What is the OpenAI Agents SDK?

The OpenAI Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails for building AI agents with OpenAI models. It's the thinnest framework available — closer to plain Python than LangChain or CrewAI, with auto-schema generation from Python type hints.

What is the difference between OpenAI Agents SDK and Swarm?

Agents SDK is the production evolution of Swarm, which was an experimental framework. Both use the same patterns: Agent objects with instructions and tools, a Runner that handles the agent loop, and handoffs for multi-agent routing. Agents SDK adds guardrails and is officially supported.

Should I use OpenAI Agents SDK or LangChain?

Use the Agents SDK if you're committed to OpenAI models and want minimal abstraction with auto-schema generation. Use LangChain if you need multi-provider support, vector store integrations, or the LangSmith ecosystem. Use plain Python if you want full control and transparency.

What is Smolagents and how does it differ from other agent frameworks?

Smolagents is HuggingFace's minimalist agent library where agents write Python code as actions instead of emitting JSON tool calls. This code-agent approach reduces LLM calls by ~30% by chaining multiple operations in a single step. The core library is about 1,000 lines — deliberately minimal compared to frameworks like LangChain or CrewAI.

Is Smolagents the same as HuggingFace Tiny Agents?

No. Smolagents is HuggingFace's Python agent library for building code-writing and tool-calling agents. Tiny Agents is a separate HuggingFace project focused on lightweight MCP (Model Context Protocol) client agents. They share the HuggingFace ecosystem but solve different problems.

Do I need Smolagents to build a code-writing agent?

No. A code agent is an LLM call that returns Python code, executed via exec() with tools in the namespace. Smolagents wraps this pattern with sandbox support, model integrations, and a clean API. If you want just the pattern, it's about 70 lines of Python. Use Smolagents when you want production sandboxing and HuggingFace model access.

Comparisons / OpenAI Agents SDK vs Smolagents

OpenAI Agents SDK vs Smolagents: Which Agent Framework to Use?

OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. Smolagents is HuggingFace's minimalist agent library. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.

By the numbers

OpenAI Agents SDK

GitHub Stars

20.6k

Forks

3.4k

Language

Python

License

MIT

Created

2025-03-11

Created by

OpenAI

github.com/openai/openai-agents-python →

Smolagents

GitHub Stars

26.4k

Forks

2.4k

Language

Python

License

Apache-2.0

Created

2024-12-05

Created by

Hugging Face

github.com/huggingface/smolagents →

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

Concept	OpenAI Agents SDK	Smolagents
Agent	`Agent(name, instructions, model, tools)`	`CodeAgent` or `ToolCallingAgent` with model and tools list
Tools	Python functions with type hints, auto-converted to schemas	`@tool` decorator or `Tool` class with name, description, and callable
Agent Loop	`Runner.run()` handles the loop internally	Internal loop: think (LLM reasons), act (code/tool call), observe (result)
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	—
Code Actions	—	`CodeAgent` writes Python code as its action, executed in sandbox
Sandbox	—	E2B, Docker, Modal, or Pyodide sandbox for safe code execution
Model Support	—	HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM

OpenAI Agents SDK vs Smolagents, head to head

OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.

Smolagents Smolagents is HuggingFace's minimalist agent library.

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; Smolagents would force you to translate.

Full OpenAI Agents SDK comparison →

Pick Smolagents if

Pick Smolagents if smolagents lives up to its name — it's genuinely minimal and the code-agent approach is a real innovation that reduces LLM calls by ~30%. If you want a lightweight agent library with HuggingFace ecosystem access, it's excellent. For understanding the fundamentals, the plain version is even simpler. Smolagents 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.

Full Smolagents comparison →

What both add

Both OpenAI Agents SDK and Smolagents 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 Smolagents 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 →