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## Why The SDK should publish under the reserved public distribution name `openai-codex`, and its import module should match that name in the Python style. Since package names can contain hyphens but import modules cannot, the public import path becomes `openai_codex`. Keeping the rename separate from the public API surface change makes the naming change easy to review and avoids mixing it with API curation. ## What - Rename the SDK distribution from `openai-codex-app-server-sdk` to `openai-codex`. - Rename the import package from `codex_app_server` to `openai_codex`. - Keep the runtime wheel as the separate `openai-codex-cli-bin` dependency. - Update docs, examples, notebooks, artifact scripts, lockfile metadata, and tests for the new distribution/module names. ## Stack 1. #21891 `[1/8]` Pin Python SDK runtime dependency 2. #21893 `[2/8]` Generate Python SDK types from pinned runtime 3. #21895 `[3/8]` Run Python SDK tests in CI 4. #21896 `[4/8]` Define Python SDK public API surface 5. This PR `[5/8]` Rename Python SDK package to `openai-codex` 6. #21910 `[6/8]` Add high-level Python SDK approval mode 7. #22014 `[7/8]` Add Python SDK app-server integration harness 8. #22021 `[8/8]` Add Python SDK Ruff formatting ## Verification - Updated package metadata and public API tests to assert the distribution and import names. Co-authored-by: Codex <noreply@openai.com>
112 lines
3.1 KiB
Markdown
112 lines
3.1 KiB
Markdown
# Getting Started
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This is the fastest path from install to a multi-turn thread using the public SDK surface.
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The SDK is experimental. Treat the API, bundled runtime strategy, and packaging details as unstable until the first public release.
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## 1) Install
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From repo root:
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```bash
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cd sdk/python
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uv sync
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source .venv/bin/activate
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```
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Requirements:
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- Python `>=3.10`
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- uv
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- installed `openai-codex-cli-bin` runtime package, or an explicit `codex_bin` override
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- local Codex auth/session configured
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## 2) Run your first turn (sync)
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```python
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from openai_codex import Codex
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with Codex() as codex:
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server = codex.metadata.serverInfo
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print("Server:", None if server is None else server.name, None if server is None else server.version)
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thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
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result = thread.run("Say hello in one sentence.")
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print("Thread:", thread.id)
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print("Text:", result.final_response)
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print("Items:", len(result.items))
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```
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What happened:
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- `Codex()` started and initialized `codex app-server`.
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- `thread_start(...)` created a thread.
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- `thread.run("...")` started a turn, consumed events until completion, and returned the final assistant response plus collected items and usage.
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- `result.final_response` is `None` when no final-answer or phase-less assistant message item completes for the turn.
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- use `thread.turn(...)` when you need a `TurnHandle` for streaming, steering, interrupting, or turn IDs/status
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- one client can consume multiple active turns concurrently; turn streams are routed by turn ID
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## 3) Continue the same thread (multi-turn)
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```python
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from openai_codex import Codex
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with Codex() 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.run("Summarize Rust ownership in 2 bullets.")
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second = thread.run("Now explain it to a Python developer.")
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print("first:", first.final_response)
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print("second:", second.final_response)
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```
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## 4) Async parity
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Use `async with AsyncCodex()` as the normal async entrypoint. `AsyncCodex`
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initializes lazily, and context entry makes startup/shutdown explicit.
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```python
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import asyncio
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from openai_codex import AsyncCodex
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async def main() -> None:
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async with AsyncCodex() as codex:
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thread = await codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
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result = await thread.run("Continue where we left off.")
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print(result.final_response)
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asyncio.run(main())
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```
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## 5) Resume an existing thread
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```python
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from openai_codex import Codex
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THREAD_ID = "thr_123" # replace with a real id
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with Codex() as codex:
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thread = codex.thread_resume(THREAD_ID)
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result = thread.run("Continue where we left off.")
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print(result.final_response)
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```
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## 6) Public app-server types
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The convenience wrappers live at the package root. Public app-server value and
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event types live under:
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```python
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from openai_codex.types import ThreadReadResponse, Turn, TurnStatus
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```
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## 7) Next stops
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- API surface and signatures: `docs/api-reference.md`
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- Common decisions/pitfalls: `docs/faq.md`
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- End-to-end runnable examples: `examples/README.md`
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