adk-python Documentation

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

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Docker & Self-Hosted Deployment Verified OCI

Deploy adk-python on any server, homelab, or cloud container platform.

1-Click Terminal Command
bash (docker run) 
docker run -d --name adk-python -p 5432:5432 --restart always postgres:16-alpine
Configured Services & Ports
⚙️ postgres
image: postgres:16-alpine
🔌 Ports: 5432:5432
docker-compose.yml
yaml (docker-compose.yml) 
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Docker Compose configuration for the postgres_session_service sample.
#
# This file defines a PostgreSQL service used to demonstrate ADK's
# DatabaseSessionService with a persistent backend. It sets up a
# postgres:16-alpine container with:
# - Default credentials (user: postgres, password: postgres)
# - A pre-created database named 'adk_sessions'
# - Port 5432 exposed for local access
# - A named volume 'postgres_data' for data persistence

services:
  postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: adk_sessions
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

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.

Important Links: [Docs](https://google.github.io/adk-docs/), [Samples](https://github.com/google/adk-samples) & [ADK Web](https://github.com/google/adk-web).

ADK in other languages: [ADK Java](https://github.com/google/adk-java), [ADK Kotlin](https://github.com/google/adk-kotlin), [ADK Go](https://github.com/google/adk-go) & [ADK Typescript](https://github.com/google/adk-js).

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

You can install the latest stable version of ADK using pip:

bash
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:

bash
# 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:

bash
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:

bash
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

python
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

python
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

bash
# 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

bash
adk eval \
    samples_for_testing/hello_world \
    samples_for_testing/hello_world/hello_world_eval_set_001.evalset.json

📚 Documentation

🤝 Contributing

We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our:

Community Repo

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.

Community Events

  • [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!

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