openai

openai-agents-python

+340

// summary

The OpenAI Agents SDK is a lightweight framework designed for building complex multi-agent workflows. It supports a wide range of LLMs and provides essential features like tool integration, guardrails, and human-in-the-loop capabilities. Developers can also utilize sandbox agents for long-running tasks and leverage built-in tracing to debug and optimize their agentic applications.

// technical analysis

The OpenAI Agents SDK is a provider-agnostic framework designed for orchestrating complex multi-agent workflows. By abstracting core concepts like agents, tools, and guardrails, it enables developers to build scalable systems that support both simple tasks and long-horizon operations via sandbox environments. The architecture prioritizes modularity and observability, offering built-in tracing and session management to simplify the debugging and maintenance of agentic interactions.

// key highlights

Supports over 100 LLMs, ensuring flexibility by being provider-agnostic beyond OpenAI's own APIs.

Features Sandbox Agents that provide controlled environments for agents to execute filesystem operations and long-running tasks.

Includes built-in guardrails to enforce safety and validation checks on both agent inputs and outputs.

Implements native tracing capabilities to help developers visualize, debug, and optimize agent workflows.

Provides a human-in-the-loop mechanism to allow manual intervention during agent execution cycles.

Supports Realtime Agents using gpt-realtime-1.5, enabling the creation of sophisticated voice-based agent applications.

// use cases

Building multi-agent workflows with task delegation and handoffs

Executing long-horizon tasks using sandbox agents with filesystem access

Developing real-time voice agents with integrated conversation history and safety guardrails

// getting started

To begin, ensure you have Python 3.10 or newer installed and set your OPENAI_API_KEY environment variable. Install the SDK using pip or uv, optionally including the 'voice' or 'redis' extras for additional functionality. You can then explore the provided examples directory or run a Sandbox Agent by defining a manifest and using the Runner class to execute tasks.