New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging

I released <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">LLM 0.32</a> this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, new models, and new features enabled by the OpenAI Responses API. I also released a new version of the <a href="https://github.com/simonw/llm-anthropic">llm-anthropic plugin</a> with substantial updates of its own. <h4 id="headline-features-for-llm-cli-users">Headline features for LLM CLI users</h4> Running LLM against reasoning models now displays their reasoning traces to standard error, so you can see what they are "thinking" without that information being included in the standard output that you might pipe to another tool. Add <code>-R/--hide-reasoning</code> to turn this off. <img src="https://static.simonwillison.net/static/2026/best-pelicans.gif" alt="Running llm "think about the best thing about pelicans" in the macOS terminal window - grey text outputs saying Exploring pelican qualities, then after a paragraph of that a white paragraph of text comes out saying: The best thing about pelicans is their wonderfully oversized, practical design: that enormous bill and pouch look comical, but they make pelicans remarkably skilled fishers. Even better, many species cooperate—working together to herd fish before scooping them up. They’re a great mix of goofy, graceful, and surprisingly clever." style="max-width: 100%;" /> LLM includes support out-of-the-box for the GPT-5.6 model family, and the new default model used with <code>llm "prompt"</code> is now the inexpensive but capable GPT-5.6 Luna. LLM calls can now use server-side tools from various providers. OpenAI provide <a href="https://llm.datasette.io/en/stable/openai-models.html#code-interpreter">a code execution environment</a> as a server-side tool; LLM can now run prompts that benefit from that like so: <div class="highlight highlight-source-shell"><pre>llm --tool CodeInterpreter 'Show current python and SQLite versions'</pre></div> OpenAI also gets a <a href="https://llm.datasette.io/en/stable/openai-models.html#web-search">WebSearch</a> tool. The <a href="https://github.com/simonw/llm-anthropic">llm-anthropic</a> plugin adds <a href="https://github.com/simonw/llm-anthropic/blob/0.26/README.md#web-search">WebSearch</a>, <a href="https://github.com/simonw/llm-anthropic/blob/0.26/README.md#web-fetch">WebFetch</a>, <a href="https://github.com/simonw/llm-anthropic/blob/0.26/README.md#code-execution">CodeExecution</a>, and <a href="https://github.com/simonw/llm-anthropic/blob/0.26/README.md#mcp-connector">AnthropicMCP</a>, which looks like this: <div class="highlight highlight-source-shell"><pre>llm -m claude-sonnet-5 -T 'AnthropicMCP("https://datasette.simonwillison.net/-/mcp")' \ 'how many rows in the blog_blogmark table?'</pre></div> That causes Anthropic to execute MCP calls against my new <a href="https://simonwillison.net/2026/Jul/31/stateless-mcp/#datasette-mcp">datasette-mcp</a> plugin as part of a single request/response interaction with their API. The new llm openai endpoint command provides a tool for <a href="https://llm.datasette.io/en/stable/other-models.html#run-against-an-endpoint-without-configuring-it">executing prompts against any OpenAI compatible endpoint</a> as a one-liner. These aren't logged, which makes this a handy tool for running one-off prompts against anything that speaks the lingua franca of the LLM API world. Here