Comparisons / OpenAI Agents SDK vs Pydantic AI
OpenAI Agents SDK vs Pydantic AI: Which Agent Framework to Use?
OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. Pydantic AI is a type-safe agent framework built by the Pydantic team. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
Pydantic AI
16.1k
1.9k
Python
MIT
2024-06-21
Pydantic (Samuel Colvin)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | OpenAI Agents SDK | Pydantic AI |
|---|---|---|
| Agent | `Agent(name, instructions, model, tools)` | `Agent()` class with typed `result_type`, system prompt, and `model` parameter |
| Tools | Python functions with type hints, auto-converted to schemas | `@agent.tool` decorator with typed parameters and Pydantic validation |
| Agent Loop | `Runner.run()` handles the loop internally | `agent.run()` handles the tool-call loop internally with typed dispatch |
| Handoffs | `Handoff` between `Agent` objects for multi-agent routing | — |
| Guardrails | `InputGuardrail` and `OutputGuardrail` with tripwire pattern | — |
| Context | Typed context object passed through the agent lifecycle | — |
| Structured Output | — | `result_type=MyModel` enforces Pydantic model on final LLM response |
| Model Switching | — | Swap `model='openai:gpt-4o'` to `model='anthropic:claude-sonnet'` in one line |
| Dependencies | — | `RunContext[DepsType]` injects typed dependencies into tools at runtime |
OpenAI Agents SDK vs Pydantic AI, head to head
OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.
Pydantic AI Pydantic AI is a type-safe agent framework built by the Pydantic team.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick OpenAI Agents SDK if
Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. OpenAI Agents SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Pydantic AI would force you to translate.
Pick Pydantic AI if
Pick Pydantic AI if pydantic AI adds genuine value if you want compile-time type checking across your agent's tools, outputs, and dependencies. If you already use Pydantic in your stack, it fits naturally. But the core agent logic — loop, dispatch, validate — is still ~60 lines of Python you can own entirely. Pydantic AI is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; OpenAI Agents SDK would force you to translate.
What both add
Both OpenAI Agents SDK and Pydantic AI pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both OpenAI Agents SDK and Pydantic AI implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →