The release of llm 0.36 introduces new OpenAI models gpt-6-sol and gpt-6-luna, and adds support for model plugins to declare that they do not support conversations via supports_conversation = False. When such models receive assistant or tool history, llm raises a ConversationNotSupported error and the chat interface rejects them before starting a session. Additional changes include wrapping reasoning traces in Markdown output with <details> tags and bug fixes from five contributors.
GitHub Models is now retired I missed this news until today, when the GitHub Actions run for my simonw/research repository failed with this error message: GitHub Models is temporarily unavailable as part of a scheduled retirement brownout. That message is already stale, because the retirement has been completed.
The release of datasette-llm 0.1a9 introduces a small user‑experience improvement: the plugin now automatically detects the default LLM model, eliminating the need for users to manually configure a default model when running Datasette Agent. This change simplifies the setup process for users who previously had to specify a model with the `-s plugins.datasette-agent.default_model` option.
The article announces the release of llm version 0.35, which introduces a new OpenAI model named gpt-6-astra for GPT-6 Astra. It highlights the addition of this model to the llm library and tags the release with openai, llm, and gpt-6-astra.
The article announces the release of llm-echo version 0.5, a debug plugin that simulates an LLM by echoing back prompts without actual model inference. The new version updates token usage calculations to include tool calls, system prompts, and reasoning tokens. An example shows how to invoke the plugin and view the resulting prompt and token usage.
The release of llm 0.37 introduces numerous new features and fixes accumulated over two months, including new GPT-6.1 models, an openai endpoint add command, and a cheaper default model (gpt-6-luna). It enhances embedding support for varied content types and incorporates many AI‑assisted pull requests from multiple authors. The update also highlights the project’s efficient handling of AI‑generated contributions, aided by triage tools like Opus 5.5 and GPT-6 Astra.
The release of llm 0.33 introduces several key updates: it upgrades to the OpenAI Python library 3.x and switches the HTTP client from httpx to httpx2, adds comprehensive key handling for embedding functions, allows repeated template usage to combine configurations, and adds a reasoning_summary option for reasoning-capable responses. These changes improve compatibility, flexibility, and usability of the llm tool.
The release of llm-mistral 0.16 introduces support for reasoning models, notably the newly released Mistral Large 4. This update expands the library’s capabilities to handle more advanced language model tasks that involve reasoning. The release is tagged under llm, mistral, and llm-reasoning.
Release: llm-openrouter 0. 7 Now that this plugin is compatible with LLM 0.
Simon Willison released the llm-typesafe 0.1a0 plugin, adding support for TypeSafe AI’s Jev model to the LLM tool. Users install it with `llm install llm-typesafe`, set an API key, and can then ask Jev-model questions such as yes/no, choice, or scoring queries via the `llm -m jev` command. The release includes examples for each question type and references a README for further details.
The release of llm 0.34 introduces a new feature that enhances log output by including response duration in both milliseconds and a human‑readable format. The short log view now contains a dedicated duration_ms field. Additionally, the update incorporates multiple bug fixes and a notable performance boost to llm logs, attributed to waveplate integration.
The article announces the release of datasette-llm version 0.1a10. It reports a bug fix that corrects the demo site at agent.datasette.io, ensuring that only LLM models specified in the configuration are available. The update addresses a configuration issue affecting model availability.
Anthropic has announced that it will donate the Agent Skills standard to the Agentic AI Foundation (AAIF), a Linux Foundation‑backed organization co‑founded with Block and OpenAI. The move follows Anthropic’s earlier declaration that skills would be an open standard and aligns with the AAIF’s recent role as the home of the Model Context Protocol (MCP). The donation is part of Anthropic’s commitment to keeping Agent Skills community‑driven and vendor‑neutral.