Google Agent Development Kit vs OpenAI Agents SDK (Python)

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!

OpenAI Agents SDK

The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.

[!NOTE]
Looking for the JavaScript/TypeScript version? Check out Agents SDK JS/TS.

Core concepts:

1. Agents: LLMs configured with instructions, tools, guardrails, and handoffs
2. Sandbox agents: Agents preconfigured to work with a container to perform work over long time horizons.
3. Realtime agents: Build powerful voice agents with gpt-realtime-2.1 and full agent features
4. Voice agents: Build voice pipelines that combine speech-to-text, an agent workflow, and text-to-speech
5. Agents as tools / Handoffs: Delegating to other agents for specific tasks
6. Tools: Various Tools let agents take actions (functions, MCP, hosted tools)
7. Guardrails: Configurable safety checks for input and output validation
8. Human in the loop: Built-in mechanisms for involving humans across agent runs
9. Sessions: Automatic conversation history management across agent runs
10. Tracing: Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows

Explore the examples directory to see the SDK in action, and read our documentation for more details.

Get started

To get started, set up your Python environment (Python 3.10 or newer required), and then install OpenAI Agents SDK package.

venv

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install openai-agents

For voice support, install with the optional voice group: pip install 'openai-agents[voice]'. For Redis session support, install with the optional redis group: pip install 'openai-agents[redis]'.

uv

If you're familiar with uv, installing the package would be even easier:

uv init
uv add openai-agents

For voice support, install with the optional voice group: uv add 'openai-agents[voice]'. For Redis session support, install with the optional redis group: uv add 'openai-agents[redis]'.

Run your first agents

The SDK supports four primary ways to run agents. Set the OPENAI_API_KEY environment variable before running any of these examples.

Run a text agent

Use a text Agent for workflows that do not need a persistent realtime connection or a sandbox workspace.

from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")

result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

## Code within the code,
## Functions calling themselves,
## Infinite loop's dance.

(For Jupyter notebook users, see hello_world_jupyter.ipynb)

Run a sandbox agent

Use a SandboxAgent when the agent needs to inspect files, run commands, apply patches, or preserve workspace state across longer tasks.

This example uses UnixLocalSandboxClient, which is supported on macOS and Linux. On Windows, use DockerSandboxClient with the openai-agents[docker] extra or a hosted sandbox client instead; see Sandbox clients for setup details.

from agents import Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.entries import GitRepo
from agents.sandbox.sandboxes import UnixLocalSandboxClient

agent = SandboxAgent(
    name="Workspace Assistant",
    instructions="Inspect the sandbox workspace before answering.",
    default_manifest=Manifest(
        entries={"repo": GitRepo(repo="openai/openai-agents-python", ref="main")}
    ),
)

result = Runner.run_sync(
    agent,
    "Inspect the repo README and summarize what this project does.",
    run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),
)
print(result.final_output)

Run a realtime agent

Use a RealtimeAgent for low-latency, server-side voice and multimodal experiences over WebSocket.

import asyncio
from agents.realtime import RealtimeAgent, RealtimeRunner

async def main() -> None:
    agent = RealtimeAgent(
        name="Assistant", instructions="You are a helpful voice assistant. Keep responses short."
    )
    runner = RealtimeRunner(starting_agent=agent)
    session = await runner.run()

    async with session:
        await session.send_message("Say hello in one short sentence.")
        async for event in session:
            if event.type == "audio":
                # Forward or play event.audio.data.
                pass
            elif event.type == "history_added":
                print(event.item)
            elif event.type == "agent_end":
                break

if __name__ == "__main__":
    asyncio.run(main())

Run a voice agent

Use a VoicePipeline to turn audio into text, run an agent workflow, and stream generated speech.

import asyncio

import numpy as np

from agents import Agent
from agents.voice import AudioInput, SingleAgentVoiceWorkflow, VoicePipeline

async def main() -> None:
    agent = Agent(name="Assistant", instructions="You are a helpful voice assistant.")
    pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent))
    audio_input = AudioInput(buffer=np.zeros(24000 * 3, dtype=np.int16))

    result = await pipeline.run(audio_input)
    async for event in result.stream():
        if event.type == "voice_stream_event_audio":
            # Forward or play event.data.
            pass

if __name__ == "__main__":
    asyncio.run(main())

Explore the examples directory to see the SDK in action, and read our documentation for more details.

Contributing

Please share bug reports and feature requests through GitHub issues.
Pull requests are limited to repository collaborators; we do not accept pull requests from non-collaborators.
See CONTRIBUTING.md for the contribution policy and development guide.
For security vulnerabilities, follow SECURITY.md.

Acknowledgements

We'd like to acknowledge the excellent work of the open-source community, especially:

• Pydantic
• Requests
• MCP Python SDK
• Griffe

This library has these optional dependencies:

• websockets
• SQLAlchemy
• any-llm and LiteLLM

We also rely on the following tools to manage the project:

• uv and ruff
• mypy and Pyright
• pytest and Coverage.py
• MkDocs

We're committed to continuing to build the Agents SDK as an open source framework so others in the community can expand on our approach.