OpenAI Agents SDK
OpenAI's official SDK for building production-ready agents with managed infrastructure, tool use, handoffs between specialized agents, and built-in guardrails. It provides a minimal yet powerful set of primitives: Agents, Handoffs, Guardrails, and a Runner to orchestrate everything.
Architecture Overview
The SDK is built around an Agent class that bundles instructions, tools, and model configuration. A Runner orchestrates the agentic loop, invoking the model, processing tool calls, and managing handoffs between agents. Guardrails run in parallel with the main agent to validate inputs and outputs, enabling safe multi-step execution.
When to Use OpenAI Agents SDK
- Production AI assistants with handoffs
- Tool-augmented chatbots and copilots
- Enterprise workflows with guardrails
- Multi-agent systems with specialized roles
- Customer support automation pipelines
Strengths & Weaknesses
Strengths
- Deep OpenAI model integration and optimization
- Built-in guardrails for input/output validation
- Elegant handoff mechanism between specialized agents
- Tracing and observability out of the box
- Minimal abstraction with maximum control
Weaknesses
- OpenAI model lock-in by default (requires adapters for other providers)
- Python-only SDK at launch
- Smaller plugin ecosystem compared to LangChain
Quick Start
from agents import Agent, Runner, function_tool
@function_tool
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"The weather in {city} is sunny, 72°F."
weather_agent = Agent(
name="Weather Assistant",
instructions="You are a helpful weather assistant. Use the get_weather tool to answer questions about weather.",
tools=[get_weather],
)
# Run the agent
result = Runner.run_sync(weather_agent, "What's the weather in Tokyo?")
print(result.final_output)Features at a Glance
| Developer | OpenAI |
| Language | Python |
| License | MIT |
| GitHub Stars | 15k+ |
| MCP Support | Yes |
| Multi-Agent | Yes |
Notable Users
Resources
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