Verdict
Choose LangGraph Deep Agents for autonomous long-horizon tasks with subagent spawning. Choose OpenAI Agents SDK for fast, ergonomic, OpenAI-native multi-agent handoffs with Temporal-backed durability.
In early 2026, LangChain doubled down on Deep Agents — a harness built on LangGraph with native planning tools, filesystem backends, and context-isolated subagent spawning for complex open-ended tasks. The OpenAI Agents SDK, meanwhile, partnered with Temporal to add durable execution in production, closing the gap on the resumability argument. The real split is now between autonomous orchestration depth (LangGraph Deep Agents) versus ergonomic, provider-native multi-agent handoffs with durable infrastructure (OpenAI Agents SDK + Temporal).
Decision Table
LangGraph / Deep AgentsOpenAI Agents SDK
| Criterion | Edge | Explanation |
|---|---|---|
| Autonomous long-horizon task execution | LangGraph / Deep Agents Best edge | LangGraph Deep Agents ships a native planning tool, a filesystem backend, and context-isolated subagent spawning specifically for complex, open-ended tasks that run over extended durations. The OpenAI Agents SDK supports multi-agent handoffs but does not provide a built-in planning layer or subagent context isolation at the same depth. |
| Durable execution and production resilience | Tie | OpenAI Agents SDK added Temporal durable execution integration in February 2026 (Public Preview), meaning agents now automatically resume from rate limits, network failures, and crashes without manual checkpointing. LangGraph provides durable graph execution natively through LangGraph Platform's checkpoint store. Both are now viable for production durability, but through different mechanisms. |
| Workflow explicitness and debuggability | LangGraph / Deep Agents Best edge | LangGraph enforces explicit graph-based state transitions, branching, and approval checkpoints. That makes it significantly easier to trace, debug, and reproduce failure modes in complex multi-step workflows. Mixing model-driven and deterministic nodes is a first-class pattern. |
| Model-native ergonomics and speed to prototype | OpenAI Agents SDK Best edge | The OpenAI Agents SDK provides a tightly designed loop of Agents, Tools, Handoffs, and Guardrails built on the Responses API. Automatic tracing and the OpenAI dashboard are included out of the box. For teams already using OpenAI models, this gets a working multi-agent system running considerably faster than designing a LangGraph graph from scratch. |
| Multi-provider portability | LangGraph / Deep Agents Best edge | LangGraph's orchestration layer is provider-agnostic by design. The Agents SDK supports LiteLLM and over 100 LLMs beyond OpenAI, but its primitives and tooling are optimized for the OpenAI Responses API. Teams mixing cheaper or specialized models per task report real cost savings with LangGraph. |
| MCP and external tool integration | Tie | Both frameworks now support the Model Context Protocol for connecting external systems. The OpenAI Agents SDK exposes MCP tools as first-class hosted tools alongside web search and computer use. LangGraph integrates MCP through its tool layer but requires more explicit wiring. |
| Context isolation for complex multi-agent systems | LangGraph / Deep Agents Best edge | LangGraph Deep Agents introduces a dedicated subagent spawning primitive that isolates each subagent's context, keeping the main agent's token window clean while going deep on subtasks. The OpenAI Agents SDK handles multi-agent coordination through handoffs, which transfers context rather than isolating it. |
Choose LangGraph / Deep Agents if...
- Teams building long-running workflows where subagents need context isolation to prevent token bloat.
- Products that need orchestration logic independent of one model provider with mixed-cost model routing.
- Systems where explicit graph-level debugging and approval checkpoints are required for compliance or audit.
- Complex document processing or research workflows that benefit from Deep Agents' built-in planning and filesystem tools.
Choose OpenAI Agents SDK if...
- Teams already committed to OpenAI Responses API primitives and wanting tight SDK ergonomics.
- Small teams that need production-grade durable execution fast via the Temporal integration.
- Use cases where multi-agent handoffs, built-in web search, and hosted computer use tools are the primary capability.
- Teams that want automatic tracing and observability in the OpenAI dashboard without additional instrumentation.
Decision Rules
- If your system requires long-horizon autonomous tasks with planning, filesystem state, and spawning specialized subagents, use LangGraph Deep Agents.
- If your team is already on OpenAI-native tooling and needs fast setup with production-grade durability, use the OpenAI Agents SDK with Temporal.
- If portability across model providers is a hard requirement, or if you are mixing expensive and cheap models per task, default to LangGraph.
- If your workflow needs explicit state machines, branching, and human-in-the-loop approval checkpoints, LangGraph's graph structure pays off early.
- If the Assistants API is in your stack today, migrate to the OpenAI Responses API and Agents SDK before August 2026, when Assistants is sunset.
Migration Notes
- If you start with the OpenAI Agents SDK, keep tool contracts and approval boundaries isolated in discrete functions so orchestration can move to LangGraph later without a full rewrite.
- If you start with LangGraph, adopt Deep Agents only when task complexity genuinely requires subagent context isolation, not as a default. Over-modelling early adds maintenance cost.
- If you are on the legacy Assistants API, migrate to the Responses API and Agents SDK before the August 2026 deprecation deadline.
- When mixing both stacks, LangGraph can wrap OpenAI Agents SDK agents as compiled subagents via CompiledSubAgent, allowing incremental adoption without choosing a single entrypoint.