OpenAI's official open-source framework for building agentic AI applications with minimal abstractions. Production-ready successor to Swarm, providing agents, handoffs, guardrails, and tracing primitives that work with Python and TypeScript.
OpenAI's official toolkit for building AI agents that can use tools, hand off tasks, and follow guardrails — backed by the makers of ChatGPT.
The OpenAI Agents SDK is OpenAI's official open-source framework for building agentic AI applications, replacing the experimental Swarm project with a production-ready, supported solution. Available for both Python and TypeScript, the SDK is deliberately minimal—providing just enough abstractions to be useful without creating a steep learning curve.
The SDK is built on a small set of primitives: Agents (LLMs equipped with instructions and tools), Handoffs (allowing agents to delegate to other agents), and Guardrails (input/output validation that runs in parallel with agent execution). These primitives, combined with native Python or TypeScript, are powerful enough to express complex multi-agent workflows.
The agent loop handles tool invocation automatically—calling tools, sending results back to the LLM, and continuing until the task is complete. Function tools are created from regular Python/TypeScript functions with automatic schema generation and Pydantic-powered validation. MCP server tools integrate identically to function tools.
Key features include Sessions (persistent memory for maintaining context across agent runs), Human-in-the-loop mechanisms for involving humans in agent decisions, and built-in Tracing for visualizing, debugging, and monitoring workflows. Traces integrate with OpenAI's evaluation, fine-tuning, and distillation tools.
The SDK also supports Realtime Agents for building voice-based agents using gpt-realtime-1.5, with automatic interruption detection, context management, and guardrails.
While designed as provider-agnostic with documented paths for non-OpenAI models, the SDK works best with OpenAI's model lineup including GPT-4o, o3, and GPT-4o-mini. It's MIT-licensed and open-source, with API usage billed separately per OpenAI's standard pricing.
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The OpenAI Agents SDK delivers on its promise of minimal abstractions for maximum capability. Its three-primitive architecture (agents, handoffs, guardrails) keeps the learning curve low while the tracing and evaluation pipeline provides genuine production value. Best suited for teams building on OpenAI models who want structured agent patterns without heavy framework overhead.
Built on just three core abstractions—Agents, Handoffs, and Guardrails—plus Python/TypeScript as the orchestration language. No custom DSLs or complex abstractions to learn.
Use Case:
A developer builds a multi-agent customer support system in an afternoon using standard Python patterns, without learning framework-specific concepts.
Agents can delegate tasks to specialized agents mid-conversation, with automatic context transfer and conversation continuity. Enables modular agent architectures.
Use Case:
A triage agent routes customer inquiries to specialized billing, technical support, or sales agents based on intent, with full conversation context passed through.
Input validation and safety checks run in parallel with agent execution rather than sequentially, with fast-fail behavior when checks don't pass.
Use Case:
A financial advisor agent validates user inputs for PII and checks output for compliance with regulations, all running concurrently with the main agent loop.
Built-in support for MCP (Model Context Protocol) server tools that work identically to native function tools, enabling agents to connect to any MCP-compatible tool ecosystem.
Use Case:
An agent connects to a company's internal MCP servers for database access, document retrieval, and API calls without custom integration code.
Persistent memory layer for maintaining working context within and across agent runs, enabling stateful conversations and long-running workflows.
Use Case:
A research assistant agent maintains context about a user's ongoing project across multiple conversation sessions over days or weeks.
Comprehensive tracing for visualizing and debugging agent workflows, with direct integration into OpenAI's evaluation, fine-tuning, and model distillation tools.
Use Case:
Using trace data to fine-tune a smaller model (GPT-4o-mini) to replicate the behavior of a more expensive agent (o3), reducing production costs by 90%.
$0
$0.15 / $0.60 per 1M tokens (input/output)
$2.50 / $10 per 1M tokens (input/output)
$10 / $40 per 1M tokens (input/output)
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In 2025, OpenAI launched the Agents SDK as the production successor to Swarm, with both Python and TypeScript SDKs. Key additions include native MCP server integration, Sessions for persistent memory, Human-in-the-loop mechanisms, Realtime Agent support for voice applications, and deep integration with OpenAI's evaluation and fine-tuning pipeline. The SDK is provider-agnostic by design.
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