OpenAI Agents SDK vs Anthropic Claude Agent SDK: Which One to Build On

OpenAI Agents SDK (the successor to Swarm) and Anthropic's Claude Agent SDK are the two model-vendor frameworks most enterprise teams compare when standardizing on an agent toolkit. Both ship the same core primitives—tool use, multi-step loops, subagents, observability—but with different design philosophies and ecosystem strengths. The choice is rarely about features and almost always about which model family you trust your hardest workloads to.

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 18, 2026

OpenAI Agents SDK at a glance

OpenAI Agents SDK is a lightweight Python framework with a small surface area: Agent, Runner, handoffs (the OpenAI term for [[subagent]] delegation), and built-in tracing through the OpenAI dashboard. Its big advantage is direct integration with OpenAI's hosted Responses API, code interpreter, file search, and computer use tools—you get capabilities without integrating third-party services. Best fit: teams already committed to GPT-4 and o-series models who want fastest path to production.

Anthropic Claude Agent SDK at a glance

Anthropic's Claude Agent SDK is the framework Anthropic uses internally to build Claude Code, and the same abstractions ship for external developers. Strong primitives for context engineering (cache_control, system prompt structure), subagents, the [[agent-skills]] / Claude Skills directory pattern, and native MCP support. Best fit: teams that prioritize Claude's reasoning and coding quality, want first-class MCP integration, or need careful control over context window economics on long-running agents.

Side-by-side: where they differ

Built-in tools: OpenAI ships more (code interpreter, file search, image generation, computer use) hosted on their infrastructure; Anthropic ships fewer hosted tools but deeper MCP integration. Tracing: OpenAI has built-in dashboard tracing; Anthropic relies on third-party observability (LangSmith, Logfire, Braintrust). Context engineering: Anthropic exposes more knobs for prompt caching and skill loading; OpenAI's caching is more automatic. Subagent ergonomics: roughly equivalent—both let you define specialized agents with focused tools and prompts.

Choosing for your situation

Choose OpenAI Agents SDK if: you're already invested in the OpenAI stack, you need built-in code interpreter or image generation, or you want the lowest-effort path to a working production agent. Choose Anthropic Agent SDK if: you've benchmarked Claude as better on your tasks (often true for coding, long-context reasoning, and tool-use reliability), you're building heavily around MCP, or you need granular context engineering. For teams that haven't picked a model yet: run both SDKs on your eval set with their respective default models before committing—the model quality on your specific workload almost always dominates SDK ergonomics.

FAQ

Frequently asked questions.

Can I switch models without switching SDKs?

Partially. Both SDKs are designed primarily for their parent model family. You can call other models through them in some cases, but you lose features that depend on model-specific capabilities (Anthropic's cache_control, OpenAI's hosted tools). For true model portability, use a framework-neutral library like LangGraph or build a thin in-house abstraction. For most production teams, the cost of full portability isn't worth the engineering investment—commit to one SDK and revisit annually.

Which one has better support for long-running, expensive agents?

Anthropic's prompt caching, batch processing, and explicit context-engineering primitives currently give it an edge for high-volume, long-running agents where token economics dominate. OpenAI is closing the gap with its caching improvements and batch API. If your monthly token bill exceeds a few thousand dollars, this is worth benchmarking on your real workload before committing.

Should I use either SDK or just call the APIs directly?

Use the SDK for new projects. Both SDKs handle retry logic, streaming, tool-use loops, and tracing correctly—reimplementing them in raw API calls is a common source of subtle bugs (deadlocks on tool-use loops, lost tool results, broken streaming). Drop to raw API only when you have a very specific reason (custom transport, exotic deployment) and the SDK abstractions are genuinely getting in your way.

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