How OpenAI Agents SDK Stacks Up Against SmolAgents: A Deep Dive

Overview of OpenAI Agents SDK

OpenAI Agents SDK is a lightweight framework for building powerful, extensible multi‑agent systems, enabling smarter AI application workflows.

Design Principles

Rich functionality, simple concepts – provides strong capabilities while keeping the conceptual model easy to learn.

Out‑of‑the‑box and customizable – works with default settings but allows deep customization for diverse business needs.

Core Concepts

Agents – LLM‑based entities configured with instructions and tools to perform specific tasks.

Handoffs – a mechanism that lets one agent delegate a task to another, improving specialization and efficiency.

Guardrails – safety checks that validate inputs and outputs to prevent misuse or unsafe behavior.

Tracing – built‑in logging of calls, tool usage, handoffs, and custom events for debugging and optimization.

Creating a Simple Agent

from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

This defines an Assistant agent and runs it synchronously.

Agent Loop Process

Call LLM – the agent invokes the language model with its configuration.

Process LLM response – the response may contain a final answer, a tool call, or a handoff request.

Check completion – if a final output is present, the loop ends.

Execute handoff – if a handoff is indicated, control passes to the designated agent.

Invoke tool – if a tool call is present, the tool runs and its result is fed back to the LLM.

Handoff Example

Two specialist agents are created for history and math tutoring, then combined under a triage agent that routes user queries to the appropriate specialist.

from agents import Agent, Runner
import asyncio

history_tutor_agent = Agent(
    name="History Tutor",
    handoff_description="Specialist agent for historical questions",
    instructions="You provide assistance with historical queries. Explain important events and context clearly."
)

math_tutor_agent = Agent(
    name="Math Tutor",
    handoff_description="Specialist agent for math questions",
    instructions="You provide help with math problems. Explain your reasoning at each step and include examples"
)

triage_agent = Agent(
    name="Triage Agent",
    instructions="You determine which agent to use based on the user's homework question",
    handoffs=[history_tutor_agent, math_tutor_agent]
)

async def main():
    result = await Runner.run(triage_agent, "What is the capital of France?")
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

The triage agent correctly forwards the question to the history tutor, which answers that the capital of France is Paris.

Guardrails Example

A guardrail agent checks whether a request is homework‑related. If the user asks about illegal activity, the guardrail rejects the request.

from agents import GuardrailFunctionOutput, Agent, Runner
from pydantic import BaseModel

class HomeworkOutput(BaseModel):
    is_homework: bool
    reasoning: str

guardrail_agent = Agent(
    name="Guardrail check",
    instructions="Check if the user is asking about homework.",
    output_type=HomeworkOutput,
)

async def homework_guardrail(ctx, agent, input_data):
    result = await Runner.run(guardrail_agent, input_data, context=ctx.context)
    final_output = result.final_output_as(HomeworkOutput)
    return GuardrailFunctionOutput(
        output_info=final_output,
        tripwire_triggered=not final_output.is_homework,
    )

When the input "Can you teach me how to make a bomb?" is processed, the guardrail returns is_homework=False and refuses to comply.

Tracing Benefits

Tracing records every event—LLM calls, tool usage, handoffs, and custom logs—allowing developers to analyze model behavior, debug errors, and visualize agent workflows for stable, efficient operation.

Additional Features

Tool Integration – built‑in tools (file retrieval, web search, computer use) and custom Python functions.

Multi‑Model Support – supports OpenAI models via the Responses API or Chat Completions API; other providers can be used by setting base_url.

Comparison with SmolAgents

Model Support

OpenAI Agents SDK – primarily supports OpenAI models; third‑party models via base_url.

SmolAgents – supports almost all third‑party providers and can run local LLMs with TransformersModel.

Tool Calling

OpenAI Agents SDK – uses JSON‑based function calling; supports built‑in and custom tools.

SmolAgents – executes Python code directly and also supports JSON function calls; offers a broader range of built‑in tools and extensibility via Python functions or subclassing.

Guardrails

OpenAI Agents SDK – built‑in guardrails for input filtering, output format enforcement, and unsafe task blocking.

SmolAgents – no native guardrails but provides sandboxed execution environments (E2B cloud sandbox or Docker) to mitigate unsafe code.

Handoff Mechanism

OpenAI Agents SDK – explicit handoff mechanism for automatic task delegation among agents.

SmolAgents – no explicit handoff; delegation is handled at the tool level based on tool descriptions.

Tracing

OpenAI Agents SDK – built‑in tracing for detailed execution logs.

SmolAgents – lacks native tracing; external services like Arize AI Phoenix or Langfuse are required.

Community Support

OpenAI Agents SDK – relatively high activity backed by the official OpenAI community; maintenance still early.

SmolAgents – smaller community but steady maintenance; fully open‑source.

Conclusion

Within the OpenAI ecosystem, OpenAI Agents SDK is the optimal choice for quickly building API‑driven agent applications, while SmolAgents offers greater flexibility through minimal design, code execution capabilities, and extensive LLM compatibility, giving developers more freedom for diverse use cases.