Google Agent Development Kit vs Strands Agents SDK

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, Samples & ADK Web.

ADK in other languages: ADK Java, ADK Kotlin, ADK Go & ADK Typescript.

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.

✨ 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 Agent Runtime on Gemini Enterprise Agent Platform
  (formerly 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 3.11+.

For transitive dependency protection, we recommend installing with our companion
constraints files (for python 3.11 to 3.15).

Choose the constraints file matching your Python version:

## For example, for Python 3.11
curl -o constraints-3.11.txt https://raw.githubusercontent.com/google/adk-python/main/constraints-3.11.txt
pip install google-adk -c constraints-3.11.txt
rm constraints-3.11.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-3.5-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

Run the bundled evaluation sample from the repo root:

adk eval \
    contributing/samples/evaluation/home_automation_agent \
    contributing/samples/evaluation/basic_criteria/home_automation.evalset.json \
    --config_file_path contributing/samples/evaluation/basic_criteria/eval_config.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

• Getting Started: https://adk.dev/
• Guides: See
  docs/guides/
  for task-oriented walkthroughs of agents, tools, events, plugins, and
  workflows.
• Samples: See
  contributing/samples/
  for runnable example agents.

🤝 Contributing

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

• General contribution guideline and flow.
• Code Contributing Guidelines to get started.

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 llms-full.txt files published on the ADK documentation site can be used as context for an LLM. The former is a summarized version, while the latter contains the full documentation for LLMs with a large 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!

Strands Agents

A model-driven approach to building AI agents in just a few lines of code.

Documentation ◆ Samples ◆ CLI ◆ MCP Server ◆ Discord
Harness (Python, TypeScript) ◆ SDK (Python, TypeScript) ◆ Box ◆ Shell ◆ Evals ◆ Labs

Strands Agents is an open-source SDK for building and running AI agents in Python and TypeScript. Choose Strands when you would otherwise write your own agent loop: it runs in your process with no hosted control plane, and it covers the jobs a hand-rolled loop grows into. In one SDK you get lifecycle controls (turn limits, token budgets, cancellation, stop reasons), tools and structured output, MCP, multi-agent patterns, memory and sessions, model portability, streaming, guardrails, tracing, and evals.

Using a coding agent? Install the Strands plugin for Claude Code, Codex, or Cursor.
It adds framework selection, scaffolding, tool authoring, MCP server connections, web
framework guides, and LangGraph migration guidance. See
Use Strands with AI coding agents
for install and verification steps.

This monorepo contains Strands harness, the Python and TypeScript SDKs, the documentation site, and supporting packages:

Why Strands

Build an agent harness. Control it end-to-end.

• Build your way. Any model, any cloud. Context management, execution limits, and observability built in before you write a line of config. Swap backends when you scale; your code stays the same.
• Model agnostic. First-class support for Amazon Bedrock, Anthropic, OpenAI, and Gemini, plus many more providers and custom ones.
• Stay in control. The agent loop traces every decision by default. Hooks let you intercept any step to log it, validate it, or redirect it.
• Deliver outcomes that work. Guardrails catch mistakes before they run. Steering handlers let agents correct themselves instead of failing silently.

MCP, streaming, multi-agent patterns, and structured output are all built in.

Quick Start

The easiest way to get started is with Strands harness, a fully assembled, state-of-the-art agent. A single create_harness() (Python) or createHarness() (TypeScript) call gives you an optimized agent with benchmarked defaults for the model, tools, memory, sessions, and context management — ready to take from idea to production. Follow the harness quickstart, or see the Python Strands harness and TypeScript Strands harness packages to get started.

Python

pip install strands-harness

from strands_harness import create_harness

agent = create_harness()
agent("Find the slowest test in this repo and explain why it's slow")

TypeScript

npm install @strands-agents/harness

import { createHarness } from '@strands-agents/harness'

const agent = await createHarness()
await agent.invoke("Find the slowest test in this repo and explain why it's slow")

Start here to get a batteries-included agent, then drop down to the SDKs below when you want to own the agent loop and wire up tools, model providers, and memory yourself. The harness configuration reference documents every default you can override.

The Quickstart Guide covers configuring providers (Amazon Bedrock, Anthropic, OpenAI, Gemini, Ollama, and more).

Working with the SDK

The Strands Harness SDK lets you go deeper and control every part of the agent: the loop, tools, model providers, memory, sessions, and hooks. You can dive into the SDK after working with Strands harness or if you prefer building your own harness from the ground up when the assembled defaults aren't enough.

Python

Requires Python 3.10+:

pip install strands-agents

from strands import Agent
from strands.vended_tools import http_request

agent = Agent(tools=[http_request])
agent("Tell me about the Strands harness features: https://strandsagents.com/docs/user-guide/harness/index.md")

The Python SDK README covers tools, model providers, MCP, and bidirectional streaming.

TypeScript

To build a Strands agent in TypeScript, install the SDK in a Node.js 22+ ESM project ("type": "module"):

npm install @strands-agents/sdk

import { Agent } from '@strands-agents/sdk'
import { httpRequest } from '@strands-agents/sdk/vended-tools/http-request'

const agent = new Agent({ tools: [httpRequest] })
await agent.invoke('Tell me about the Strands harness features: https://strandsagents.com/docs/user-guide/harness/index.md')

The agent uses Amazon Bedrock by default, so configure AWS credentials or pass another model.
Start with the TypeScript quickstart
to configure a model provider, run your first agent, and add tools. Use the
TypeScript API reference for the full
SDK surface, or explore more examples in the TypeScript SDK README.

Documentation

For detailed guidance & examples, explore our documentation:

• User Guide
• Strands Harness Guide (quickstart · configuration reference)
• Quick Start Guide
• Agent Loop
• Bidirectional Streaming (Python)
• Examples
• API Reference: Python · TypeScript
• Production & Deployment Guide

The docs themselves live in this monorepo under site/, and doc PRs are welcome alongside code changes.

Development

Git operations (commits, branches, PRs) are done from the repo root. Each package has its own toolchain:

Python SDK (strands-py/):

cd strands-py
pip install hatch
hatch test        # run unit tests
hatch fmt         # format & lint

TypeScript SDK (strands-ts/):

npm ci            # install from repo root
npm run build     # build
npm test          # run unit tests

Documentation site (site/):

cd site
npm install
npm run dev       # local dev server at http://localhost:4321/

Contributing ❤️

We welcome contributions! See our Contributing Guide for details on:

• Reporting bugs & features
• Development setup
• Contributing via Pull Requests
• Code of Conduct
• Reporting of security issues

Stay in touch with the team

Come meet the Strands team and other users on Discord

License

This project is licensed under the Apache License 2.0 - see the LICENSE.APACHE file for details.

Security

See CONTRIBUTING for more information.