# llm 0.33 发布，升级 OpenAI 库与 HTTP 依赖，嵌入命令支持 --key，模板可组合，新增 reasoning_summary 选项

- 来源：Simon Willison
- 发布时间：2026-08-23 01:01
- AIWatch 分数：63
- AIWatch 标记：当日精选
- AIWatch 链接：https://aiwatch.icu/events/evt_01m0s8y1nn7v5ej6eq0e8fr98z
- 原文链接：https://simonwillison.net/2026/Aug/22/llm/

## 精选理由

常规快讯，保留列表

## AI 摘要

llm 0.33 发布，升级 OpenAI 库与 HTTP 依赖，嵌入命令支持 --key，模板可组合，新增 reasoning_summary 选项。

## 正文

Release: llm 0.33

My highlights from this release:

Upgraded to the OpenAI Python library 3.x and switched the HTTP client dependency from httpx to httpx2. #1608, #1631

I shipped a quick 0.32.1 fix for this yesterday, but this is the more comprehensive fix.

llm embed and llm embed-multi now accept --key. The Python EmbeddingModel.embed(), EmbeddingModel.embed_multi(), Collection.embed() and Collection.embed_multi() methods accept key= too, passing the resolved per-call key to embedding plugins without changing shared model state. Existing plugins that read self.key continue to work through a compatibility fallback. Thanks, ChrisJr404. #757, #1620

The embedding models now use the same pattern for keys that regular LLM models do.

llm prompt -t/--template can now be repeated to combine templates in order. This allows model configuration and options from one template to be used with a prompt from another.

This unlocks a neat pattern where you can create templates that package a model with a set of default options:

llm -m gpt-5.6-luna -o reasoning_effort high --save lhigh
llm "Generate an SVG of a pelican riding a bicycle" --save pelican
# Combine and run the templates
llm -t lhigh -t pelican

Reasoning-capable Responses API models now support a reasoning_summary option with auto, concise, and detailed values. This can be used with llm openai endpoint --responses. #1600

This is particularly useful for exercising different models that provide their own imitation of the OpenAI Responses API.

Tags: annotated-release-notes, llm