openai-agents-sdk_skill

This skill helps you design and deploy OpenAI Agents SDK powered multi-agent systems with handoffs, guardrails, tools, and tracing.
  • Python

4

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill jawwad-ali/claude-code-skills --skill openai-agents-sdk

  • SKILL.md13.5 KB

Overview

This skill provides practical guidance for building agentic AI applications with the OpenAI Agents SDK in Python. It covers agents, handoffs, tools, guardrails, Runner usage, tracing, and multi-agent patterns to help you design reliable, observable workflows. Follow the examples to configure agents, add function tools, implement validation, and orchestrate multi-agent systems.

How this skill works

The SDK models agents as LLM-driven components configured with instructions, optional structured output types, tools (callable functions), and handoffs to other agents. A Runner executes agent loops, manages conversation history and context, and supports streaming and tracing. Guardrails run as input/output validators that can block or transform flows and raise clear exceptions that you should handle.

When to use it

  • Create a conversational or task-oriented AI agent with clear instructions and tools
  • Build a triage or multi-agent routing layer that forwards requests to specialists
  • Implement safe input/output validation and policy checks using guardrails
  • Expose external APIs or internal systems to agents via function tools
  • Add observability and debugging with tracing during development and production

Best practices

  • Write precise, unambiguous instructions and handoff descriptions
  • Use Pydantic models for structured outputs to make downstream processing predictable
  • Keep tools focused: single responsibility and clear docstrings for auto-generated tool descriptions
  • Implement input and output guardrails to detect and stop unsafe content or PII leaks
  • Share state through the Runner context rather than global variables
  • Enable tracing in development and set a custom trace handler for monitoring

Example use cases

  • Triage agent that routes customer requests to Billing or Technical specialists
  • Pipeline that analyzes input, processes data, and formats results across stages
  • Supervisor coordinating research and writing agents, then merging outputs
  • Function tools that fetch user data, call external APIs, or read files with context access
  • Guardrails that block harmful inputs or detect PII before returning responses

FAQ

Decorate a Python function with @function_tool and include it in the Agent.tools list. Use async functions for I/O and accept a RunContextWrapper to access shared context.

What happens when a guardrail trips?

Guardrails return a GuardrailFunctionOutput indicating whether a tripwire triggered. The Runner raises InputGuardrailTripwireTriggered or OutputGuardrailTripwireTriggered, which you should catch and handle to present safe responses or take alternate actions.

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