Claude Agent SDK vs OpenAI Agents SDK: A Developer's Comparison

Anthropic's Claude Agent SDK and OpenAI's Agents SDK both shipped production releases in 2025 and 2026, but they solve the same problem from opposite directions. Claude Agent SDK treats the agent as a stateful runtime with deep system access. OpenAI Agents SDK treats the agent as a lightweight orchestration primitive that hands off work between specialists.

The Claude Agent SDK grew out of the Claude Code project. Its core idea is to give an AI model access to a computer: file system operations, bash execution, code editing, and web search all come built in. You configure an agent, start a session, and the SDK manages a persistent environment where files written to disk, environment variables set, and databases created all survive across turns.

The OpenAI Agents SDK evolved from Swarm, an experimental multi-agent framework OpenAI released in late 2024. It landed on three primitives: Agents (LLMs with instructions and tools), Handoffs (agent-to-agent delegation), and Guardrails (input/output validation). The April 2026 update added a use system with native sandbox execution, bringing it closer to production parity for long-running tasks.

The practical difference shows up immediately in how you think about building. With Claude Agent SDK, you build one highly capable agent that can do many things. With OpenAI Agents SDK, you build several focused agents that pass work between each other.

This table covers the major decision points developers face when choosing between the two frameworks.

Neither SDK charges separately for the framework itself. You pay for the underlying model tokens.

The biggest architectural split between these SDKs is how they handle tools.

Claude Agent SDK ships with eight built-in tools that give the agent direct access to the host system. An agent can read and edit files, run bash commands, search codebases with glob and grep, and fetch web content without any extra configuration. Custom tools are defined as Python functions wrapped in an @tool decorator, then bundled into an in-process MCP server:

from claude_agent_sdk import create_sdk_mcp_server, tool
import json

@tool(
    "lookup_price",
    "Get the current price for a product SKU.",
    {"sku": str},
)
async def lookup_price(args):
    price = get_price_from_db(args["sku"])
    return {"content": [{"type": "text", "text": json.dumps(price)}]}

pricing_server = create_sdk_mcp_server(
    name="pricing", tools=[lookup_price]
)

Because Anthropic created the Model Context Protocol, MCP integration is first-class. Any MCP-compatible server (Slack, GitHub, Google Drive, databases) plugs in without custom glue code.

OpenAI Agents SDK takes a lighter approach. Tools use the @function_tool decorator with Python type hints for automatic schema generation:

from agents import Agent, Runner, function_tool

@function_tool
def lookup_price(sku: str) -> dict:
    """Get the current price for a product SKU."""
    return get_price_from_db(sku)

agent = Agent(
    name="Pricing Agent",
    instructions="Help users check product prices.",
    tools=[lookup_price],
)

OpenAI's approach requires fewer lines of code per tool. The trade-off is less explicit control over the schema the model sees. OpenAI also offers hosted tools (web search, file search, code interpreter) that run on their infrastructure, eliminating the need to manage execution environments for common operations.

Both SDKs support MCP, but the integration depth differs. Claude Agent SDK handles MCP connection lifecycle automatically. OpenAI added MCP support in v0.7, and while functional, it requires more manual wiring.

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This is where the two SDKs diverge most sharply.

Claude Agent SDK uses subagents. The orchestrator spawns child agents for specific subtasks, receives their results, and decides what to do next. The parent agent never loses control. Think of it as a manager who delegates work but reviews every deliverable:

from claude_agent_sdk import AgentDefinition, ClaudeAgentOptions

researcher = AgentDefinition(
    description="Researches competitor pricing data.",
    prompt="Find current pricing for the specified competitors...",
    tools=["mcp__pricing__lookup_price"],
)

writer = AgentDefinition(
    description="Writes pricing comparison reports.",
    prompt="Using the research data, write a comparison...",
    tools=[],
)

orchestrator = ClaudeAgentOptions(
    model="claude-sonnet-4-6",
    system_prompt="Route pricing tasks to the right subagent.",
    agents={"researcher": researcher, "writer": writer},
    allowed_tools=["Agent"],
)

This delegation model works well for deep, sequential workflows where one agent's output feeds directly into another's input. The orchestrator maintains full context and can make routing decisions based on intermediate results.

OpenAI Agents SDK uses handoffs. When Agent A encounters a task better suited for Agent B, it transfers the entire conversation. Agent B takes over completely. This is closer to a call center routing model:

from agents import Agent, Runner

billing_agent = Agent(
    name="Billing",
    instructions="Handle billing questions and refund requests.",
    tools=[check_balance, process_refund],
)

technical_agent = Agent(
    name="Technical",
    instructions="Troubleshoot technical issues.",
    tools=[run_diagnostics, check_logs],
)

triage = Agent(
    name="Triage",
    instructions="Route users to the right specialist.",
    handoffs=[billing_agent, technical_agent],
)

result = Runner.run_sync(triage, "I was double-charged last week")

Handoffs are intuitive for customer support pipelines, content workflows, and any system where distinct roles handle distinct phases. The limitation, noted in the 2026 AI Agent Framework Showdown, is that handoffs are primarily sequential. The framework does not natively support parallel agent execution.

Claude's subagent model can run agents in parallel and nest them arbitrarily deep. OpenAI's handoff model is simpler to reason about but less flexible for complex orchestration.

Both SDKs have matured for production use, but they emphasize different operational concerns.

Sessions and State

Claude Agent SDK provides two session modes. In-memory sessions reuse the same client object for multi-turn conversations. Disk-backed sessions persist state to the filesystem and can resume across process restarts using a session ID:

### Resume a previous session in a new process
from dataclasses import replace
resume_opts = replace(agent_options, resume=session_id)
async with ClaudeSDKClient(options=resume_opts) as client:
    await client.query("Continue where we left off")

OpenAI Agents SDK offers pluggable session backends, including SQLAlchemy, SQLite, Redis, and MongoDB. This gives you more flexibility in choosing where session state lives, especially in distributed deployments where filesystem persistence is not practical.

Tracing and Observability

OpenAI's built-in tracing records every step of agent execution as spans within traces, viewable through the OpenAI dashboard. The April 2026 use update added detailed tracing for tool approvals, resume events, and handoff decisions. You can also export traces to Logfire, AgentOps, or any OpenTelemetry-compatible backend.

Claude Agent SDK streams events (SystemMessage, AssistantMessage, UserMessage, ResultMessage) that form a complete audit trail. It works alongside OpenTelemetry for export to Grafana, Datadog, Honeycomb, or Langfuse. Per-turn usage metrics (input tokens, output tokens, cache hits, cost in USD) are available directly on the ResultMessage.

Guardrails and Validation

OpenAI provides declarative guardrails through decorators. You write an @input_guardrail function that validates user input before it reaches the agent, and an @output_guardrail that validates the agent's response before returning it. If a guardrail triggers, the SDK raises a GuardrailTripwireTriggered exception.

Claude Agent SDK handles validation through hooks or plain functions. Hooks fire at lifecycle points (like UserPromptSubmit) and can block or modify requests. Plain function guardrails are simpler: you write a regular Python function that returns a pass/fail decision, and call it before or after the agent runs. This is less magical but keeps guardrail logic visible in your application code.

Cost Controls

Claude Agent SDK includes automatic prompt caching that can reduce input token costs by up to 90% on subsequent turns with the same system prompt and tool definitions. It also supports max_budget_usd to cap spending per session. OpenAI Agents SDK does not expose prompt caching at the SDK level, so cost management relies on choosing the right model tier and monitoring token usage through the dashboard.

After comparing both frameworks across architecture, tooling, multi-agent patterns, and production features, the decision comes down to your use case and existing infrastructure.

Choose Claude Agent SDK when:

• Your agents need deep system access (file editing, code execution, bash commands)
• You are building developer tools, coding assistants, or data analysis pipelines
• Single-agent depth matters more than multi-agent breadth
• You want managed hosting without operational overhead (Claude Managed Agents)
• MCP integration is central to your architecture
• You are already using Claude models and want the tightest integration

Choose OpenAI Agents SDK when:

• Your workflow maps naturally to sequential handoffs (support tiers, content pipelines, approval chains)
• You need voice agent capabilities via Realtime Agents
• Model flexibility matters (swap between OpenAI, Anthropic via LiteLLM, or local models)
• You prefer an open-source framework (MIT license)
• Your team needs pluggable session storage (Redis, MongoDB) for distributed deployments
• You want built-in sandbox execution with provider choice (E2B, Modal, Vercel)

Consider a third option when:

Neither SDK solves the file persistence problem on its own. When agents generate reports, analyze datasets, or build deliverables, those outputs need to go somewhere accessible to both agents and humans. Local disk storage disappears when containers restart. S3 requires custom integration code.

Fast.io provides workspaces where agents and humans collaborate on the same files. Agents connect through the MCP server or REST API to read, write, and organize files. Intelligence Mode auto-indexes uploaded content for semantic search and RAG, so agent outputs are immediately searchable and queryable. When the agent's work is done, ownership transfer hands the workspace to a human, keeping the agent as admin.

The free agent plan includes 50GB storage, 5,000 credits per month, and 5 workspaces with no credit card required. Both Claude Agent SDK and OpenAI Agents SDK can connect to Fast.io through MCP, making it a framework-agnostic persistence layer for agent-generated content.