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`

OpenAI Agents SDK for Python Use this skill for openai agents / agents , not the TypeScript SDK. Resolve the installed package and its provider integrations before implementing an API. Current [SDK documentation](https://openai.github.io/openai agents python/) and a matching source tag take precedence over static examples. Choose a model from the current [OpenAI model catalog](https://developers.openai.com/api/docs/models) and [model selection guide](https://developers.openai.com/api/docs/guides/model selection). Check the selected model's tools, reasoning settings and provider availability; an SDK fallback or a demo ID is not a permanent recommendation. The wheel ships no docs. Offline, read the installed source: the version from importlib.metadata.version("openai agents") , public exports in agents/ init .py , docstrings and types in the package directory ( python c "import agents; print(agents. file )" ). The documentation source is docs/ at repository tag v<version . Core shape Runner.run sync raises inside a running event loop (async handlers and notebooks); use await Runner.run(...) . Runner.run streamed(...) itself is not awaited; consume its stream events() async iterator to completion. Checked with openai agents 0.22.3; confirm names in the installed source for another version: The example uses Responses (the SDK's default API). [GPT 6.1 Sol](https://developers.openai.com/api/docs/models/gpt 6.1 sol) requires Responses for tool calling and does not support none or minimal reasoning effort. Integration decisions Keep the project's provider, model and storage unless the task requires a change. Verify model IDs/capabilities in provider configuration or live docs. SDK model/settings defaults may change; configure product critical choices explicitly. Use native provider/client support when it fits. LiteLLM/Any LLM are optional integrations with their own compatibility and settings behavior. Choose handoffs when a specialist takes over, or agent.as tool() when the manager should continue after delegated work. A fixed pipeline does not need extra agents merely to implement ordinary control flow. Authorize side effects in tools. Agent instructions, output schemas and guardrails do not replace authorization or idempotency. Decide history ownership, approval/resume and tracing data policy before exposing a multi turn agent to untrusted clients. Read for the feature [Agents/providers](references/agents.md): model defaults, Azure and adapters. [Tools](references/tools.md): local/hosted execution, delegation and approval/resume. [Structured output](references/structured output.md): schema and capability constraints. [Streaming](references/streaming.md): event types, failures and guardrails. [Handoffs](references/handoffs.md): control transfer and filtering. [Guardrails](references/guardrails.md): execution timing and coverage. [Sessions](references/sessions.md): history ownership and persistence. [Orchestration/tracing](references/patterns.md): run limits and observability. [Sandbox](references/sandbox.md): workspace execution and resume state. Use the OpenAI Developer Docs MCP ( https://developers.openai.com/mcp ) if available for current OpenAI API/provider behavior, or its plain text index https://developers.openai.com/api/docs/llms.txt ; use the Python SDK's own reference for SDK signatures. Read selected [official examples](https://github.com/openai/openai agents python/tree/main/examples) from a compatible tag, not a copied catalog of demos. Verification Verify changed tools, multi turn history, approval/denial and failure recovery. Use installed agents.testing (verified in 0.22.3) for offline orchestration: ScriptedModel , ModelStep , assistant message , function call and model.assert complete() , with RunConfig(tracing disabled=True) ; assigning agent.model = ScriptedModel([...]) runs the core shape offline. Run the project's checks; report missing provider access separately from verified SDK behavior.

openai-agents-sdk