Openai Agents 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 (@functiontool) | | Streaming | Real-time UI (Runner.runstreamed) | | 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/.md).

Reference Documentation

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

• [agents.md](references/agents.md) - Agent creation, Azure/LiteLLM integration
• [tools.md](references/tools.md) - Function tools, hosted tools, agents as tools
• [structured-output.md](references/structured-output.md) - Pydantic output, AgentOutputSchema
• [streaming.md](references/streaming.md) - Streaming patterns, SSE with FastAPI
• [handoffs.md](references/handoffs.md) - Agent delegation
• [guardrails.md](references/guardrails.md) - Input/output validation
• [sessions.md](references/sessions.md) - Sessions, conversation history
• [patterns.md](references/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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: laguagu
• Source: laguagu/claude-code-nextjs-skills
• License: MIT

Install and usage instructions live in the source repository linked above.