Release: llm 0.33

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Summary

The release of llm 0.33 includes upgrades to the OpenAI Python library, new key handling for embedding models, and enhanced template functionality.

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Key Insights

“The embedding models now use the same pattern for keys that regular LLM models do.” — Discussing the new key handling in embedding models.

“This unlocks a neat pattern where you can create templates that package a model with a set of default options.” — Explaining the new template functionality.

“Reasoning-capable Responses API models now support a reasoning_summary option with auto, concise, and detailed values.” — Highlighting the new features in the Responses API.

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Topics

• openai
• python-library
• embedding-models
• llm

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Full Article

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.

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