgent

greeting_agent = LlmAgent(
    name="greeter",
    model="gemini-2.0-flash",
    instruction="Provide a friendly greeting only.",
    description="Handles greetings"
)

weather_agent = LlmAgent(
    name="weather",
    model="gemini-2.0-flash",
    instruction="Use the get_weather tool to answer weather questions.",
    description="Returns weather data",
    tools=[get_weather]
)

root_agent = LlmAgent(
    name="coordinator",
    model="gemini-2.0-flash",
    instruction="Delegate to sub-agents based on user intent.",
    sub_agents=[greeting_agent, weather_agent]
)

Development UI

bash 
# Run the built-in development UI
adk web
# or start the API server only
adk api_server
# Visit http://localhost:4200 to chat, trace, and debug

Use Cases

  • Conversational assistants with search and code execution
  • Data pipelines orchestrating multi-step agents
  • B2B enterprise tools integrated with internal APIs
  • Interactive streaming UIs like voice or video assistants
  • Multi-agent mashups combining GitHub, chat, and data agents

Pros & Cons

Advantages

  • Code-first orchestration - Full developer control
  • Multi-language support - Python (mature) and Java (early v0.1.0)
  • Rich debugging and evaluation built in
  • Model-agnostic - Swap Gemini, OpenAI, Anthropic, etc.
  • Scalable deployment on Vertex AI or any container runtime
  • Open protocols (A2A, MCP) for cross-framework compatibility

Disadvantages

  • Early development - Expect occasional rough edges
  • Cloud familiarity required - Most value unlocked with Vertex AI, Cloud Run, IAM
  • Java ecosystem lags behind Python in maturity
  • Developer UI adds Angular/Node toolchain complexity

Future Outlook & Integrations

  • TypeScript & Go SDKs [Q4 2025]: First public releases with parity to Python 1.0 API
  • C# & Rust SDKs [H1 2026]: Road-mapped after TypeScript/Go stabilize
  • Agent Engine Autoscaling 2.0 [Aug 2025]: GPU-aware scale-to-zero, global edge endpoints
  • MCP Marketplace [Sep 2025]: Curated registry of vetted MCP servers with one-line installation
  • Vertex AI Fine-tune API [Oct 2025]: In-console fine-tuning of Gemini models directly from ADK traces
  • A2A v1.0 Protocol [Oct 2025]: Final spec with multi-org federated agent discovery & billing
  • Snowflake & Databricks MCP [Nov 2025]: Native connectors exposing SQL, warehouse, and feature-store tools
  • Slack / Teams Bot Templates [Dec 2025]: Ready-to-deploy agents with OAuth, mention handling, file threads
  • LangGraph → ADK Bridge [Jan 2026]: Drop-in wrapper allowing LangGraph graphs to run as ADK sub-agents