From laguagu-claude-code-nextjs-skills
Builds AI agents with OpenAI Agents SDK (Python). Handles multi-agent handoffs, function tools, guardrails, sessions, streaming, and tracing. Use with Azure OpenAI via LiteLLM.
How this skill is triggered — by the user, by Claude, or both
Slash command
/laguagu-claude-code-nextjs-skills:openai-agents-sdk [question or feature][question or feature]The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this skill when developing AI agents using OpenAI Agents SDK (`openai-agents` package).
Use this skill when developing AI agents using OpenAI Agents SDK (openai-agents package).
pip install openai-agents
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.
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")
| 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 |
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).
Offline/quick-lookup snippets. Verify model names and API signatures against the MCP or docs when accuracy matters.
npx claudepluginhub joshuarweaver/cascade-code-languages-misc-1 --plugin laguagu-claude-code-nextjs-skillsProvides in-process AI agent framework for TypeScript/Node.js, allowing Claude agent to run without CLI subprocess for serverless and cloud environments.
Builds and troubleshoots Claude Agent SDK apps in Python and TypeScript, covering APIs, sessions, permissions, streaming, tools, plugins, and extensibility.
Builds text, realtime voice, and multi-agent apps with OpenAI Agents SDK in JavaScript/TypeScript. Covers tools, guardrails, workflows, templates, and fixes Zod schema errors, tool calls, infinite loops.