mirror of
https://github.com/openai/codex.git
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09a265122d
Remove CODEX_PYTHON_RUNTIME_VERSION from the repo bootstrap path and always provision the checked-in pinned codex-cli-bin runtime version for examples, notebook, and real integration coverage. This keeps repo-source Python execution aligned with one binary contract, rewires the real integration harness and notebook bootstrap to use the pinned runtime directly, and updates the docs to describe automatic pinned-runtime provisioning instead of env-driven overrides. Validation: - RUN_REAL_CODEX_TESTS=1 python3 -m pytest sdk/python/tests -rs - RUN_REAL_CODEX_TESTS=1 python3 -m pytest sdk/python/tests/test_real_app_server_integration.py -rs Co-authored-by: Codex <noreply@openai.com>
549 lines
20 KiB
Plaintext
549 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Codex Python SDK Walkthrough\n",
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"\n",
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"Public SDK surface only (`codex_app_server` root exports)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 1: bootstrap local SDK imports + pinned runtime package\n",
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"import os\n",
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"if sys.version_info < (3, 10):\n",
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" raise RuntimeError(\n",
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" f'Notebook requires Python 3.10+; current interpreter is {sys.version.split()[0]}.'\n",
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" )\n",
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"\n",
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"try:\n",
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" _ = os.getcwd()\n",
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"except FileNotFoundError:\n",
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" os.chdir(str(Path.home()))\n",
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"\n",
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"\n",
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"def _is_sdk_python_dir(path: Path) -> bool:\n",
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" return (path / 'pyproject.toml').exists() and (path / 'src' / 'codex_app_server').exists()\n",
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"\n",
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"\n",
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"def _iter_home_fallback_candidates(home: Path):\n",
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" # bounded depth scan under home to support launching notebooks from unrelated cwd values\n",
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" patterns = ('sdk/python', '*/sdk/python', '*/*/sdk/python', '*/*/*/sdk/python')\n",
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" for pattern in patterns:\n",
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" yield from home.glob(pattern)\n",
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"\n",
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"\n",
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"def _find_sdk_python_dir(start: Path) -> Path | None:\n",
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" checked = set()\n",
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"\n",
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" def _consider(candidate: Path) -> Path | None:\n",
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" resolved = candidate.resolve()\n",
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" if resolved in checked:\n",
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" return None\n",
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" checked.add(resolved)\n",
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" if _is_sdk_python_dir(resolved):\n",
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" return resolved\n",
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" return None\n",
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"\n",
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" for candidate in [start, *start.parents]:\n",
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" found = _consider(candidate)\n",
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" if found is not None:\n",
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" return found\n",
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"\n",
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" for candidate in [start / 'sdk' / 'python', *(parent / 'sdk' / 'python' for parent in start.parents)]:\n",
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" found = _consider(candidate)\n",
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" if found is not None:\n",
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" return found\n",
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"\n",
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" env_dir = os.environ.get('CODEX_PYTHON_SDK_DIR')\n",
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" if env_dir:\n",
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" found = _consider(Path(env_dir).expanduser())\n",
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" if found is not None:\n",
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" return found\n",
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"\n",
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" for entry in sys.path:\n",
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" if not entry:\n",
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" continue\n",
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" entry_path = Path(entry).expanduser()\n",
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" for candidate in (entry_path, entry_path / 'sdk' / 'python'):\n",
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" found = _consider(candidate)\n",
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" if found is not None:\n",
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" return found\n",
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"\n",
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" home = Path.home()\n",
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" for candidate in _iter_home_fallback_candidates(home):\n",
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" found = _consider(candidate)\n",
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" if found is not None:\n",
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" return found\n",
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"\n",
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" return None\n",
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"\n",
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"\n",
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"repo_python_dir = _find_sdk_python_dir(Path.cwd())\n",
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"if repo_python_dir is None:\n",
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" raise RuntimeError('Could not locate sdk/python. Set CODEX_PYTHON_SDK_DIR to your sdk/python path.')\n",
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"\n",
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"repo_python_str = str(repo_python_dir)\n",
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"if repo_python_str not in sys.path:\n",
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" sys.path.insert(0, repo_python_str)\n",
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"\n",
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"from _runtime_setup import ensure_runtime_package_installed\n",
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"\n",
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"runtime_version = ensure_runtime_package_installed(\n",
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" sys.executable,\n",
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" repo_python_dir,\n",
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")\n",
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"\n",
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"src_dir = repo_python_dir / 'src'\n",
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"src_str = str(src_dir)\n",
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"if src_str not in sys.path:\n",
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" sys.path.insert(0, src_str)\n",
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"\n",
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"# Force fresh imports after SDK upgrades in the same notebook kernel.\n",
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"for module_name in list(sys.modules):\n",
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" if module_name == 'codex_app_server' or module_name.startswith('codex_app_server.'):\n",
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" sys.modules.pop(module_name, None)\n",
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"\n",
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"print('Kernel:', sys.executable)\n",
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"print('SDK source:', src_dir)\n",
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"print('Runtime package:', runtime_version)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 2: imports (public only)\n",
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"from codex_app_server import (\n",
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" AsyncCodex,\n",
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" Codex,\n",
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" ImageInput,\n",
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" LocalImageInput,\n",
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" TextInput,\n",
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" retry_on_overload,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 3: simple sync conversation\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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" turn = thread.turn(TextInput('Explain gradient descent in 3 bullets.'))\n",
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" result = turn.run()\n",
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"\n",
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" print('server:', codex.metadata)\n",
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" print('status:', result.status)\n",
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" print(result.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 4: multi-turn continuity in same thread\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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"\n",
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" first = thread.turn(TextInput('Give a short summary of transformers.')).run()\n",
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" second = thread.turn(TextInput('Now explain that to a high-school student.')).run()\n",
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"\n",
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" print('first status:', first.status)\n",
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" print('second status:', second.status)\n",
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" print('second text:', second.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 5: full thread lifecycle and branching (sync)\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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" first = thread.turn(TextInput('One sentence about structured planning.')).run()\n",
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" second = thread.turn(TextInput('Now restate it for a junior engineer.')).run()\n",
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"\n",
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" reopened = codex.thread_resume(thread.id)\n",
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" listing_active = codex.thread_list(limit=20, archived=False)\n",
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" reading = reopened.read(include_turns=True)\n",
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"\n",
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" _ = reopened.set_name('sdk-lifecycle-demo')\n",
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" _ = codex.thread_archive(reopened.id)\n",
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" listing_archived = codex.thread_list(limit=20, archived=True)\n",
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" unarchived = codex.thread_unarchive(reopened.id)\n",
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"\n",
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" resumed_info = 'n/a'\n",
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" try:\n",
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" resumed = codex.thread_resume(\n",
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" unarchived.id,\n",
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" model='gpt-5',\n",
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" config={'model_reasoning_effort': 'high'},\n",
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" )\n",
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" resumed_result = resumed.turn(TextInput('Continue in one short sentence.')).run()\n",
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" resumed_info = f'{resumed_result.turn_id} {resumed_result.status}'\n",
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" except Exception as e:\n",
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" resumed_info = f'skipped({type(e).__name__})'\n",
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"\n",
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" forked_info = 'n/a'\n",
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" try:\n",
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" forked = codex.thread_fork(unarchived.id, model='gpt-5')\n",
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" forked_result = forked.turn(TextInput('Take a different angle in one short sentence.')).run()\n",
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" forked_info = f'{forked_result.turn_id} {forked_result.status}'\n",
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" except Exception as e:\n",
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" forked_info = f'skipped({type(e).__name__})'\n",
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"\n",
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" compact_info = 'sent'\n",
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" try:\n",
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" _ = unarchived.compact()\n",
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" except Exception as e:\n",
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" compact_info = f'skipped({type(e).__name__})'\n",
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"\n",
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" print('Lifecycle OK:', thread.id)\n",
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" print('first:', first.turn_id, first.status)\n",
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" print('second:', second.turn_id, second.status)\n",
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" print('read.turns:', len(reading.thread.turns or []))\n",
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" print('list.active:', len(listing_active.data))\n",
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" print('list.archived:', len(listing_archived.data))\n",
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" print('resumed:', resumed_info)\n",
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" print('forked:', forked_info)\n",
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" print('compact:', compact_info)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 5b: one turn with most optional turn params\n",
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"from pathlib import Path\n",
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"from codex_app_server import (\n",
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" AskForApproval,\n",
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" Personality,\n",
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" ReasoningEffort,\n",
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" ReasoningSummary,\n",
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" SandboxPolicy,\n",
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")\n",
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"\n",
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"output_schema = {\n",
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" 'type': 'object',\n",
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" 'properties': {\n",
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" 'summary': {'type': 'string'},\n",
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" 'actions': {'type': 'array', 'items': {'type': 'string'}},\n",
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" },\n",
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" 'required': ['summary', 'actions'],\n",
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" 'additionalProperties': False,\n",
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"}\n",
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"\n",
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"sandbox_policy = SandboxPolicy.model_validate({'type': 'readOnly', 'access': {'type': 'fullAccess'}})\n",
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"summary = ReasoningSummary.model_validate('concise')\n",
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"\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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" turn = thread.turn(\n",
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" TextInput('Propose a safe production feature-flag rollout. Return JSON matching the schema.'),\n",
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" approval_policy=AskForApproval.never,\n",
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" cwd=str(Path.cwd()),\n",
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" effort=ReasoningEffort.medium,\n",
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" model='gpt-5',\n",
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" output_schema=output_schema,\n",
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" personality=Personality.pragmatic,\n",
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" sandbox_policy=sandbox_policy,\n",
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" summary=summary,\n",
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" )\n",
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" result = turn.run()\n",
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"\n",
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" print('status:', result.status)\n",
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" print(result.text)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 5c: choose highest model + highest supported reasoning, then run turns\n",
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"from pathlib import Path\n",
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"from codex_app_server import (\n",
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" AskForApproval,\n",
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" Personality,\n",
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" ReasoningEffort,\n",
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" ReasoningSummary,\n",
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" SandboxPolicy,\n",
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")\n",
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"\n",
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"reasoning_rank = {\n",
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" 'none': 0,\n",
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" 'minimal': 1,\n",
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" 'low': 2,\n",
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" 'medium': 3,\n",
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" 'high': 4,\n",
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" 'xhigh': 5,\n",
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"}\n",
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"\n",
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"\n",
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"def pick_highest_model(models):\n",
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" visible = [m for m in models if not m.hidden] or models\n",
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" known_names = {m.id for m in visible} | {m.model for m in visible}\n",
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" top_candidates = [m for m in visible if not (m.upgrade and m.upgrade in known_names)]\n",
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" pool = top_candidates or visible\n",
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" return max(pool, key=lambda m: (m.model, m.id))\n",
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"\n",
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"\n",
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"def pick_highest_turn_effort(model) -> ReasoningEffort:\n",
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" if not model.supported_reasoning_efforts:\n",
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" return ReasoningEffort.medium\n",
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" best = max(model.supported_reasoning_efforts, key=lambda opt: reasoning_rank.get(opt.reasoning_effort.value, -1))\n",
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" return ReasoningEffort(best.reasoning_effort.value)\n",
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"\n",
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"\n",
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"output_schema = {\n",
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" 'type': 'object',\n",
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" 'properties': {\n",
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" 'summary': {'type': 'string'},\n",
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" 'actions': {'type': 'array', 'items': {'type': 'string'}},\n",
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" },\n",
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" 'required': ['summary', 'actions'],\n",
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" 'additionalProperties': False,\n",
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"}\n",
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"sandbox_policy = SandboxPolicy.model_validate({'type': 'readOnly', 'access': {'type': 'fullAccess'}})\n",
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"\n",
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"with Codex() as codex:\n",
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" models = codex.models(include_hidden=True)\n",
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" selected_model = pick_highest_model(models.data)\n",
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" selected_effort = pick_highest_turn_effort(selected_model)\n",
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"\n",
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" print('selected.model:', selected_model.model)\n",
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" print('selected.effort:', selected_effort.value)\n",
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"\n",
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" thread = codex.thread_start(model=selected_model.model, config={'model_reasoning_effort': selected_effort.value})\n",
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"\n",
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" first = thread.turn(\n",
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" TextInput('Give one short sentence about reliable production releases.'),\n",
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" model=selected_model.model,\n",
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" effort=selected_effort,\n",
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" ).run()\n",
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" print('agent.message:', first.text)\n",
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" print('usage:', first.usage)\n",
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"\n",
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" second = thread.turn(\n",
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" TextInput('Return JSON for a safe feature-flag rollout plan.'),\n",
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" approval_policy=AskForApproval.never,\n",
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" cwd=str(Path.cwd()),\n",
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" effort=selected_effort,\n",
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" model=selected_model.model,\n",
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" output_schema=output_schema,\n",
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" personality=Personality.pragmatic,\n",
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" sandbox_policy=sandbox_policy,\n",
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" summary=ReasoningSummary.model_validate('concise'),\n",
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" ).run()\n",
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" print('agent.message.params:', second.text)\n",
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" print('usage.params:', second.usage)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 6: multimodal with remote image\n",
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"remote_image_url = 'https://raw.githubusercontent.com/github/explore/main/topics/python/python.png'\n",
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"\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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" result = thread.turn([\n",
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" TextInput('What do you see in this image? 3 bullets.'),\n",
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" ImageInput(remote_image_url),\n",
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" ]).run()\n",
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"\n",
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" print('status:', result.status)\n",
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" print(result.text)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 7: multimodal with local image (bundled asset)\n",
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"local_image_path = repo_python_dir / 'examples' / 'assets' / 'sample_scene.png'\n",
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"if not local_image_path.exists():\n",
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" raise FileNotFoundError(f'Missing bundled image: {local_image_path}')\n",
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"\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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" result = thread.turn([\n",
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" TextInput('Describe this local image in 2 bullets.'),\n",
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" LocalImageInput(str(local_image_path.resolve())),\n",
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" ]).run()\n",
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"\n",
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" print('status:', result.status)\n",
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" print(result.text)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cell 8: retry-on-overload pattern\n",
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"with Codex() as codex:\n",
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" thread = codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
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"\n",
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" result = retry_on_overload(\n",
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" lambda: thread.turn(TextInput('List 5 failure modes in distributed systems.')).run(),\n",
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" max_attempts=3,\n",
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" initial_delay_s=0.25,\n",
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" max_delay_s=2.0,\n",
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" )\n",
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"\n",
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" print('status:', result.status)\n",
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" print(result.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
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"# Cell 9: full thread lifecycle and branching (async)\n",
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"import asyncio\n",
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"\n",
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"\n",
|
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"async def async_lifecycle_demo():\n",
|
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" async with AsyncCodex() as codex:\n",
|
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" thread = await codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
|
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" first = await (await thread.turn(TextInput('One sentence about structured planning.'))).run()\n",
|
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" second = await (await thread.turn(TextInput('Now restate it for a junior engineer.'))).run()\n",
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"\n",
|
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" reopened = await codex.thread_resume(thread.id)\n",
|
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" listing_active = await codex.thread_list(limit=20, archived=False)\n",
|
|
" reading = await reopened.read(include_turns=True)\n",
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"\n",
|
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" _ = await reopened.set_name('sdk-lifecycle-demo')\n",
|
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" _ = await codex.thread_archive(reopened.id)\n",
|
|
" listing_archived = await codex.thread_list(limit=20, archived=True)\n",
|
|
" unarchived = await codex.thread_unarchive(reopened.id)\n",
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"\n",
|
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" resumed_info = 'n/a'\n",
|
|
" try:\n",
|
|
" resumed = await codex.thread_resume(\n",
|
|
" unarchived.id,\n",
|
|
" model='gpt-5',\n",
|
|
" config={'model_reasoning_effort': 'high'},\n",
|
|
" )\n",
|
|
" resumed_result = await (await resumed.turn(TextInput('Continue in one short sentence.'))).run()\n",
|
|
" resumed_info = f'{resumed_result.turn_id} {resumed_result.status}'\n",
|
|
" except Exception as e:\n",
|
|
" resumed_info = f'skipped({type(e).__name__})'\n",
|
|
"\n",
|
|
" forked_info = 'n/a'\n",
|
|
" try:\n",
|
|
" forked = await codex.thread_fork(unarchived.id, model='gpt-5')\n",
|
|
" forked_result = await (await forked.turn(TextInput('Take a different angle in one short sentence.'))).run()\n",
|
|
" forked_info = f'{forked_result.turn_id} {forked_result.status}'\n",
|
|
" except Exception as e:\n",
|
|
" forked_info = f'skipped({type(e).__name__})'\n",
|
|
"\n",
|
|
" compact_info = 'sent'\n",
|
|
" try:\n",
|
|
" _ = await unarchived.compact()\n",
|
|
" except Exception as e:\n",
|
|
" compact_info = f'skipped({type(e).__name__})'\n",
|
|
"\n",
|
|
" print('Lifecycle OK:', thread.id)\n",
|
|
" print('first:', first.turn_id, first.status)\n",
|
|
" print('second:', second.turn_id, second.status)\n",
|
|
" print('read.turns:', len(reading.thread.turns or []))\n",
|
|
" print('list.active:', len(listing_active.data))\n",
|
|
" print('list.archived:', len(listing_archived.data))\n",
|
|
" print('resumed:', resumed_info)\n",
|
|
" print('forked:', forked_info)\n",
|
|
" print('compact:', compact_info)\n",
|
|
"\n",
|
|
"\n",
|
|
"await async_lifecycle_demo()\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Cell 10: async stream + steer + interrupt (best effort)\n",
|
|
"import asyncio\n",
|
|
"\n",
|
|
"\n",
|
|
"async def async_stream_demo():\n",
|
|
" async with AsyncCodex() as codex:\n",
|
|
" thread = await codex.thread_start(model='gpt-5', config={'model_reasoning_effort': 'high'})\n",
|
|
" turn = await thread.turn(TextInput('Count from 1 to 200 with commas, then one summary sentence.'))\n",
|
|
"\n",
|
|
" try:\n",
|
|
" _ = await turn.steer(TextInput('Keep it brief and stop after 20 numbers.'))\n",
|
|
" print('steer: sent')\n",
|
|
" except Exception as e:\n",
|
|
" print('steer: skipped', type(e).__name__)\n",
|
|
"\n",
|
|
" try:\n",
|
|
" _ = await turn.interrupt()\n",
|
|
" print('interrupt: sent')\n",
|
|
" except Exception as e:\n",
|
|
" print('interrupt: skipped', type(e).__name__)\n",
|
|
"\n",
|
|
" event_count = 0\n",
|
|
" async for event in turn.stream():\n",
|
|
" event_count += 1\n",
|
|
" print(event.method, event.payload)\n",
|
|
"\n",
|
|
" print('events.count:', event_count)\n",
|
|
"\n",
|
|
"\n",
|
|
"await async_stream_demo()\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"name": "python",
|
|
"version": "3.10+"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|