examples-auto-run

作者: openai

以自動模式執行 Python 範例,包含日誌記錄、重新執行輔助工具及背景控制。

npx skills add https://github.com/openai/openai-agents-python --skill examples-auto-run

examples-auto-run

What it does

  • Runs uv run examples/run_examples.py with:
    • Optional dependency extras enabled by default: litellm, any-llm, sqlalchemy, redis, blaxel, modal, runloop, and temporal.
    • EXAMPLES_INTERACTIVE_MODE=auto (auto-input/auto-approve).
    • Per-example logs under .tmp/examples-start-logs/.
    • Main summary log path passed via --main-log (also under .tmp/examples-start-logs/).
    • Generates a rerun list of failures at .tmp/examples-rerun.txt when --write-rerun is set.
  • Provides start/stop/status/logs/tail/collect/rerun helpers via run.sh.
  • Background option keeps the process running with a pidfile; stop cleans it up.

Usage

# Start (auto mode; interactive included by default)
.agents/skills/examples-auto-run/scripts/run.sh start [extra args to run_examples.py]
# Examples:
.agents/skills/examples-auto-run/scripts/run.sh start --filter basic
.agents/skills/examples-auto-run/scripts/run.sh start --include-server --include-audio

# Check status
.agents/skills/examples-auto-run/scripts/run.sh status

# Stop running job
.agents/skills/examples-auto-run/scripts/run.sh stop

# List logs
.agents/skills/examples-auto-run/scripts/run.sh logs

# Tail latest log (or specify one)
.agents/skills/examples-auto-run/scripts/run.sh tail
.agents/skills/examples-auto-run/scripts/run.sh tail main_20260113-123000.log

# Collect rerun list from a main log (defaults to latest main_*.log)
.agents/skills/examples-auto-run/scripts/run.sh collect

# Rerun only failed entries from rerun file (auto mode)
.agents/skills/examples-auto-run/scripts/run.sh rerun

Codex execution environment

  • When Codex invokes start or rerun, run the command outside the Codex sandbox by default (sandbox_permissions=require_escalated). Many examples start nested sandboxes, browsers, npm helpers, or cloud/local service processes; running from inside the Codex sandbox can produce environment-only failures such as sandbox-exec: sandbox_apply: Operation not permitted, Playwright cache permission errors, or npm cache permission errors.
  • Use sandboxed execution only when the user explicitly asks for it or when running a narrow dry-run / log inspection command that does not execute examples.

Defaults (overridable via env)

  • EXAMPLES_INTERACTIVE_MODE=auto
  • EXAMPLES_INCLUDE_INTERACTIVE=1
  • EXAMPLES_INCLUDE_SERVER=0
  • EXAMPLES_INCLUDE_AUDIO=0
  • EXAMPLES_INCLUDE_EXTERNAL=0
  • EXAMPLES_UV_EXTRAS="litellm any-llm sqlalchemy redis blaxel modal runloop temporal" (set to an empty string to disable extras)
  • Auto-approvals in auto mode: APPLY_PATCH_AUTO_APPROVE=1, SHELL_AUTO_APPROVE=1, AUTO_APPROVE_MCP=1

Log locations

  • Main logs: .tmp/examples-start-logs/main_*.log
  • Per-example logs (from run_examples.py): .tmp/examples-start-logs/<module_path>.log
  • Rerun list: .tmp/examples-rerun.txt
  • Stdout logs: .tmp/examples-start-logs/stdout_*.log

Notes

  • The runner delegates to uv run --extra ... examples/run_examples.py, which already writes per-example logs and supports --collect, --rerun-file, and --print-auto-skip.
  • examples/sandbox/extensions/vercel_runner.py is temporarily excluded from auto runs due to credential issues. Do not force-run it until the credential setup is fixed.
  • start uses --write-rerun so failures are captured automatically.
  • If .tmp/examples-rerun.txt exists and is non-empty, invoking the skill with no args runs rerun by default.

Behavioral validation (Codex/LLM responsibility)

The runner does not perform any automated behavioral validation. After every foreground start or rerun, Codex must manually validate all exit-0 entries:

  1. Read the example source (and comments) to infer intended flow, tools used, and expected key outputs.
  2. Open the matching per-example log under .tmp/examples-start-logs/.
  3. Confirm the intended actions/results occurred; flag omissions or divergences.
  4. Do this for all passed examples, not just a sample.
  5. Report immediately after the run with concise citations to the exact log lines that justify the validation.

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