A Developer's Guide to AI Agents: OpenAI Agents SDK vs. Google's ADK

The era of AI agents is upon us. As developers, we’re moving beyond simple API calls to building sophisticated, autonomous systems that can reason, plan, and execute complex tasks. To empower this shift, major AI labs are releasing specialized frameworks. Two of the most prominent are the OpenAI Agents SDK and Google’s Agent Development Kit (ADK).

Both are designed to simplify the creation of multi-agent workflows, but they approach the problem with different philosophies, features, and target ecosystems. This guide will provide a detailed, code-level comparison of the two frameworks, helping you decide which is the right fit for your next project.

What is the OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight, Python-first framework for building agentic applications. Its core philosophy is to provide a minimal set of powerful primitives and then get out of the way, allowing developers to use standard Python for orchestration.

OpenAI’s Philosophy: Lightweight & Unopinionated

The SDK feels like a library, not a restrictive platform. It gives you the core components (Agent, Runner, Tool, Handoff) and trusts you to build the surrounding application logic in a way that best suits your needs.

Its main components are:

• Agent: The fundamental building block. It’s an LLM configured with instructions, tools, and potential handoffs to other agents.
• Runner: The execution engine. It takes a starting agent and an input, then manages the “agent loop”—calling the LLM, executing tools, and processing handoffs until a final output is produced.
• Tools: Agents can use tools to interact with the outside world. The SDK provides a simple @function_tool decorator to turn any Python function into a tool, and also supports “Hosted Tools” like Web Search and File Search when using the OpenAI Responses API.
• Handoffs: A specialized type of tool that allows one agent to delegate control to another, enabling complex, multi-agent collaboration.
• Tracing: Built-in, enabled-by-default tracing that integrates with the OpenAI Traces dashboard to help you debug and visualize your agent workflows.

A key feature is its provider-agnostic design, with built-in support for the Chat Completions API and an integration with LiteLLM, allowing it to work with over 100 different models.

What is Google’s Agent Development Kit (ADK)?

Google’s ADK is a comprehensive, code-first toolkit designed for building, evaluating, and deploying sophisticated AI agents, with a strong focus on the Google Cloud and Gemini ecosystem.

Google’s Philosophy: A Full-Fledged Development Platform

The ADK is more than just a library; it’s a full development platform. It comes with a powerful command-line interface (adk), a local web UI for debugging, and built-in features for evaluation and deployment.

Its architecture is more structured and geared towards enterprise applications:

• Agent: Similar to OpenAI’s, this is the core component, configured with a model and instructions. ADK offers different agent types for orchestration, like SequentialAgent, ParallelAgent, and LoopAgent.
• Runner: The execution engine, responsible for running agents within a session.
• Tools & Toolsets: ADK boasts a massive and rich ecosystem of pre-built tools, especially for Google services. This includes BigQueryToolset, APIHubToolset, VertexAiSearchTool, and ApplicationIntegrationToolset. A Toolset is a collection of related tools.
• Services (SessionService, ArtifactService, MemoryService): ADK formalizes state and data management with dedicated services. These have in-memory implementations for local development and production-ready backends like DatabaseSessionService and GcsArtifactService.
• adk CLI & Dev UI: A powerful command-line tool (adk web, adk deploy, adk eval) and a local web UI provide a robust developer experience for testing, evaluating, and deploying agents.

Head-to-Head: Similarities and Differences

At a high level, both frameworks share common ground in their core concepts.

Similarities

Feature	Common Approach
Core Concept	Both are code-first Python frameworks designed to build and orchestrate AI agents.
Agent Primitives	Both use a central Agent class as the main building block, configured with instructions and tools.
Execution	Both use a Runner class to manage the agent execution loop (LLM calls, tool execution, etc.).
Tooling	Both natively support turning Python functions into tools that the agent can call.
Multi-Agent	Both frameworks provide primitives for creating multi-agent systems where agents can delegate tasks to each other.

Key Differences

The real distinction lies in their philosophy and the breadth of their out-of-the-box features.

Feature	OpenAI Agents SDK	Google Agent Development Kit (ADK)
Philosophy	Lightweight and unopinionated. Provides core primitives, leaving orchestration to the developer’s Python code.	Comprehensive and platform-focused. Provides a full development, evaluation, and deployment lifecycle.
Orchestration	Relies on Handoffs and “Agents as Tools” patterns, orchestrated programmatically.	Offers declarative SequentialAgent, ParallelAgent, and LoopAgent types for structured orchestration.
State Management	A simple, mutable context object is passed through the Runner. State is managed by the developer.	Formalized SessionService, ArtifactService, and MemoryService with database and cloud (GCS) backends.
Tool Ecosystem	Simple @function_tool decorator. Hosted tools (Web Search, Code Interpreter) tied to OpenAI Responses API.	Massive ecosystem of pre-built Toolsets for Google services (BigQuery, Vertex AI Search, API Hub).
Developer Experience	Focuses on simplicity and Python-native development. Built-in tracing is a key debugging feature.	Rich CLI (adk web, adk deploy) and a local Web UI for interactive debugging, visualization, and testing.
Evaluation	No built-in evaluation framework. Relies on external tools or custom scripts.	A first-class evaluation framework with EvalSet, EvalCase, and adk eval command for structured testing.
Provider Agnosticism	High. LitellmModel integration supports 100+ models out-of-the-box.	Lower. Optimized for Gemini and Google Cloud, though other models can be used.

Use Cases: When to Choose Which?

Your choice of framework will likely depend on your project’s requirements, existing infrastructure, and development philosophy.

Choose OpenAI Agents SDK When…

• You need flexibility and a provider-agnostic solution. The LiteLLMModel integration is a killer feature if you want to experiment with or use models from Anthropic, Cohere, or open-source providers alongside OpenAI’s.
• You prefer a lightweight, “Python-first” approach. If you dislike learning new abstractions and prefer to orchestrate logic using standard Python (asyncio, if/else, loops), this SDK will feel natural.
• Your project is a startup or a prototype. The simplicity and speed of development make it excellent for getting a powerful agent up and running quickly without the overhead of a larger platform.
• Your state management needs are simple. If a simple context object is sufficient for your application’s state, the OpenAI SDK is a great fit.

Choose Google’s Agent Development Kit (ADK) When…

• You are building for the Google Cloud ecosystem. The deep integration with services like BigQuery, Vertex AI Search, and Application Integration is a massive advantage.
• You need an enterprise-grade, end-to-end solution. ADK provides a robust lifecycle from local development (adk web) to structured testing (adk eval) and deployment (adk deploy).
• You are building complex, stateful applications. The built-in SessionService and ArtifactService with database and GCS backends are designed for production applications that need to persist state and data.
• You value a rich set of pre-built, specialized tools. If your agent needs to interact with various Google services, ADK saves you a tremendous amount of time by providing ready-to-use toolsets.

A Simple Code Comparison

Let’s look at a simple example of an agent that uses a tool to see the similarities and differences in code.

OpenAI Agents SDK

from agents import Agent, Runner, function_tool

# Turn a Python function into a tool
@function_tool
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."

# Define the agent
agent = Agent(
    name="Hello world",
    instructions="You are a helpful agent.",
    tools=[get_weather],
)

# Run the agent
result = await Runner.run(agent, input="What's the weather in Tokyo?")
print(result.final_output)
# The weather in Tokyo is sunny.

Google ADK

from google.adk.agents import Agent
from google.adk.tools import google_search

# Define the agent with a pre-built tool
root_agent = Agent(
    name="search_assistant",
    model="gemini-2.0-flash",
    instruction="Answer user questions using Google Search when needed.",
    description="An assistant that can search the web.",
    tools=[google_search]
)

# Runner is typically managed by the CLI/web server,
# but can be instantiated for programmatic use.
# from google.adk.runners import InMemoryRunner
# runner = InMemoryRunner(agent=root_agent)
# ... run logic ...

The basic agent definition is conceptually similar, but the surrounding ecosystem and tooling are where they diverge. OpenAI’s example is self-contained, whereas the power of Google’s ADK often comes from using its full platform capabilities.

Conclusion

Both the OpenAI Agents SDK and Google’s ADK are powerful, well-designed frameworks that significantly lower the barrier to building complex AI agents.

• OpenAI Agents SDK offers a flexible, lightweight, and provider-agnostic path for developers who want to stay close to Python and build custom orchestration logic.
• Google’s ADK provides a structured, enterprise-ready platform with deep integration into the Google Cloud ecosystem, complete with a robust development and deployment lifecycle.

The choice isn’t about which is “better,” but which is the right tool for your job. By understanding their core philosophies and features, you can confidently select the framework that will best accelerate your journey into the exciting world of AI agents.