LangChain Deep Agents vs OpenAI Agents SDK (2026)
This article compares two competing Python frameworks for building AI agents: LangChain Deep Agents and the OpenAI Agents SDK. It highlights their different architectures, features, and use cases.
Why it matters
The choice between these two AI agent frameworks can have a significant impact on the development and maintenance of complex AI applications.
Key Points
- 1LangChain Deep Agents is an 'agent harness' with built-in planning, context management, and subagent spawning
- 2OpenAI Agents SDK is a lightweight toolkit with minimal primitives for composing agent workflows
- 3Deep Agents is better suited for long-running, stateful tasks, while OpenAI Agents SDK excels at multi-agent handoff workflows
Details
The article provides a detailed comparison of the two frameworks, covering their architecture, language support, planning capabilities, memory management, multi-agent features, context handling, tracing, guardrails, and model support. Deep Agents is built on top of the LangChain framework and provides more opinionated, batteries-included functionality, while the OpenAI Agents SDK takes a more lightweight, composable approach. The choice between the two depends on the specific needs of the AI application, with Deep Agents better suited for long-horizon, stateful tasks and the OpenAI Agents SDK more suitable for multi-agent handoff workflows.
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