AI agents
Compare OpenAI Agents SDK and LangGraph for agent orchestration, tool execution, approvals, state, long-running workflows, tracing, and production control.
Comparison
| Option | Best for | Strengths | Tradeoffs | Use when |
|---|---|---|---|---|
| OpenAI Agents SDK | OpenAI-centered apps that want a supported SDK for tools, handoffs, guardrails, and sessions | Directly aligned with OpenAI APIs and agent patterns. | Less neutral if your architecture must treat many model providers equally. | OpenAI is your primary model and you want a focused framework around its agent primitives. |
| LangGraph | Long-running, stateful workflows, graph control, provider flexibility, and complex orchestration | Low-level control over state, nodes, edges, retries, and human-in-the-loop flows. | Requires more architectural discipline than a simpler SDK path. | You need explicit workflow state and want to own more of the agent runtime. |
| Application-owned loop | Small tools, strict compliance environments, and teams that need complete custom control | Maximum transparency and minimal framework dependency. | You must build orchestration, tracing, retries, approvals, and memory yourself. | Your flow is simple or your controls are too specialized for a framework. |
If the app needs one model call, a few tools, and deterministic application logic, a full agent framework may be unnecessary. Frameworks become useful when tasks span multiple steps, tools, approvals, state transitions, or specialist agents.
Production agents fail in the transitions: tool calls, retries, human approvals, partial state, background tasks, and recovery after interruption. Choose the framework that makes your state model reviewable.
Agent frameworks are only valuable if you can debug them. Record traces, tool inputs, model outputs, state changes, costs, and evaluator scores so failures become inspectable.
Decision Rules
Use OpenAI Agents SDK for OpenAI-native apps that want a supported orchestration path.
Use LangGraph for complex stateful workflows and provider-flexible architecture.
Keep a custom loop for simple flows or highly specialized controls.
Do not add agent orchestration until tool permissions and logging are clear.
Related Guides
Design tool access before adding agent orchestration.
OpenCompare Dify, n8n, LangGraph, MCP, and custom agent stacks.
OpenControl tool access before giving an agent more power.
OpenTrace and evaluate agent behavior in production.
OpenCompare agent concepts in coding workflows.
OpenTopic Hubs
FAQ
It depends on the architecture. OpenAI Agents SDK is a strong fit for OpenAI-native apps. LangGraph is a strong fit for stateful, graph-controlled, provider-flexible workflows.
Yes. LangGraph is commonly used with many model providers, including OpenAI, but the key difference is that you own more of the graph and state model.
Avoid one when the app is a simple request-response workflow. A smaller application-owned loop can be easier to test and operate.
Source Links
The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.
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