openai-agents-sdk — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited openai-agents-sdk (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when developing AI agents using OpenAI Agents SDK (openai-agents package).
pip install openai-agents# 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-previewfrom 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/mcpOr 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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.