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f0166cadbb
## Why
`TurnHandle.run()` returned the raw app-server `Turn`, whose live
start/completed payloads do not include loaded `items`, so users saw
empty `items` after starting a turn. That made the handle-based path
behave differently from `Thread.run(...)`, and pushed examples toward
persisted-thread reads plus helper extraction.
This PR makes the run APIs standalone: starting a turn and running it
returns collected turn data directly, or fails visibly when required
stream events are missing.
## What Changed
- Replaces the public `RunResult` export with `TurnResult`.
- Adds turn metadata to `TurnResult`: `id`, `status`, `error`,
`started_at`, `completed_at`, and `duration_ms`, alongside
`final_response`, `items`, and `usage`.
- Changes `TurnHandle.run()` and `AsyncTurnHandle.run()` to consume
stream events with the same collector used by `Thread.run(...)`.
- Exports `TurnError` from `openai_codex.types` for the new result
shape.
- Updates tests, examples, docs, and the walkthrough notebook to use
`result.final_response` and `result.items` directly.
- Removes persisted-thread helper paths and placeholder/skipped control
flows from the public examples and notebook.
## Verification
- `python3 -m py_compile ...` over changed SDK, example, and test Python
files.
- `python3 -c "import json;
json.load(open('sdk/python/notebooks/sdk_walkthrough.ipynb'))"`
- `git diff --check`
- `PYTHONPATH=sdk/python/src python3 -c ...` import/signature smoke for
`TurnResult`, `TurnHandle.run`, and `AsyncTurnHandle.run`.
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
import sys
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from pathlib import Path
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_EXAMPLES_ROOT = Path(__file__).resolve().parents[1]
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if str(_EXAMPLES_ROOT) not in sys.path:
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sys.path.insert(0, str(_EXAMPLES_ROOT))
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from _bootstrap import ensure_local_sdk_src, runtime_config
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ensure_local_sdk_src()
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from openai_codex import Codex, TextInput
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with Codex(config=runtime_config()) as codex:
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thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
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first = thread.turn(TextInput("One sentence about structured planning.")).run()
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second = thread.turn(TextInput("Now restate it for a junior engineer.")).run()
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reopened = codex.thread_resume(thread.id)
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listing_active = codex.thread_list(limit=20, archived=False)
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reading = reopened.read(include_turns=True)
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_ = reopened.set_name("sdk-lifecycle-demo")
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_ = codex.thread_archive(reopened.id)
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listing_archived = codex.thread_list(limit=20, archived=True)
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unarchived = codex.thread_unarchive(reopened.id)
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resumed = codex.thread_resume(
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unarchived.id,
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model="gpt-5.4",
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config={"model_reasoning_effort": "high"},
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)
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resumed_result = resumed.turn(TextInput("Continue in one short sentence.")).run()
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forked = codex.thread_fork(unarchived.id, model="gpt-5.4")
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forked_result = forked.turn(TextInput("Take a different angle in one short sentence.")).run()
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compact_result = unarchived.compact()
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print("Lifecycle OK:", thread.id)
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print("first:", first.id, first.status)
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print("second:", second.id, second.status)
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print("read.turns:", len(reading.thread.turns))
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print("list.active:", len(listing_active.data))
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print("list.archived:", len(listing_archived.data))
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print("resumed:", resumed_result.id, resumed_result.status)
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print("forked:", forked_result.id, forked_result.status)
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print("compact:", compact_result.model_dump(mode="json", by_alias=True))
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