LangGraph vs OpenAI Agents SDK
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose LangGraph if you want low-level framework for stateful, durable, graph-based llm agents (Supervised agent, freemium); choose OpenAI Agents SDK if you want lightweight open-source framework for building multi-agent workflows (Supervised agent, free).
| LangGraph | OpenAI Agents SDK | |
|---|---|---|
| What it is | Low-level framework for stateful, durable, graph-based LLM agents | Lightweight open-source framework for building multi-agent workflows |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | freemium · Framework free (MIT); LangGraph Platform via LangSmith (free Developer tier) | free · Free (open source; pay underlying model usage) |
| Best for | developers, enterprise, mid-market | developers |
| Deployment | self-hosted, api, saas | self-hosted, api |
| Modalities | text, code, api | text, code, voice, api |
| Models | model-agnostic, gpt, claude, gemini, open-source | model-agnostic, gpt |
| Protocols | function-calling, mcp, rest-api | mcp, function-calling, rest-api |
| Integrations | OpenAI, Anthropic, Google, AWS Bedrock, LangSmith | OpenAI API, MCP servers, Python, JavaScript/TypeScript |
| Capabilities | 4 documented | 4 documented |
LangGraph
- +Explicit graph model makes complex agent control flow (loops, branching, multi-agent routing) inspectable and controllable
- +Production-grade primitives: durable execution, checkpointing/time-travel, and first-class human-in-the-loop interrupts
- +Open source and model-agnostic, with a hosted LangGraph Platform and LangSmith observability for deployment
- -Lower-level and more verbose than higher-level agent libraries; a steeper learning curve
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
OpenAI Agents SDK
- +Minimal, low-abstraction framework that is quick to learn
- +Built-in handoffs, guardrails, sessions, and tracing
- +Provider-agnostic (100+ LLMs) with Python and JS/TS (and voice) SDKs
- -A framework, not a product: you build, host, and secure the agent yourself
- -Autonomy and safety depend entirely on how the developer configures it
Which should you choose?
LangGraph is low-level framework for stateful, durable, graph-based llm agents, best for developers, enterprise, mid-market. OpenAI Agents SDK is lightweight open-source framework for building multi-agent workflows, best for developers. The right choice depends on the autonomy level you want, your existing integrations, and your budget, all compared above.
This comparison is generated from the sourced LangGraph and OpenAI Agents SDK profiles. Open either profile to review its evidence and last-reviewed date.