For tool-building teams, the Claude Agent SDK is the fit when you want an agent harness built around the same tools, agent loop and context management that power Claude Code; the OpenAI Agents SDK is the fit when you want provider-agnostic model support and tool categories spanning hosted execution, local/runtime execution, Python functions and agents used as tools. The clearest documented contrasts are Claude’s permission controls and supervised local process versus OpenAI’s handoffs, input and output guardrails, session memory and default tracing. This comparison is based on each project’s documentation and repository; we have not run or benchmarked either SDK.
Set up the Claude Agent SDK
The setup starts with built-in operations for reading, writing and editing files, running commands and searching the web. Those capabilities are part of the SDK rather than tools a team must assemble separately (Claude Agent SDK overview).
Permission modes and rules define what can run automatically. The canUseTool callback handles tool decisions that require an answer at runtime, giving the host application a place to apply context-specific controls rather than treating every call the same way (Configure permissions).
Subagents are separate agent instances that the main agent can spawn. Their documented uses include isolating context, running analyses in parallel and applying specialized instructions without adding those instructions to the main agent’s prompt (Subagents in the SDK). MCP provides a separate integration path: agents can query databases, connect to APIs and reach other services without a custom tool implementation for each connection (Connect external tools with MCP).
A Claude Agent SDK session accumulates the prompt, tool calls, tool results and responses. The SDK writes this conversation history to disk automatically so it can be returned to later (Work with sessions).
Hosting introduces an important deployment boundary. The SDK spawns and supervises a claude CLI subprocess that owns a shell, a working directory and session files. Anthropic describes each running agent as a long-lived process tied to local state, not a stateless API wrapper (Hosting the Agent SDK).
Use of the SDK is governed by Anthropic’s Commercial Terms of Service. The documentation does not state an installation command, package name or startup flag here, so check the current Claude Agent SDK setup instructions rather than inferring syntax.
Set up the OpenAI Agents SDK
The OpenAI Agents SDK README describes a lightweight framework for multi-agent workflows. It is provider-agnostic, supports the OpenAI Responses and Chat Completions APIs, and names support for 100+ other LLMs. That provider scope does not establish that every tool works with every model; the README does not provide a provider compatibility matrix.
Its tools documentation states that the SDK supports five categories, including hosted OpenAI tools that execute on OpenAI servers, runtime tools, Python FunctionTool instances and agents exposed as tools. ComputerTool and ApplyPatchTool always run in your environment. ShellTool can run locally or in a hosted container, while an agent can become a callable tool without a full handoff.
Multiple MCP transports let teams reuse existing MCP servers or build servers that expose filesystem, HTTP and connector-backed tools (OpenAI Agents SDK MCP). Handoffs delegate work to agents with distinct specialties, guardrails check user input and agent output, and built-in session memory maintains conversation history across runs (handoffs, guardrails and sessions).
Tracing is enabled by default. The documented global disable setting is:
OPENAI_AGENTS_DISABLE_TRACING=1
Ensure the runtime environment receives that variable before treating tracing as disabled (OpenAI Agents SDK tracing). The openai-agents-python repository is published under the MIT License. The documentation does not state an installation command or package name here, so check the current OpenAI Agents SDK setup instructions.
Check it worked
For the Claude side, run a representative file or command task from a known working directory, then inspect the result where a user would consume it. Return to the session and confirm that the prompt, tool calls, results and responses are present. Separately exercise an automatically allowed action and a case routed through canUseTool (sessions and permissions).
For the OpenAI side, exercise the exact tool category you intend to ship. Inspect the result of a FunctionTool, confirm whether ShellTool is local or hosted, and follow a handoff until the specialized agent returns (tools and handoffs). Put representative input and output through the guardrail path, resume the session in a later run, and verify the tracing state you intended (guardrails, sessions and tracing).
In our setup, done means checked: we read the result back from where a user would see it rather than trusting an agent summary or successful process exit. We also require every new verification gate to have a case that must fail. Otherwise, a copied check can report success without testing anything relevant. These are coordination rules, not benchmark results for either SDK.
Where it breaks
Claude Agent SDK
The documented hosting model breaks a stateless-wrapper assumption. A long-lived claude process owns a shell, working directory and disk-backed session files, so a deployment that expects every request to start clean must account for that state (Hosting the Agent SDK).
Permissions also have separate automatic and runtime paths. Teams need to decide which actions belong in modes and rules and which require canUseTool. The documentation names the callback but does not state its signature here; use the current interface documentation instead of inventing one (Configure permissions).
OpenAI Agents SDK
The main boundary is execution location. Hosted tools run on OpenAI servers, while ComputerTool, ApplyPatchTool and a local ShellTool run in your environment. A hosted ShellTool uses a different location again. Treating all of these as interchangeable generic tools can leave deployment and permission assumptions unresolved (OpenAI Agents SDK tools).
The guardrails documentation says checks and validations run on user input and agent output, but it does not say whether a failure blocks execution, triggers a retry or rewrites the result. The session documentation does not specify the storage backend, and the README does not provide provider-by-provider tool compatibility. Check current documentation before depending on those details (Guardrails, Sessions and the OpenAI Agents SDK README).
Shared workspaces
Across frameworks, our operating rule is that uncommitted changes in a shared working tree belong to every session. We use a single writer per shared file, separate scratch and output locations, and disjoint work identifiers for parallel jobs. We also delegate noisy, independent searches rather than checks that are easier to run directly. These are our coordination controls, not claims about isolation built into either SDK.
Tool-building comparison
| Tool-building concern | Claude Agent SDK | OpenAI Agents SDK |
|---|---|---|
| Language and model scope | Programmable in Python and TypeScript, using the tools, agent loop and context management that power Claude Code (overview). | Provider-agnostic; supports OpenAI Responses and Chat Completions APIs plus 100+ other LLMs (README). |
| Tool surface | Built-ins cover file reading, writing and editing, command execution and web search (overview). | Five categories include hosted tools, runtime tools, Python FunctionTool instances and agents used as tools (tools). |
| External systems | MCP can connect databases, APIs and services without custom tool implementations for each connection (MCP). | Multiple MCP transports can reuse servers or expose filesystem, HTTP and connector-backed tools (MCP). |
| Specialist agents | The main agent spawns separate subagents for isolated context, parallel analyses and specialized instructions (subagents). | Handoffs delegate tasks to specialized agents; an agent can also become a callable tool without a full handoff (handoffs, tools). |
| Controls | Permission modes, rules and canUseTool handle automatic and runtime decisions (permissions). |
Guardrails perform checks and validations on user input and agent output (guardrails). |
| State | Sessions contain the prompt, tool calls, results and responses, and are written to disk automatically (sessions). | Built-in session memory maintains conversation history across runs (sessions). |
| Runtime and visibility | Each agent is a supervised, long-lived claude process tied to a shell, working directory and session files; the overview does not state tracing behavior (hosting). |
Some tools execute on OpenAI servers and others in your environment; tracing is enabled by default (tools, tracing). |
| Legal and repository boundary | Use is governed by Anthropic’s Commercial Terms of Service (overview). | The Python repository is published under the MIT License (repository license). |
When each one fits
The Claude side fits teams that organize agent work around files, commands and web access, need permission modes and runtime decisions, and want subagents, MCP and disk-backed sessions. It also fits deployments that can treat the long-lived claude process, its shell, working directory and local state as part of the runtime rather than an implementation detail (Claude Agent SDK documentation).
The OpenAI side fits teams that require provider-agnostic model support, a clear choice between hosted and environment-executed tools, or Python functions exposed directly to agents. Handoffs, guardrails, built-in sessions and default tracing are relevant when explicit coordination and observability features belong in the framework layer (OpenAI Agents SDK tools and handoffs). These are fit conditions, not rankings.
Where each gets in the way
Claude gets in the way when the deployment model assumes stateless request handling or when a team wants one generic tool interface despite substantial differences between web search, file operations, shell commands, permissions and subagents. Its local process and session state must be managed rather than hidden behind an API-shaped abstraction (Hosting the Agent SDK).
OpenAI gets in the way when a generic tool layer hides execution location or when provider choice is more important than provider compatibility evidence. The framework’s hosted and runtime categories require an explicit deployment boundary, and default tracing requires a deliberate environment decision (OpenAI Agents SDK tools and tracing).
The legal comparison is narrow: Anthropic’s documentation points to Commercial Terms for Claude Agent SDK use, while OpenAI’s cited license covers the Python repository. Those are different legal objects and should not be expanded into an unsupported comparison of service terms. Neither project’s documentation establishes which SDK is faster, cheaper or more successful in a particular team, and we have no benchmark data for either.