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OpenAI Agents SDK

An open-source, MIT-licensed Python framework for building and coordinating multi-agent LLM workflows. It supports configurable agents, tool use, handoffs and tracing.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

OpenAI Agents SDK — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project needs structured orchestration across multiple LLM-driven roles rather than a single isolated model interaction.

Who it is for

  • Python developers building applications with multiple cooperating LLM agents
  • Teams designing research, support, document-processing or business-automation workflows
  • Developers who need support for OpenAI APIs and other LLMs

Documented uses

  • Coordinate handoffs between specialized agents
  • Delegate research or processing tasks across multiple LLM agents
  • Add tool-enabled steps to developer assistants
  • Orchestrate multi-step business automations
  • Trace activity within agent workflows
Before you choose

Requirements & limits

The project is available through a public GitHub repository, is MIT-licensed and implemented in Python. Listed platforms include CLI, cloud, self-hosted and server environments.

  • Python is the primary implementation language
  • The SDK supports OpenAI Responses and Chat Completions APIs plus other LLMs
  • Agent handoffs and tool use are central workflow concepts
  • Tracing is included among the documented capabilities
  • The repository is public, active and MIT-licensed

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run a first text agent

Create a Python environment, install the SDK, configure the API key, and run the documented text-agent example.

Before you start

  • Python 3.10 or newer
  • An OPENAI_API_KEY environment variable before running the example

Steps

  1. Create and activate a virtual environment, then install the package with the command below.

    Documented command
    pip install openai-agents

  2. Set the OPENAI_API_KEY environment variable.

  3. Create an Agent with instructions and call Runner.run_sync with a prompt.

  4. Print result.final_output.

Expected outcome

The text agent returns generated output for the supplied prompt.

Guide limitations

  • The documented example uses the OpenAI API key and does not document a local model setup.
  • The source does not document dependency pinning, deployment, or error handling.
Documentation 1 ↗Documentation 2 ↗
BUILD WITH THIS PROJECT

Documented task routes

COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
OpenAI Agents SDKCoordinate handoffs between specialized agentsCLI, Cloud, Self-hosted, ServerThe project is available through a public GitHub repository, is MIT-licensed and implemented in Python. Listed platforms include CLI, cloud, self-hosted and server environments.
AgeniteDefine typed tools with JSON Schema validation and error handling.CLI
VeldraCreate an agent with a policy, tools, skills, knowledge base and reasoning method.Self-hostedThe documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
openai/openai-agents-python · Official homepage title · retained in its source language
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