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Your Complete Guide to OpenAI Agent: Everything You Need to Know about the OpenAI Agents SDK

Descubre todo sobre OpenAI Agents SDK, el marco en Python para crear flujos de trabajo con agentes de IA. Aprende conceptos clave, ejemplos y mejores prácticas aquí.

Your Complete Guide to OpenAI Agent

Estimated reading time: 8 minutes

Key Points

  • The OpenAI Agents SDK facilitates the creation of advanced and flexible artificial intelligence workflows, bringing together agents, tools, and safety validations.
  • Agents can coordinate with each other, integrate custom tools, and perform tracing for problem diagnosis and optimization.
  • It offers support for real-time voice and multimodal workflows (in beta), expanding the possibilities of human-AI interaction.
  • The session memory and the ability to manage persistent context facilitate complex conversations.
  • While powerful, it has limitations in code execution, global conversational memory, and extended multimodal support.

Table of Contents

Key concepts of OpenAI Agents SDK

The OpenAI Agents SDK in Python allows you to build flexible, scalable, and coordinated AI workflows, connecting multiple agents and tools under one framework (official documentation).

  • Agents: They are the core elements, configured with instructions, tools, guards, and handoffs. They enable simple tasks or the coordination of multiple steps (source).

  • Tools: They extend the capabilities of agents, which can be Python functions or APIs like web searching or file handling (source).

  • Handoffs: Delegation mechanism to other agents, enabling multi-agent workflows (more info).

  • Guards: Validate inputs/outputs to ensure accuracy and safety, enabling the stopping of faulty executions (source).

  • Sessions: Enable state management and conversational history, allowing for persistent context (importance of memory).

  • Tracing: Tracks, visualizes, and debugs executions, facilitating the evaluation and improvement of agents (external guide).

Design principles and features

  • Lightweight and Python focus: Minimal abstractions and familiar code facilitate integration (detail).

  • Multiple agent orchestration: Primitives for collaborative workflows with tools and chained handoffs (source).

  • Provider agnostic: Adaptable to any LLM with wrapper libraries (more than 100 already available) (source).

  • Voice and multimodality support (Beta): Support for real-time interaction via voice, exchanging audio and text.

  • Expandable tools: Easily add custom functions or utilize out-of-the-box web searches (more on context).

  • Robust validation with guardrails: Allows comprehensive security and parallel error detection (source).

  • Tracing and monitoring: Visualize, debug, and evaluate workflows easily (source).

Example of implementation with an agent that responds to stock actions, using web search as a tool: 

from agents import Agent, Runner, WebSearchTool

agent = Agent(
    name="Finance Agent",
    instructions="You are a finance agent that can answer questions about stocks. Use web search to retrieve up‑to‑date context. Then, return a brief, concise answer that is one sentence long.",
    tools=[WebSearchTool()],
    model="gpt-4.1-mini",
)
running = Runner(agent)
running.run("What is Apple's stock price today?")

Source: here

Advanced capabilities

  • Real-time voice support: Natural conversations with low latency, capable of pausing/interruption, both input and output (guide).

  • Efficient multi-agent orchestration: Handoffs allow contextual delegation of complex tasks (applied example).

  • Parallel tool use: Supports concurrent calls to reduce latency in critical flows (source).

Design limitations

  • Memory and code execution: There is no persistent user memory, nor native code execution; it must be developed externally (details here).

  • Retry logic: Not managed by the SDK; failures must be handled by the developer (documentation).

  • Partial multimodal support: Currently only voice, with no image or video input/output (source).

Getting Started

Quick installation: 

pip install openai-agents

For voice support: 

pip install 'openai-agents[voice]'

Or with uv for quick start: 

uv add openai-agents
uv add 'openai-agents[voice]'

Check the official guides and technical documentation or external resources like this complete explanation.

Reference guides and examples

Summary table: Main components

Component Purpose/Function Example/Details
Agent Instructions, tools, guards, decisions, and action Specialized LLM according to task
Tools Extension of agent’s skills Web searching, file retrieval
Handoffs Delegation between agents Research agent → Evaluation agent
Guards Input/output validation Schema control and security
Sessions Contextual/state management/history Conversational memory
Tracing Diagnosis, visualization, and continuous improvement Execution graphs and logs

For constantly updated information, check the official documentation and resources like this specialized article.

Frequently asked questions

Is the SDK only for OpenAI models?

No. It is adaptable to more than 100 LLM providers thanks to wrapper library integrations (more details).

What can I achieve using agents and tools?

You can design conversational assistants, financial agents, search systems, clinical applications, and more, combining custom tools and multi-agent flows.

Does it have conversational memory between sessions?

Only session memory is native (importance explained here). For long-term memory, you must implement it.

Can it be integrated into voice applications?

Yes, with real-time voice support you can build voice assistants and multimodal agents (beta).

Where can I see complete examples or patterns?

Check the Phoenix cookbook and OpenAI guides.

Additional sources

Do you have questions or want to share your experience using agents? Leave them in the comments.