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name: core-runtime description: "Define and run OpenAI Agents Python core agents, interpret run results and streams, configure core Runner behavior, and resume interrupted RunState flows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Core Runtime

Use this sub-skill when the task is to define a plain Agent, call Runner.run, Runner.run_sync, or Runner.run_streamed, inspect RunResult / RunResultStreaming, reason about new_items and stream events, configure core RunConfig behavior, or resume a paused RunState after human approval.

Start Here

• For verified signatures and object relationships, read references/api-reference.md.
• For implementation recipes, read references/workflows.md.
• For failure diagnosis, read references/troubleshooting.md.
• To inspect the installed/core import surface without network calls, run scripts/inspect_core_runtime.py.

Routing Boundaries

• Stay here for agent construction, Runner loops, sync/async/streamed execution, RunResult properties, RunState pause/resume, previous_response_id, conversation_id, max_turns, call_model_input_filter, and stream-event interpretation.
• Route function tools, handoffs, approvals declarations, guardrails, and tool-specific error policy depth to ../tools-handoffs-guardrails/SKILL.md.
• Route persistent session backends and memory-store choices to ../sessions-memory/SKILL.md.
• Route provider/model transport, OpenAI provider setup, websocket provider selection, and model retries to ../models-providers/SKILL.md.
• Route tracing/export details to ../tracing-observability/SKILL.md.
• Route sandbox-specific RunConfig.sandbox, manifests, and sandbox agents to ../sandbox-agents/SKILL.md.

Core Decisions

• Prefer Runner.run inside async applications and Runner.run_sync only from synchronous code with no active event loop.
• Use Runner.run_streamed when UI code needs raw model deltas or semantic progress events; keep consuming stream_events() until it finishes.
• Use exactly one conversation-state strategy per turn: local to_input_list(), a session, conversation_id, or previous_response_id.
• Resume approvals with result.to_state(), state.approve(...) / state.reject(...), then pass the RunState back to the same original top-level agent.
• Inspect new_items for SDK-rich metadata and to_input_list() for replay-ready Responses input items.

Safety Notes

• Do not tell future agents to run original repo examples or tests; the patterns are distilled into the bundled references.
• Do not store API keys, local paths, or environment details in RunState.context unless the application intentionally wants them serialized.
• If a streamed run is canceled or paused for approval, drain stream_events() before inspecting final result fields or resuming.