OpenAI Agents SDK vs Claude Agent SDK

OpenAI Agents SDK vs Claude Agent SDK: which official agent framework wins for your use case. Composable lightweight primitives vs computer-use environment primitives. Both work with self-hosted fine-tuned models.

Overview

_Updated 2026-05-10 — reflects OpenAI Agents SDK v0.17.0 (May 7) bumping the default RealtimeAgent model to `gpt-realtime-2`, and the May 8 Claude Agent SDK release adding a `defer` permission decision in PreToolUse hooks, a `strict` MCP option, batched/eager `session_store_flush` for live transcript tailing, and an `xhigh` effort level._

By 2026, both OpenAI and Anthropic have released official agent SDKs, and both have made deliberate choices about what their frameworks include. OpenAI's Agents SDK is intentionally minimal — a few core primitives (Agent, Tool, Handoff, Runner) that compose into arbitrarily complex workflows without the conceptual overhead of older frameworks. Claude's Agent SDK takes the opposite approach, including production-grade implementations of common environment primitives (computer-use, sandboxed code execution, file system access, web search) so the agent can take action in computer environments without each team building those layers from scratch.

Both SDKs are model-agnostic at the orchestration layer — neither locks you into the framework author's hosted models. The OpenAI Agents SDK accepts any OpenAI-compatible HTTP endpoint, which means Ollama, vLLM, LM Studio Server, and other self-hosted runtimes all work. The Claude Agent SDK provides a model-provider abstraction that supports the same range of backends. The choice between them is therefore not about API economics — both can run against fine-tuned local models — but about which framework's shape matches your problem.

This comparison helps you pick the right SDK based on your agent's environment requirements, then explains how an Ertas-trained model improves reliability under either framework.

Feature Comparison

Feature	OpenAI Agents SDK	Claude Agent SDK
Released by	OpenAI	Anthropic
Released	2024 (Swarm successor)	2024–2025
Languages	Python + TypeScript (@openai/agents)	Python (TS in development)
Design philosophy	Minimal composable primitives	Environment-rich primitives
Built-in computer-use
Built-in code execution sandbox
Built-in file system primitive
Built-in web search
Multi-agent handoffs	First-class	Manual composition
Tracing built in
Model-agnostic
Works with fine-tuned local models
License	MIT	MIT

Strengths

OpenAI Agents SDK

Lightweight, composable design — minimal conceptual overhead
First-class TypeScript companion (@openai/agents) for JavaScript projects and mobile-app backends
Multi-agent handoffs as a first-class primitive — easier than rolling your own coordination
Built-in tracing system that works regardless of underlying model
Designed deliberately to be model-agnostic — point at any endpoint and the SDK works
Best developer experience among the official agent SDKs as of 2026
Smaller surface area means fewer bugs and easier debugging

Claude Agent SDK

Production-grade environment primitives — computer-use, code execution, file system, web search
'Give the agent a computer' design philosophy — agents reason at higher level, not micro-actions
Sandboxed execution by default — built-in security primitives for risky operations
Particularly strong for computer-use agents (Operator-style workflows)
Built-in safety primitives — action permissions, audit trails, sandboxing
Less work to ship environment-rich agents — battle-tested implementations rather than build-your-own
Strong fit for internal-ops automation, screen-reading assistants, code-execution agents

Which Should You Choose?

Simple multi-tool agent (CRM lookups, API calls, structured output)OpenAI Agents SDK

OpenAI Agents SDK is the lighter-weight choice for agents that don't need a computer environment. Faster to ship, smaller code surface, easier to debug.

Computer-use agent that automates browser or desktop workflowsClaude Agent SDK

Claude Agent SDK's computer-use primitive is production-grade and battle-tested. Building this from scratch on top of the OpenAI Agents SDK is meaningful work.

Code-execution agent that runs and iterates on Python or BashClaude Agent SDK

Claude Agent SDK's sandboxed code execution primitive includes the safety filtering and resource limits that production code-running agents need.

TypeScript backend or React Native mobile app calling the agentOpenAI Agents SDK

OpenAI Agents SDK's TypeScript companion (@openai/agents) is a first-class peer to the Python version. Claude Agent SDK's TypeScript story is less mature in 2026.

Multi-agent system with specialized agents handing work between each otherOpenAI Agents SDK

OpenAI Agents SDK treats handoffs as a first-class primitive. Claude Agent SDK supports multi-agent through composition but doesn't have the same dedicated abstraction.

Internal-ops automation that operates company tools through a sandboxed environmentClaude Agent SDK

Claude Agent SDK's environment primitives (file system, code execution, computer-use) plus its sandboxing make it the natural fit. The framework gives you the operational guarantees you need.

Research-and-summarize agent that browses the web and synthesizes findingsClaude Agent SDK

Claude Agent SDK's web-search primitive plus the sandboxed code execution for synthesis matches this use case directly. OpenAI Agents SDK would require integrating these from scratch.

On-device mobile agent calling local tools (no computer environment)OpenAI Agents SDK

OpenAI Agents SDK's lightweight design and TypeScript companion fit the mobile-backend pattern naturally. Claude Agent SDK's computer-use primitives are unused weight for on-device agents.

Verdict

Pick OpenAI Agents SDK when your agent's actions are mostly tool calls — CRM lookups, API requests, structured outputs, multi-agent handoffs. The framework's minimal primitive set keeps the code surface small and the latency low, and the TypeScript companion makes it the right pick for mobile-app backends. Pick Claude Agent SDK when your agent needs to take action in a computer environment — browser automation, code execution, file system manipulation, web search. The framework's environment primitives are battle-tested and would be significant work to build yourself.

Neither framework requires you to use the developer's hosted model. Both accept self-hosted endpoints, which makes the API-economics question independent of the framework choice. For teams whose primary concern is per-token cost rather than environment-primitive complexity, both frameworks become equally attractive after the model is swapped to a fine-tuned local one.

How Ertas Fits In

Both SDKs work cleanly with fine-tuned models served via Ollama, vLLM, or Ertas Cloud. The OpenAI Agents SDK's `OpenAIChatCompletionsModel` configures with a base URL and a model name; the Claude Agent SDK's OpenAI-compatible provider does the same. Once configured, every agent — whether it's calling tools, executing code, or coordinating handoffs — runs against your fine-tuned model.

The combination is particularly relevant for the agentic cost cliff. Agents make many model calls per task (5–30, depending on workflow), so the per-token bill scales with the loop, not with user-facing turns. An app at 1K MAU paying $120/month on baseline cloud APIs is typically paying $3,000+/month at 40K MAU. Swapping the underlying model to an Ertas-trained local one — without changing the SDK code — flips the curve from variable to fixed. For mobile app builders shipping AI features, this is the difference between staying on the cliff and getting off it. The Ertas Deployment CLI bridges from the GGUF export to a working iOS, Android, Flutter, or React Native integration in minutes.