OpenAI Agents SDK
Open SourceA lightweight, powerful framework for multi-agent workflows.
Published 1 October 2026
Scores
Popularity4/5
Around 30K GitHub stars on the Python repo and the default starting point in OpenAI's own documentation and cookbook, which puts it among the most-used agent frameworks alongside LangChain and CrewAI.
Learning Curve2/5
Five core concepts and plain Python functions as tools make the first agent quick to write, and the official examples cover handoffs, guardrails, and voice end to end.
Flexibility4/5
Handoffs, agents-as-tools, custom guardrails, MCP, 100+ model providers, and pluggable sandboxes cover most agent designs, though graph-level control over branching state is thinner than in LangGraph.
Performance4/5
A thin runtime over the model APIs adds little overhead, parallel guardrails avoid blocking the main run, and sandboxed agents handle long-horizon coding and file tasks in production.
Portability4/5
MIT-licensed with support for non-OpenAI models and multiple sandbox vendors, so an agent can move providers, although tracing and hosted tools work best on OpenAI's platform.
About OpenAI Agents SDK
The OpenAI Agents SDK is OpenAI's official open-source agent framework, the production successor to its experimental Swarm project. 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.
The 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.
Despite 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/JavaScript port (@openai/agents) covers the core primitives and is catching up on the sandbox layer. Subagents and a code mode have been announced for both languages.
The 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.
Key Features
- 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
Pros
- 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
Cons
- 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
OpenAI Agents SDK Pricing
Open SourceTools Related to OpenAI Agents SDK
Integrates with OpenAI Agents SDK(5)
E2B is one of the hosted sandbox providers with a native adapter in the OpenAI Agents SDK, so sandbox agents can run commands and edit files inside an E2B microVM.
Modal is a built-in sandbox provider in the OpenAI Agents SDK: sandbox agents run their commands and file edits inside Modal Sandboxes through the SDK's adapter.
The OpenAI Agents SDK ships a native Daytona adapter, letting its sandbox agents execute commands and work with files in a Daytona sandbox.
The OpenAI Agents SDK ships an optional LiteLLM extension (the LitellmModel class) that routes agents to non-OpenAI models through LiteLLM's provider layer.
The OpenAI Agents SDK is OpenAI's own agent framework and runs on OpenAI's Responses and Chat Completions APIs by default, with hosted web search and file search tools.
Built on (2)
The OpenAI Agents SDK is a Python framework (the openai-agents package), and the Python edition receives new features first.
The @openai/agents package is the SDK's official TypeScript and JavaScript port, covering agents, handoffs, guardrails, and sessions.
Alternatives to OpenAI Agents SDK(8)
These are the two model vendors' own agent SDKs. The OpenAI Agents SDK is provider-agnostic and built from small primitives; the Claude Agent SDK is Claude-only but ships Claude Code's full harness, with file, shell, and web tools ready to use.
Both target multi-agent systems in Python. The OpenAI Agents SDK keeps to minimal primitives and handoffs, while CrewAI is more opinionated, organising agents into role-based crews with assigned tasks.
The OpenAI Agents SDK is a small set of primitives (agents, handoffs, guardrails, sessions) that gets a multi-agent setup running quickly; LangChain offers a far larger library of integrations and components for teams that need retrieval, many providers, and custom chains.
Both are lightweight Python agent frameworks that work with many model providers. The OpenAI Agents SDK adds handoffs, built-in tracing, sandbox agents, and voice; Pydantic AI centres on type-safe, validated outputs and dependency injection.
The OpenAI Agents SDK runs a ready-made agent loop with handoffs between agents; LangGraph makes you define the graph, state, and branching explicitly. Pick the SDK for speed to a working agent, LangGraph for fine control over complex stateful workflows.
The OpenAI Agents SDK focuses on agent loops, handoffs, and tool orchestration; LlamaIndex is the stronger choice when the hard part is retrieval over documents, with data connectors, indexes, and parsing built in.
Vendor
OpenAI
Website →Tags
Details
- Maintained
- Yes
- Agent type
- Framework
- Primary language
- Python
- Open source
- Yes
- Hosting
- Self-hosted
- GitHub stars
- 29.7k
- Stars updated
- 2026-09-30