openai-agents-sdk
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool int
OpenAI Agents SDK (Python)
Use this skill when developing AI agents using OpenAI Agents SDK (openai-agents package).
Quick Reference
Installation
uv add openai-agents # or `pip install openai-agents` outside a uv project
Environment Variables
OPENAI_API_KEY=sk-...
Using Azure or another provider instead? See agents.md — don't hardcode provider env vars here, they vary and go stale.
Basic Agent
from agents import Agent, Runner
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant.",
model="gpt-5.6-sol", # or "gpt-5.6-terra" / "gpt-5.6-luna" (cheaper tiers). There is no
# bare "gpt-5.6" — every GPT-5.6 call names a variant. Verify
# current IDs from the model catalog.
)
# 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, multi-provider setup via LiteLLM
- 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