Two major AI labs have released first-party agent SDKs for building production multi-agent applications. Anthropic’s Agents SDK and OpenAI’s Agents SDK share broad goals but make different design choices, and one of those choices has significant production implications: model lock-in.
The OpenAI Agents SDK is built for OpenAI’s models. The Anthropic Agents SDK is built for Claude. The key question: If you are evaluating which SDK to build on, understanding what you are committing to beyond the API surface is the most important part of the decision.
First-party SDKs from AI labs are infrastructure choices with multi-year implications. The model you build your agent architecture around shapes what you can access, what you pay, and what your options are when the competitive landscape shifts.
What the OpenAI Agents SDK is and what Claude’s Agents SDK offers
The OpenAI Agents SDK
The OpenAI Agents SDK (released in early 2026) provides primitives for building multi-agent applications on top of OpenAI’s models: Agents (with instructions and tools), Handoffs (structured agent-to-agent delegation), the Responses API (a stateful conversation interface), and Guardrails (input/output validation). It is designed to make multi-agent orchestration with OpenAI’s models production-ready.
The lock-in: It works exclusively with OpenAI’s models. There is no configuration path to run OpenAI Agents SDK applications against Claude, Gemini, or other providers.
The Claude API and Anthropic Agents SDK
The Anthropic Agents SDK provides structured patterns for building multi-agent applications with Claude: subagent creation, task delegation, structured handoffs, tool use through native MCP support, and context passing between agents. It is maintained by Anthropic and tracks Claude’s latest capabilities. The subagents guide covers the delegation patterns in detail, and parallel agents with Claude Code covers concurrent execution approaches.
Like the OpenAI Agents SDK, it is model-specific: it is designed for Claude. It is not a multi-provider abstraction framework.
Feature comparison
| Dimension | Claude API + Agents SDK | OpenAI Agents SDK |
|---|---|---|
| Abstraction level | Medium: structured primitives | Medium: agents, handoffs, guardrails |
| Language support | Python, TypeScript | Python, TypeScript |
| Multi-agent support | Subagent delegation, handoffs | Agents + Handoffs pattern |
| Stateful conversation | Manual context management | Responses API (stateful threads) |
| Input/output validation | Manual or Pydantic | Built-in Guardrails system |
| Tool use / MCP | Native full MCP support | Function tools, Responses API tools |
| Model portability | Claude only | OpenAI models only |
| Human-in-the-loop | Manual implementation | Via Responses API patterns |
| Tracing/observability | Via third-party tools | Built-in tracing in SDK |
| Production-ready | Yes, Anthropic-maintained | Yes, OpenAI-maintained |
| Best for | Claude-based production agents | OpenAI-based production agents |
What the OpenAI Agents SDK adds over a raw OpenAI API integration
Responses API for stateful conversations
OpenAI’s Responses API maintains conversation state server-side, reducing the amount of context you need to manage in your application code. Instead of passing full conversation history with every request, the Responses API maintains the thread and you reference it by ID. For long-running agent conversations, this reduces payload size and simplifies client code.
The Claude API uses a different model: you manage conversation history in your application and pass it with each request. This gives you full control over what is included in context but requires your application to manage state.
Structured Handoffs between agents
The OpenAI Agents SDK formalises agent-to-agent delegation through its Handoffs system. When one agent determines that another agent should handle a task, it produces a structured Handoff object that routes control to the appropriate agent. This pattern is explicit and auditable.
The Anthropic Agents SDK has equivalent subagent delegation patterns. The specific implementation differs, but the conceptual model is similar: one agent orchestrates, others specialise.
Built-in Guardrails
The OpenAI Agents SDK includes a Guardrails system for validating agent inputs and outputs. You define guardrails that run before and after agent calls, catching cases where inputs or outputs do not meet your application’s requirements. This is integrated into the SDK’s execution model rather than being a separate layer.
The Claude API does not have a built-in guardrails system. Input/output validation requires external tooling or manual implementation. Claude’s constitution and safety training provide model-level safety, but application-level validation is your responsibility.
Integrated tracing
The OpenAI Agents SDK includes built-in tracing that records agent execution steps, tool calls, and handoffs. This is available without third-party tooling and integrates with OpenAI’s platform observability features.
Why teams choose Claude’s approach
Claude’s instruction-following quality
Claude is consistently rated highly on complex instruction following, nuanced reasoning, and long-context tasks. For agent applications where the quality of the model’s judgment matters (complex document analysis, multi-step reasoning, subtle decision-making), Claude’s model capabilities are a primary reason teams choose it over the OpenAI ecosystem.
The bottom line: The SDK is infrastructure. The model is what produces outcomes. Teams that prefer Claude’s outputs should build on Claude’s SDK, not try to adapt it to a different SDK.
Native MCP support
Claude’s native Model Context Protocol support gives you direct access to the rapidly growing MCP tool ecosystem without adapter layers. The OpenAI Agents SDK uses function-calling-based tools, which work well but require manual integration with MCP servers. For applications that want broad tool access through the MCP standard, Claude’s approach is more direct. The MCP setup guide covers how to configure and extend that ecosystem for production use.
Pricing and cost structure
Claude and OpenAI models have different pricing, and that pricing changes over time as both labs compete. For high-volume production agent applications, cost differences between models at the same capability tier can be significant. Teams should model their specific usage patterns against current pricing rather than assuming either approach is uniformly cheaper.
Avoiding OpenAI’s terms and ecosystem
For teams avoiding OpenAI: Some teams have policy, procurement, or governance reasons to avoid OpenAI’s platform. Claude provides equivalent or superior capabilities without those constraints. The Anthropic Agents SDK gives those teams a first-party agent SDK that does not require engaging with OpenAI’s ecosystem at all.
The hybrid consideration: model-agnostic frameworks
Teams that want to avoid committing to either first-party SDK have a third option: model-agnostic frameworks like LangChain, LangGraph, or LiteLLM that abstract over multiple providers. These add framework overhead but preserve the ability to switch models by changing a configuration value.
The reality: In practice, model portability through frameworks is less reliable than it sounds: prompting strategies, tool use patterns, and system prompt design are often model-specific. The abstraction layer does not automatically transfer well-tuned Claude prompts to GPT-4o or vice versa.
The model-agnostic framework approach makes sense during evaluation, when you are genuinely comparing models. Once you have selected a model for production, committing to that model’s first-party SDK typically produces better results than maintaining the portability abstraction.
Production considerations
SDK version stability
Both first-party SDKs are relatively new and will evolve. The OpenAI Agents SDK launched in 2026 and has been updating rapidly. The Anthropic Agents SDK is similarly active. For production applications, pin to tested versions and have a clear upgrade testing plan.
Observability
The OpenAI Agents SDK’s built-in tracing is a genuine advantage for teams that value integrated observability without third-party tooling. Claude-based applications need to add observability separately (through LangSmith, Arize, or custom instrumentation). This is not a blocker, but it is additional work.
When to choose the OpenAI Agents SDK
The OpenAI Agents SDK is the right choice when your team has made a strategic decision to standardise on OpenAI’s models, you value the Responses API’s stateful conversation management, and you want built-in Guardrails and tracing without third-party tooling.
When to choose Claude’s Agents SDK
Claude’s Agents SDK is the right choice when your team prefers Claude’s model capabilities, you need native MCP support for broad tool access, you are in a context where avoiding OpenAI’s platform is a requirement, or you want a model with strong long-context and instruction-following performance for complex agent tasks. Note: For a deeper look at the patterns the SDK enables, see the guides on agentic workflows and parallel agents.
FAQ
Can I use the OpenAI Agents SDK with Claude?
No. The OpenAI Agents SDK is designed for OpenAI’s models and does not support Claude directly. You would need a compatibility layer (like LiteLLM in proxy mode) to route OpenAI SDK calls to Claude, and that approach introduces latency, reliability risk, and reduced access to Claude-specific features.
Can I use Anthropic’s Agents SDK with OpenAI models?
No, for the same reason in reverse. Both first-party SDKs are designed for their respective models. If you need multi-model support, use a model-agnostic framework.
Is the OpenAI Agents SDK’s stateful Responses API a significant advantage?
For applications with very long-running conversations or applications where reducing payload size matters, yes. For most agent applications, managing conversation history in application code is straightforward and the Responses API’s advantages are marginal. The tradeoff is control: with the Responses API, OpenAI manages your conversation state server-side.
How do tool use patterns compare between the two SDKs?
Both SDKs support tool/function calling where agents declare tools, the model decides to call them, and the application handles execution. The OpenAI Agents SDK has a tighter integration with OpenAI’s function calling format. Claude’s Agents SDK has native MCP support, which provides access to a broader standardised tool ecosystem.
What happens to my application if Anthropic or OpenAI changes pricing significantly?
This is the core risk of model lock-in. Both first-party SDKs commit you to a specific provider. Migrating an application built on the Anthropic Agents SDK to the OpenAI Agents SDK (or vice versa) is non-trivial: agent definitions, prompts, tool integrations, and orchestration patterns all need to be reworked. The cost consideration: Price sensitivity is a reason some teams prefer model-agnostic frameworks despite their overhead.
Choosing between the two ecosystems
If you have decided to use Claude as your model, the Anthropic Agents SDK is the right foundation. If you have decided to use OpenAI’s models, the OpenAI Agents SDK is the right foundation. The choice of SDK follows from the model choice, not the other way around.
The deeper question is which model your application should be built on. That decision turns on capability requirements, pricing at your expected volume, ecosystem preferences, and risk tolerance for vendor lock-in.
Path one: evaluate both models for your application. Run your core agent task against Claude (Sonnet or Opus) and GPT-4o with equivalent prompts and tools. Measure output quality on your specific task. Build on the SDK that corresponds to the better model for your use case. If you want to go deeper on Claude’s agentic capabilities, our Claude Code course covers the patterns that production agent teams rely on.
Path two: work with Phos AI Labs. If you are making a strategic decision about which AI ecosystem to build on and want a grounded recommendation based on your specific application, scale, and team, Phos AI Labs can run the evaluation and help you build on the right foundation. Thirty minutes, no deck. Start here.