OpenAI Agents SDK
A lightweight yet powerful framework for building multi-agent workflows with built-in tracing, guardrails, and handoffs. Production-ready upgrade of the Swarm framework.
26.9K
GitHub Stars
4.1K
Forks
100+
LLM Providers
Python & TS
Languages
Overview
The OpenAI Agents SDK is a lightweight, production-ready framework for building multi-agent AI applications. Released in March 2025 as a production-grade upgrade of the experimental Swarm framework, it operates around three fundamental primitives: Agents (LLMs configured with instructions and tools), Handoffs (agents delegating tasks to specialized agents), and Guardrails (validation mechanisms for inputs and outputs). Despite its name, the SDK is provider-agnostic and supports over 100 LLMs through the Chat Completions API, with both Python and TypeScript implementations maintaining feature parity.
The Verdict
Who Should Use OpenAI Agents SDK?
Best For
- Rapid prototyping of multi-agent systems with minimal boilerplate
- Teams already invested in the OpenAI ecosystem wanting minimal abstraction
- Projects requiring built-in observability and tracing out of the box
- Customer support automation with agent handoffs between specialized roles
- Developers prioritizing shipping speed over complex orchestration patterns
- Applications needing three-tier guardrails (input, output, tool) for safety
Not Ideal For
- Complex graph-based workflows with cyclical logic (consider LangGraph)
- Applications requiring built-in long-term memory or cross-session persistence
- Systems with more than 8-10 agent types where handoffs become unwieldy
- Projects needing arbitrary graph topologies instead of linear handoff chains
What's Great
- Minimal learning curve — Requires just a few lines of code to get started with clean, opinionated API
- Built-in observability — Automatic tracing of agent runs without custom instrumentation, with OpenAI Traces dashboard
- Production-ready guardrails — Three-tier validation (input, output, tool) running in parallel by default
- Clean handoff model — Considered the cleanest agent-to-agent delegation pattern in the ecosystem
- Provider-agnostic — Supports 100+ LLMs beyond OpenAI, avoiding vendor lock-in
- Dual-language support — Full feature parity between Python and TypeScript implementations
- Native sandbox execution — Agents run in controlled container environments with files, tools, and dependencies
- Voice agent support — Real-time voice agents via gpt-realtime with interruption detection
- MCP integration — HostedMCPTool exposes remote MCP server tools directly to models
Watch Out For
- No built-in long-term memory — State persistence relies on ephemeral context variables; requires external memory solutions
- Linear handoff chains only — No support for arbitrary graph topologies; handoff pattern unwieldy with 8+ agent types
- Minimal architecture — Lacks graph-based workflow engines and opinionated planning systems by design
- Session-scoped state — Only handles conversational state within sessions; durable memory must be added externally
- Relatively new — Released March 2025, less battle-tested than LangChain/LangGraph ecosystem
Pricing
View all features & details
Core Primitives
- Agents — LLMs with instructions, tools, guardrails, and handoffs
- Handoffs — Agent-to-agent delegation with conversation history
- Guardrails — Input, output, and tool validation running in parallel
- Tracing — Built-in observability with OpenAI Traces dashboard
- Sessions — Automatic conversation history management
Tool Types
- Hosted OpenAI Tools — Web search, file search, code interpreter, image generation
- Function Tools — @function_tool decorator for Python functions
- Agents as Tools — Expose agents as callable tools without full handoff
- MCP Integration — HostedMCPTool and ToolSearchTool for deferred loading
- Sandbox Tools — ComputerTool, ApplyPatchTool, ShellTool in containers
Advanced Features
- Sandbox agents running in isolated container environments
- Voice agents with gpt-realtime-2 and interruption detection
- Human-in-the-loop integration options
- Persistent memory sessions across turns
- Blocking or parallel guardrail execution modes
- Automatic schema generation for function tools
Requirements
- Python 3.10+ or Node.js/TypeScript
- pip install openai-agents
- Works on Linux, macOS, Windows
- Docker support for sandbox agents
- Cloud platform compatible (AWS, Azure, GCP)
How It Compares
| Feature | OpenAI Agents SDK | LangGraph | CrewAI |
|---|---|---|---|
| Learning Curve | Beginner-friendly | Steep (graph concepts) | Medium |
| Development Speed | Rapid prototyping | Slower initial setup | Moderate |
| Workflow Patterns | Linear handoff chains | Complex cyclical graphs | Role-based teams |
| Built-in Tracing | Yes, automatic | Requires setup | Basic |
| Long-term Memory | No (external needed) | Built-in checkpointing | External |
| Guardrails | Three-tier built-in | Manual implementation | Manual |
| GitHub Stars | 26.9K | 10K+ | 27K+ |
| Best For | Simple multi-agent chains | Complex stateful graphs | Collaborative agent teams |
Language Support
Python 3.10+ and TypeScript with full feature parity across both implementations
License
MIT License — free for commercial use with no restrictions
Release
March 2025 — Production-ready upgrade of experimental Swarm framework
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