Agent Development Kit (ADK) 2.0
An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
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
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.
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.
Modular Multi-Agent Systems: Design scalable applications by composing
multiple specialized agents into flexible hierarchies.
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.
Code-First Development: Define agent logic, tools, and orchestration
directly in Python for ultimate flexibility, testability, and versioning.
Agent Config: Build agents without code. Check out the
Agent Config feature.
Tool Confirmation: A tool confirmation flow (HITL) that can guard tool execution with explicit confirmation and custom input.
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) and
Workflow (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.",
)
generate_benefit_agent = Agent(
name="generate_benefit_agent",
instruction="Tell me a health benefit about the specified fruit.",
)
root_agent = Workflow(
name="root_agent",
edges=[("START", generate_fruit_agent, generate_benefit_agent)],
)
Run Locally
# Interactive CLI
adk run path/to/my_agent
# Web UI (supports multi-agent directories or pointing directly to a single agent folder)
adk web path/to/agents_dir
Development UI
A built-in development UI to help you test, evaluate, debug, and showcase your agent(s).
Evaluate Agents
adk eval \
samples_for_testing/hello_world \
samples_for_testing/hello_world/hello_world_eval_set_001.evalset.json
🚀 Deployment Options
Deploying the Agent Locally with Docker Container:
adk deploy docker --with_ui <agent-folder>
Deploying the Agent in Google Cloud (Cloud Run)
adk deploy cloud_run --with_ui <agent-folder>
You may set the following environment variables in adk command, or in a .env file instead.
adk deploy cloud_run --with_ui --env GOOGLE_GENAI_USE_ENTERPRISE=1 <agent-folder>
📚 Documentation
🤝 Contributing
We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our:
We have the adk-python-community repo that is home to a growing ecosystem of community-contributed tools, third-party
service integrations, and deployment scripts that extend the core capabilities
of the ADK.
Vibe Coding
If you want to develop an agent via vibe coding the llms.txt and the llms-full.txt can be used as context to an LLM. While the former one is a summarized one and the latter one has the full information in case your LLM has a big enough context window.
- [Completed] ADK's 1st community meeting on Wednesday, October 15, 2025. Remember to join our group to get access to the recording, and deck.
📄 License
This project is licensed under the Apache 2.0 License — see the
LICENSE file for details.
Happy Agent Building!
Agent Development Kit（ADK）是谷歌开源的、代码优先的 Python 框架，用于构建、评估和部署复杂的 AI 智能体——从简单任务到复杂的多智能体系统都可以胜任。