Unpacking OpenAI’s Agents SDK: A Guide

OpenAI is introducing several new offerings: Responses API, built-in tools for web and file search, a computer use tool and the open-source Agents SDK. While the Responses API lets developers build agents atop its tech, the Agents SDK can help them link agents to other web tools and processes, performing “workflows” that do what the user or business wants, autonomously.

2025 is often hailed as the “Year of Agents” and OpenAI’s move is seen as a key step for the industry. The Agents SDK allows developers to easily leverage OpenAI’s latest advances (such as improved reasoning, multimodal interactions, and new safety techniques) in real-world, multi-step scenarios. For LLM developers and AI agent builders, the Agents SDK provides a set of “building blocks” to create and manage their own autonomous AI systems.

The significance of the Agents SDK lies in its ability to address the challenges of deploying AI agents in production environments. Traditionally, translating powerful LLM capabilities into multi-step workflows has been labor-intensive, requiring a lot of custom rule writing, sequential prompt design, and trial and error without proper observability tooling. With the Agents SDK and related new API tools such as the Responses API, OpenAI aims to significantly simplify this process, enabling developers to build more complex and reliable agents with less effort.

What is Agents SDK

OpenAI is getting back into open source in a big way with the release of its Agents SDK, a toolkit designed to help developers manage, coordinate and optimize agent workflows — even building agents powered by other, non-OpenAI models such as those by competitors Anthropic and Google, or open-source models from DeepSeek, Qwen, Mistral and Meta’s Llama family.

Why use the Agents SDK

The SDK has two driving design principles:

1. Enough features to be worth using, but few enough primitives to make it quick to learn.
2. Works great out of the box, but you can customize exactly what happens.

Here are the main features of the SDK:

• Agent loop: Built-in agent loop that handles calling tools, sending results to the LLM, and looping until the LLM is done.
• Python-first: Use built-in language features to orchestrate and chain agents, rather than needing to learn new abstractions.
• Handoffs: A powerful feature to coordinate and delegate between multiple agents.
• Guardrails: Run input validations and checks in parallel to your agents, breaking early if the checks fail.
• Function tools: Turn any Python function into a tool, with automatic schema generation and Pydantic-powered validation.
• Tracing: Built-in tracing that lets you visualize, debug and monitor your workflows, as well as use the OpenAI suite of evaluation, fine-tuning and distillation tools.

How to use Openai Agents SDK

1. Set up your Python environment

python -m venv env
source env/bin/activate

2. Install Agents SDK

pip install openai-agents

3. set the OPENAI_API_KEY environment variable

Freely set the OPENAI_API_KEY API from CometAPI

• Log in to cometapi.com. If you are not our user yet, please register first
• Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
• Get the url of this site: https://api.cometapi.com/
• Select the OPENAI_API_KEY endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience.

4. Set Up Your Agent

Define what tools your AI can use. Let’s say we want to enable web search and file retrieval:

from agent_sdk import Agent, WebSearchTool, FileRetrievalTool

search_tool = WebSearchTool(api_key="your_api_key")
file_tool = FileRetrievalTool()

agent = Agent(tools=[search_tool, file_tool])

Now your agent knows how to search the web and fetch documents.

5. run

Unlike traditional chatbots, this AI decides which tool to use based on user input:

def agent_task(query):
    result = agent.use_tool("web_search", query)
    return result

response = agent_task("Latest AI research papers")
print(response)

No manual intervention—just autonomous execution.

The Agent Loop

When you call Runner.run(), the SDK runs a loop until it gets a final output:

1. The LLM is called using the model and settings on the agent, along with the message history.
2. The LLM returns a response, which may include tool calls.
3. If the response has a final output, the loop ends and returns it.
4. If the response has a handoff, the agent is set to the new agent and the loop continues from step 1.
5. Tool calls are processed (if any) and tool response messages are appended. Then the loop continues from step 1.

You can use the max_turns parameter to limit the number of loop executions.

​Final Output

Final output is the last thing the agent produces in the loop:

• If you set an output_type on the agent, the final output is when the LLM returns something of that type using structured outputs.
• If there’s no output_type (i.e., plain text responses), then the first LLM response without any tool calls or handoffs is considered the final output.

Hello world example

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.

Technical Structure

“The OpenAI Agents SDK aims to be a conceptual framework demonstrating how different agents, such as a ‘Triage Agent’ or a ‘CRM Agent,’ can collaborate to complete tasks via tool interactions and delegation mechanisms.”

Core Components and Architecture of Agents SDK

The OpenAI Agents SDK is built on a concise yet robust set of principles. At its core is the concept of the Agent, which represents an instance of a language model tailored with specific instructions and equipped to use various tools. Agents start by receiving user requests — such as questions or task definitions — then break down these tasks into subtasks that may involve using predefined tools, eventually delivering a complete response. These Tools are functionally described as callable functions; leveraging the Agents SDK, any Python function can seamlessly serve as a tool, with automatic schema validation for inputs and outputs provided via Pydantic. For example, Python functions representing a database query tool or a web search tool can be integrated directly into an agent’s toolkit.

Another central piece of the Agents SDK is the Agent Loop, which defines the iterative process of task resolution. Starting with an initial attempt to answer a query, an agent evaluates whether it has sufficient information or needs to perform external actions. When needed, the agent invokes a relevant tool, processes the output, and reassesses the task. This cycle repeats until the agent signifies task completion with an “I’m done” response. Agents SDK manages this process autonomously, simplifying the development process by automating recurring tasks like tool invocation, result handling, and iterative retries. This allows developers to focus more on defining workflows and agent capabilities without worrying about underlying mechanics. OpenAI describes this approach as Python-first, emphasizing the use of familiar Python constructs — such as loops, conditionals, and function calls — over domain-specific languages (DSLs). With this flexibility, developers can orchestrate interconnected agents while relying on native Python syntax.

Handoff and Multi-Agent Architecture

The SDK’s capabilities go beyond individual agents. Through a feature known as Handoff, tasks can transfer between multiple agents, enabling them to collaborate seamlessly. For example, a “Triage Agent” might determine the nature of an incoming query, delegating it to another specialized agent, or one agent’s output might act as input for another. This system supports workflows where specialized agents execute distinct parts of a broader task, empowering complex multi-agent architectures. OpenAI has designed the toolkit for scalable applications, such as customer support automation, research processes, multi-step projects, content creation, sales operations, or even code reviews. Additionally, Guardrails enhance reliability by imposing validation rules on agent inputs or outputs. For instance, guardrails can enforce parameter format compliance or terminate the loop early when anomalies are detected, reducing risks like inefficient execution or undesired behaviors in real-world operations.

Orchestration and Monitoring

Beyond task execution, the Agents SDK includes robust orchestration features, taking charge of tool execution, data flows, and loop management. Despite the high level of automation, OpenAI prioritizes transparency, equipping developers with tools to monitor agent activity in real time. Through the built-in Tracing feature accessible in the OpenAI dashboard, developers can visualize workflows, step-by-step, observing when tools are called, the inputs they use, and the outputs they return. The platform utilizes OpenAI’s monitoring infrastructure to break down the execution of agent logic into traces and spans, offering granular insights into agent behavior. This empowers developers to diagnose bottlenecks, debug issues, optimize workflows, and track performance. Moreover, the tracing architecture supports sophisticated evaluations, enabling fine-tuning and improvement of agent performance over time.

Advantages

OpenAI Agents SDK is not only for individual developers, it also provides significant advantages to companies building AI agent-based products. Let’s start with the advantages:

Fast Prototyping and Production: Agents SDK implements complex agent behaviors with minimal code and configuration, shortening the cycle from idea to product. For example, mainstream crypto platform Coinbase uses SDK to quickly prototype and deploy multi-agent support systems. Similarly, in areas such as enterprise search assistants, companies can integrate SDK’s web and file search tools to quickly deliver value. By offloading orchestration details, developers can focus on product-specific features.

Reduced Development Costs: Building an agent system from scratch requires a significant engineering investment. Agents SDK reduces costs by providing ready-made solutions for common needs – loop management, API call synchronization, error handling, and formatted tool output for LLM. Being open source, it also allows customization to meet the needs of the company. This is a boon to startups, enabling them to create powerful agent-driven products with limited resources.

Traceability and Debugging: The SDK’s integrated tracking dashboard transforms business applications. Industry concerns about AI being a “black box” now allow every agent step to be logged and audited. If a customer support agent gives the wrong answer, the trace shows which tool call or step failed. The OpenAI Platform’s log/trace screen improves the auditability of agents — critical in industries subject to regulation or internal audits. This allows companies to integrate AI with greater confidence, knowing they can explain the results when needed.

Access to OpenAI’s latest models and tools: Using the Agents SDK means taking advantage of OpenAI’s top models (e.g. GPT-4) and current tools (web search, code execution). This provides a quality advantage over building alternatives that may rely on weaker models. For applications that require high accuracy or up-to-date information (e.g. research assistants, financial analysis agents), the performance of OpenAI’s models is a big advantage. As OpenAI adds tools (hinting at more integrations to come), SDK users can easily adopt them.

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Related topics CometAPI: The Ultimate AI Model Integration Platform

Conclusion

OpenAI is dedicated to advancing AI capabilities with innovative offerings like the Responses API. By introducing these tools, businesses and developers gain the chance to build smarter, more adaptable, and highly reliable AI solutions. These developments point to a future where artificial intelligence continues to drive impactful changes and unlock new possibilities across industries.