Remarkable velocity for age. Google's enterprise distribution machinery is a genuine advantage.
Google Agent Development Kit (ADK)
active4.3M PyPI downloads/month for a framework under 10 months old. v1.27.2 (2026-03-17), multi-language (Python, TypeScript, Go, Java), model-agnostic (Gemini, Claude, Ollama, vLLM, LiteLLM). Native Cloud Run + Vertex AI Agent Engine deployment. ADK 2.0 Alpha adds graph-based workflows. Best for GCP/Vertex AI teams.
Where it wins
4.4M PyPI downloads/month — fastest absolute star growth (18.5K in <12 months)
Multi-language: Python, TypeScript, Go, Java — widest language breadth in category
Model-agnostic despite Google origins: Gemini, Claude, Ollama, vLLM, LiteLLM
Pre-built Workflow agents (Sequential, Parallel, Loop) reduce boilerplate
Native Cloud Run + Vertex AI Agent Engine deployment — unique GCP advantage
Most complete DevOps story: built-in evaluation, testing, containerization, deployment pipelines
Named customers: Renault Group, Box, Revionics (Google blog, self-reported)
Where to be skeptical
GCP/Vertex lock-in concern — unfavorable tradeoff vs LangGraph for non-GCP teams
Bi-weekly release cadence may introduce instability
Named customers from Google-controlled publications only
Editorial verdict
Strong download velocity for its age — GCP-native deployment and multi-language commitment give it a longer runway than single-language frameworks. Best for teams already on GCP/Vertex AI. No independently-verified production case studies outside Google-controlled publications.
Source
Videos
Reviews, tutorials, and comparisons from the community.
Introduction to Google ADK
Related

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LangGraph
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Pydantic AI
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AutoGen (Microsoft)
95⚠️ MAINTENANCE MODE — Microsoft officially confirmed bug fixes and security patches only, no new features (VentureBeat 2026-02-19). 55.9K stars but only 1.57M PyPI/month — DL/star ratio of 28, the most inflated among active frameworks. Being replaced by Microsoft Agent Framework (AutoGen + Semantic Kernel merge, GA targeted ~Q2 2026). Teams on AutoGen should plan migration.
Public evidence
Multi-language support (not just Python) and graph workflow additions in ADK 2.0 Alpha show investment in longevity beyond Python-first peers.
Named enterprise customers reported via Google blog. Self-reported but names are verifiable companies.
Raw GitHub source
GitHub README peek
Constrained peek so you can sanity-check the source material without leaving the site.
Agent Development Kit (ADK) 2.0
<a href="https://codewiki.google/github.com/google/adk-python"><img src="https://www.gstatic.com/_/boq-sdlc-agents-ui/_/r/Mvosg4klCA4.svg" alt="Ask Code Wiki" height="20"></a>
Agent Development Kit (ADK) is a flexible and modular framework that applies software development principles to AI agent creation. It is designed to simplify building, deploying, and orchestrating agent workflows, from simple tasks to complex systems. While optimized for Gemini, ADK is model-agnostic, deployment-agnostic, and compatible with other frameworks.
⚠️ BREAKING CHANGES FROM 1.x
This release includes breaking changes to the agent API, event model, and session schema. Sessions generated by ADK 2.0 are readable by ADK 1.28+ (extra fields will be ignored), but are incompatible with older 1.x versions.
✨ Key Features
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Workflow Runtime: A graph-based execution engine for composing deterministic execution flows for agentic apps, with support for routing, fan-out/fan-in, loops, retry, state management, dynamic nodes, human-in-the-loop, and nested workflows.
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Task API: Structured agent-to-agent delegation with multi-turn task mode, single-turn controlled output, mixed delegation patterns, human-in-the-loop, and task agents as workflow nodes.
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Modular Multi-Agent Systems: Design scalable applications by composing multiple specialized agents into flexible hierarchies.
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Rich Tool Ecosystem: Utilize pre-built tools, custom functions, OpenAPI specs, MCP tools or integrate existing tools to give agents diverse capabilities, all for tight integration with the Google ecosystem.
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Code-First Development: Define agent logic, tools, and orchestration directly in Python for ultimate flexibility, testability, and versioning.
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Agent Config: Build agents without code. Check out the Agent Config feature.
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Tool Confirmation: A tool confirmation flow (HITL) that can guard tool execution with explicit confirmation and custom input.
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Deploy Anywhere: Easily containerize and deploy agents on Cloud Run or scale seamlessly with Vertex AI Agent Engine.
🚀 Installation
Stable Release (Recommended)
You can install the latest stable version of ADK using pip:
pip install google-adk
Requirements: Python 3.10+.
For transitive dependency protection, we recommend to install with our companion constraints files (for python 3.10 to 3.14).
Choose the constraints file matching your Python version:
# For example, for Python 3.10
curl -o constraints-3.10.txt https://raw.githubusercontent.com/google/adk-python/main/constraints-3.10.txt
pip install google-adk -c constraints-3.10.txt
rm constraints-3.10.txt
To install optional integrations, you can use the following command:
pip install "google-adk[extensions]"
The release cadence is roughly bi-weekly.
Development Version
Bug fixes and new features are merged into the main branch on GitHub first. If you need access to changes that haven't been included in an official PyPI release yet, you can install directly from the main branch:
pip install git+https://github.com/google/adk-python.git@main
Note: The development version is built directly from the latest code commits. While it includes the newest fixes and features, it may also contain experimental changes or bugs not present in the stable release. Use it primarily for testing upcoming changes or accessing critical fixes before they are officially released.
Quick Start
Beginner Note: ADK applications are built using two main classes:
Agent(defines an AI's instructions, tools, and behavior) andWorkflow(orchestrates agents and tasks in a graph-based flow).
Agent
from google.adk import Agent
root_agent = Agent(
name="greeting_agent",
model="gemini-2.5-flash",
instruction="You are a helpful assistant. Greet the user warmly.",
)
Workflow
from google.adk import Agent, Workflow
generate_fruit_agent = Agent(
name="generate_fruit_agent",
instruction="Return the name of a random fruit. Return only the name.",
)