OpenAI Agents SDK vs Claude Agent SDK
Both frameworks remove the tool loop you would otherwise write yourself. They disagree about what an agent is for.
The short answer
Pick the Claude Agent SDK when one agent needs deep, permissioned access to files, commands and MCP tools; pick the OpenAI Agents SDK when the job is routing a conversation between a few defined specialists.
The Claude Agent SDK gives you the same tools, agent loop and context management that run Claude Code, as a library for Python and TypeScript. It treats an agent as something that reads files, runs commands and edits code across many turns, with a permission layer deciding what happens unattended.
The OpenAI Agents SDK treats an agent as a specialist with its own instructions and tools. It builds the primitives for handing a conversation from one specialist to another. Handoffs, guardrails and sessions are the core pieces, and the framework runs the loop between them so you define agents rather than orchestration code.
Side by side
Both are Python and TypeScript libraries. The difference is what each one assumes an agent is doing.
| Dimension | Claude Agent SDK | OpenAI Agents SDK |
|---|---|---|
| Core model | One agent, given tools and a workspace | Several agents, wired together by handoffs |
| Built-in tools | Read, write, edit files, run commands, search the web | Platform tools plus tool wrappers and MCP servers, hosted or local |
| Safety layer | Permissions: which tools run automatically, which need approval | Guardrails: input and output checks that can pause a run |
| Multi-step control | Hooks at points in the agent lifecycle, plus subagents | Handoffs that pass a conversation to a different specialist |
| State | Sessions you can resume or fork | Sessions that persist history across runs |
| External tools | Model Context Protocol | MCP: hosted via the Responses API, remote Streamable HTTP, or local stdio |
| Languages | Python and TypeScript as a library; other languages drive the CLI as a subprocess | Python and TypeScript, maintained as separate SDKs |
| Voice | Not a stated focus of the SDK | Realtime agents for speech-to-speech, in both languages |
| Natural fit | A single agent that needs to act on a real filesystem or shell | A conversation that should route between a small set of specialists |
- Task arrivesA prompt or a user turn.
- Claude: one agent plansSame agent picks the next tool call.
- Claude: permission gateAuto-run, or a human approves.
- OpenAI: agent decidesHandle it, or hand off.
- OpenAI: guardrail gateInput or output check can pause the run.
- ResultOne trace, or a chain of handoffs to read.
Claude narrows control to one agent and a permission gate. OpenAI spreads it across agents and a handoff chain, which is either flexibility or a longer trace, depending on the job.
What an agent definition looks like in each
The two SDKs shape code differently because they shape agents differently. Claude's query() call hands one agent a tool list and a permission mode. OpenAI's Agent() constructor defines a specialist, then a handoffs list wires it to the others.
A minimal Claude Agent SDK call, in Python:
async for message in query(prompt="Fix the failing test", options=ClaudeAgentOptions(allowed_tools=["Read", "Edit", "Bash"], permission_mode="acceptEdits")): print(message)
One call. The tools and the permission mode sit on the same options object, because one agent is doing the whole job.
A minimal OpenAI Agents SDK setup, in Python:
billing_agent = Agent(name="Billing", instructions="Handle invoice questions"); triage_agent = Agent(name="Triage", instructions="Route the user to the right specialist", handoffs=[billing_agent]); result = Runner.run_sync(triage_agent, "Why was I charged twice?")
Two agent objects and a handoff between them, run through a shared Runner. Adding a third specialist means defining another agent and adding it to the handoff list, not touching the first two.
Claude Agent SDK
Where it wins
- File and command access is built in, so an agent that has to actually change something in a codebase or a filesystem does not need custom tooling for that.
- Permissions are first-class. You decide which actions run unattended and which stop for a person, which is what makes an agent with real system access defensible.
- Hooks put logging and policy checks inside the agent lifecycle, so an audit trail exists without wrapping the loop yourself.
- Sessions resume or fork, which turns a long task into something recoverable rather than something you restart from zero.
Where it hurts
- It assumes one agent doing one job well. Coordinating several distinct specialists is not what the primitives are built around.
- Library support stops at Python and TypeScript. Any other language means driving the CLI as a subprocess, which is workable but heavier to integrate.
- An agent with file and command access is a security decision, not just a feature flag. The SDK gives you the controls; getting the permission set wrong is still on you.
- No stated voice or realtime layer, so a spoken interface is built on top rather than provided.
OpenAI Agents SDK
Where it wins
- Handoffs give you a clean way to split a conversation across specialists, each with its own instructions and tools, without writing the routing logic by hand.
- Guardrails run before and after a tool call, so content filtering or a business rule check has a defined place to live.
- Sessions persist conversation history across runs with little setup on your side.
- Realtime agents give voice a first-class path, which matters if the product is a spoken interface rather than a text one.
Where it hurts
- The design leans toward routing conversations, not driving deep access to a filesystem or shell. That kind of agent is possible but is not what the framework is shaped around.
- More agents means more handoffs, and each one is a place where context can narrow before the next specialist sees it.
- A run that crosses several agents produces a longer trace to read when something goes wrong, compared with one agent's single trace.
- Two separately maintained SDKs, Python and TypeScript, so feature parity between them is worth checking before you commit to one.
How to choose
Writing down what a correct outcome looks like, and choosing the architecture around it, is the part of this we do with clients directly through AI agent development, before either SDK gets picked.
- 01Pick the Claude Agent SDK if the job is one agent acting on real files, commands or a codebase, and you need a permission layer to keep that safe.
- 02Pick the OpenAI Agents SDK if the job is routing a conversation between a small set of specialists, each doing one narrow thing well.
- 03Lean toward OpenAI's SDK if the product includes a spoken interface. Realtime agents are a stated part of that SDK.
- 04Run both if you have to. Nothing stops a team running a Claude Agent SDK process for file-level work behind an OpenAI Agents SDK front end that routes the conversation, though it is two systems to operate rather than one.
- 05Wait on either one if you have not written down what a correct outcome looks like. Neither framework fixes an unmeasured goal. Both only make the loop that produces one faster to build.
Questions teams ask before picking one
01Can we use OpenAI Agents SDK with a Claude model, or the reverse?+
It is asymmetric. The OpenAI Agents SDK can run non-OpenAI models, including Claude, through its LiteLLM integration (litellm/... model names), which OpenAI labels best-effort and beta, and some hosted platform tools stay OpenAI-only. The Claude Agent SDK runs Claude only. So you can point OpenAI's SDK at Claude for the model; the reverse is not available, and each SDK's primitives, such as handoffs or the permission system, still assume its own runtime.
02Which one is easier to debug in production?+
A single Claude agent produces one trace, which is usually faster to read when an answer is wrong. A multi-agent OpenAI setup gives you a trace per agent, plus the handoffs between them. That is more to read, but it tells you which specialist owned the failing step. Neither is strictly easier; they trade off differently.
03Do both SDKs support the Model Context Protocol?+
Yes. The Claude Agent SDK connects external tools and data sources through MCP directly. OpenAI's SDK supports MCP too: hosted servers through the Responses API, remote Streamable HTTP servers and local stdio servers, alongside its own platform tools. MCP is not a reason to prefer one over the other.
04What is the actual switching cost between them?+
Higher than it looks from the docs. Claude's permission model and hooks, and OpenAI's handoffs and guardrails, are not interchangeable concepts. An agent design built around one does not port cleanly to the other. Decide on the architecture first, one agent with deep access or several routed specialists. That decision drives the choice more than any feature list.
Related
- Claude Agent SDK review →Where we use it in production and where the Client SDK is the better call.
- OpenAI API review →Our production notes on building against OpenAI directly.
- hire Claude Agent SDK developers →Engineers who have shipped permission-gated agents on this stack.
- AI agent frameworks, ranked →Where these two sit against the rest of the field.
