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It is deliberately small: a handful of primitives rather than a large abstraction layer. An agent is a model plus instructions and tools; **handoffs** let one agent delegate to another or be called as a tool; **guardrails** validate inputs and outputs in parallel with the run; and **sessions** keep conversation history automatically. Every run is traced by default, and traces can be viewed in the OpenAI dashboard or exported to other observability tools.\n\nThe April 2026 update added a **sandbox and harness layer** for long-horizon work. Sandbox agents run inside a container where they can inspect files, run commands, and edit code, with configurable memory and standard integrations. The SDK ships native adapters for several hosted sandbox providers, including E2B, Modal, Daytona, Cloudflare, and Vercel, so the execution environment is swappable. Realtime and voice agents with interruption handling are built in, and tools can come from plain functions, hosted OpenAI tools such as web and file search, or MCP servers.\n\nDespite the branding it is **provider-agnostic**: it runs on OpenAI's Responses and Chat Completions APIs by default and supports more than 100 other models through LiteLLM-style routing. The Python package (`openai-agents`) is the reference implementation and receives new features first; the TypeScript\u002FJavaScript port (`@openai\u002Fagents`) covers the core primitives and is catching up on the sandbox layer. Subagents and a code mode have been announced for both languages.\n\nThe SDK is **MIT-licensed** and free; the only cost is the model and hosted-tool usage an agent consumes. Independent comparisons usually place it as the quickest framework to get a working multi-agent setup, lighter than LangChain or LangGraph, which give finer control over graph-shaped workflows, and less opinionated than CrewAI's role-based crews.","A lightweight, powerful framework for multi-agent workflows.",[331,332,333,334,335,336,337,338],"Small set of primitives: agents, tools, handoffs, guardrails, and sessions","Built-in tracing with export to the OpenAI dashboard or other backends","Sandbox agents that run commands and edit files inside a container","Native adapters for hosted sandbox providers such as E2B, Modal, and Daytona","Realtime and voice agents with interruption detection","MCP server tools alongside hosted web search and file search","Works with OpenAI models and 100+ other providers","Python reference implementation plus an official TypeScript port",[340,341,342,343],"Very little boilerplate: a working multi-agent handoff takes a few dozen lines","Tracing is on by default, which makes debugging agent runs easy without extra setup","First-party support means new OpenAI features such as realtime voice and hosted tools arrive here first","Swappable sandbox providers keep code-execution agents from being tied to one host",[345,346,347,348],"Fewer control-flow abstractions than LangGraph for complex stateful workflows","Default tracing and hosted tools pull projects toward the OpenAI platform","The TypeScript port lags the Python package on newer features such as sandbox agents","Rapid release cadence means APIs and examples change between versions",{},[],[],{"tool_id":285,"agent_type":353,"primary_language":125,"open_source":3,"hosting_model":354,"github_stars":355,"github_stars_checked_at":356,"created_at":357,"updated_at":357},"framework","self_hosted",29736,"2026-09-30","2026-10-01T11:46:46.671427",[],1790856096789]