openai-agents-sdk

name	openai-agents-sdk
argument-hint	[question or feature]
description	OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent handoffs, function tools, guardrails, sessions, streaming, or tracing with the `openai-agents` / `agents` Python package — including Azure OpenAI via LiteLLM. Triggers on imports from `agents`, uses of `Runner.run_sync`/`Runner.run_streamed`, `@function_tool`, `AgentOutputSchema`, `SQLiteSession`, or questions about the openai-agents-python SDK.

OpenAI Agents SDK (Python)

Use this skill when developing AI agents using OpenAI Agents SDK (openai-agents package).

Quick Reference

Installation

pip install openai-agents

Environment Variables

# OpenAI (direct)
OPENAI_API_KEY=sk-...
LLM_PROVIDER=openai

# Azure OpenAI (via LiteLLM)
LLM_PROVIDER=azure
AZURE_API_KEY=...
AZURE_API_BASE=https://your-resource.openai.azure.com
AZURE_API_VERSION=2024-12-01-preview

Basic Agent

from agents import Agent, Runner

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model="gpt-5.4",  # or "gpt-5.4-mini", "gpt-5.4-nano"
)

# Synchronous
result = Runner.run_sync(agent, "Tell me a joke")
print(result.final_output)

# Asynchronous
result = await Runner.run(agent, "Tell me a joke")

Key Patterns

Pattern	Purpose
Basic Agent	Simple Q&A with instructions
Azure/LiteLLM	Azure OpenAI integration
AgentOutputSchema	Strict JSON validation with Pydantic
Function Tools	External actions (@function_tool)
Streaming	Real-time UI (Runner.run_streamed)
Handoffs	Specialized agents, delegation
Agents as Tools	Orchestration (agent.as_tool)
LLM as Judge	Iterative improvement loop
Guardrails	Input/output validation
Sessions	Automatic conversation history
Multi-Agent Pipeline	Multi-step workflows
Sandboxing	Isolated execution environment for agents
Subagents	Spawn specialized subordinate agents (Python; TS in beta/development)
Observability	Built-in execution graph recording

Preferred: Live Docs via MCP

Model names and API details change frequently. When available, consult the OpenAI Developer Docs MCP server (openaiDeveloperDocs) before relying on the static references below.

Setup (Codex CLI):

codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp

Or config (~/.codex/config.toml, VS Code .vscode/mcp.json, Cursor ~/.cursor/mcp.json):

[mcp_servers.openaiDeveloperDocs]
url = "https://developers.openai.com/mcp"

Key tools: mcp__openaiDeveloperDocs__search_openai_docs, fetch_openai_doc, list_api_endpoints, get_openapi_spec.

Rules: Cite fetched docs. Never speculate on field names, defaults, or current model IDs — fetch first. Keep quotes under 125 chars.

Fallback when MCP is unavailable: https://developers.openai.com/api/docs/llms.txt (plain-text index of all API docs; each entry has a .md twin at /api/docs/<slug>.md).

Reference Documentation

Offline/quick-lookup snippets. Verify model names and API signatures against the MCP or docs when accuracy matters.

• agents.md - Agent creation, Azure/LiteLLM integration
• tools.md - Function tools, hosted tools, agents as tools
• structured-output.md - Pydantic output, AgentOutputSchema
• streaming.md - Streaming patterns, SSE with FastAPI
• handoffs.md - Agent delegation
• guardrails.md - Input/output validation
• sessions.md - Sessions, conversation history
• patterns.md - Multi-agent workflows, LLM as judge, tracing

Official Documentation

• Docs: https://openai.github.io/openai-agents-python/
• Examples: https://github.com/openai/openai-agents-python/tree/main/examples
• Major update: https://openai.com/index/the-next-evolution-of-the-agents-sdk/
• Docs MCP setup: https://developers.openai.com/learn/docs-mcp
• Docs index (llms.txt): https://developers.openai.com/api/docs/llms.txt
• Current model IDs: https://platform.openai.com/docs/models