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

LLM 0.32, the most significant release since the project's launch, adds support for visible reasoning traces, server-side tools, and redesigned content-addressable SQLite logs, according to developer Simon Willison. The new version includes out-of-the-box support for the GPT-5.6 model family, with GPT-5.6 Luna as the new default model, and introduces a new `llm openai endpoint` command for one-off prompts against any OpenAI-compatible endpoint. Updated plugins `llm-anthropic`, `llm-gemini`, and `llm-openrouter` also bring substantial enhancements, including Anthropic's WebSearch, WebFetch, CodeExecution, and AnthropicMCP tools.

I released LLM 0.32 https://llm.datasette.io/en/stable/changelog.html v0-32 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 new versions of the llm-anthropic , llm-gemini , and llm-openrouter plugins, each with substantial updates of their own. 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 -R/--hide-reasoning to turn this off. LLM includes support out-of-the-box for the GPT-5.6 model family , and the new default model used with llm "prompt" is now the inexpensive but capable GPT-5.6 Luna . LLM calls can now use server-side tools from various providers. OpenAI provide a code execution environment https://llm.datasette.io/en/stable/openai-models.html code-interpreter as a server-side tool; LLM can now run prompts that benefit from that like so: llm --tool CodeInterpreter 'Show current python and SQLite versions' OpenAI also gets a WebSearch https://llm.datasette.io/en/stable/openai-models.html web-search tool. The llm-anthropic https://github.com/simonw/llm-anthropic plugin adds WebSearch https://github.com/simonw/llm-anthropic/blob/0.26/README.md web-search , WebFetch https://github.com/simonw/llm-anthropic/blob/0.26/README.md web-fetch , CodeExecution https://github.com/simonw/llm-anthropic/blob/0.26/README.md code-execution , and AnthropicMCP https://github.com/simonw/llm-anthropic/blob/0.26/README.md mcp-connector , which looks like this: llm -m claude-sonnet-5 -T 'AnthropicMCP "https://datasette.simonwillison.net/-/mcp" ' \ 'how many rows in the blog blogmark table?' That causes Anthropic to execute MCP calls against my new datasette-mcp https://simonwillison.net/2026/Jul/31/stateless-mcp/ datasette-mcp plugin as part of a single request/response interaction with their API. The new llm openai endpoint command provides a tool for executing prompts against any OpenAI compatible endpoint https://llm.datasette.io/en/stable/other-models.html run-against-an-endpoint-without-configuring-it 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's how I use that to run prompts against Gemma 4 12B running in my localhost LM Studio https://lmstudio.ai API, via uvx no LLM installation required and mixing in the llm-tools-quickjs https://github.com/simonw/llm-tools-quickjs tool plugin for good measure: uvx --with llm-tools-quickjs \ llm openai endpoint http://localhost:1234/v1 -m google/gemma-4-12b \ -T QuickJS 'Use QuickJS to multiply 3434 2434' --td LLM's Python API previously required you to create a conversation and then send messages to it one at a time. This was an abstraction over the true nature of LLMs, where each request carries a complete history of the messages that came before it. That abstraction started to get in the way for some more advanced cases, so the new release introduces a model.prompt messages= parameter that can be used like this: python import llm from llm import user, assistant, system model = llm.get model "gpt-5.6-luna" response = model.prompt messages= system "You are a helpful pirate." , user "What is the capital of France?" , assistant "Paris, matey." , user "And Germany?" , print response.text LLM previously returned an iterable sequence of strings from each prompt. This worked great when models returned a string response, but failed to predict the weird shape that models would evolve towards. Today many models return a mix of reasoning text, output strings, tool calls, and even image attachments. With LLM 0.32 you can do this instead https://llm.datasette.io/en/stable/python-api.html structured-messages-and-streaming-events : for event in model.prompt "Explain cats" .stream events : if event.type == "reasoning": print f" thinking {event.chunk}", end="", flush=True elif event.type == "text": print event.chunk, end="", flush=True else: print f"Other event: {event}" Combine these features and we can finally provide a robust implementation of the semi-standard OpenAI chat completions API, which I've now released as the llm-chat-completions-server https://github.com/simonw/llm-chat-completions-server plugin: llm install llm-chat-completions-server llm chat-completions-server --port 9000 Server is now running on http://127.0.0.1:9000/v1 Now you can run prompts against LLM via that server, using the new llm openai endpoint command llm openai endpoint http://127.0.0.1:9000/v1 'hello' -m gpt-5.4-mini The bigger challenge with that kind of API concerns logging. If we're going to support the pattern where the message sequence is appended to on every request, ideally we can avoid logging all of that duplicate JSON for every turn. The solution is the new content-addressable message store https://llm.datasette.io/en/stable/logging.html the-message-store , modeled after Git. You can see the new schema for that in the documentation https://llm.datasette.io/en/stable/logging.html sql-schema , but the llm logs and llm logs --json commands have both been upgraded to convert that format back into something that's easy to consume. There is a whole lot more in this release. The 0.32 release notes https://llm.datasette.io/en/stable/changelog.html v0-32 are pretty comprehensive, and the notes for 0.32rc2 https://llm.datasette.io/en/stable/changelog.html rc2-2026-07-30 , 0.32rc https://llm.datasette.io/en/stable/changelog.html rc1-2026-07-30 , 0.32a3 https://llm.datasette.io/en/stable/changelog.html a3-2026-06-09 , 0.32a2 https://llm.datasette.io/en/stable/changelog.html a2-2026-05-12 , and 0.32a0 https://llm.datasette.io/en/stable/changelog.html a0-2026-04-28 should fill in any gaps. Existing LLM plugins should all continue to work, but plugins that provide extra models will need to be upgraded to 0.32 in order to participate fully in the new streaming events system. There's a guide to implementing plugins with Structured messages and streaming events https://llm.datasette.io/en/stable/plugins/advanced-model-plugins.html structured-messages-and-streaming-events in the documentation. I've updated some of my own plugins: WebSearch , WebFetch , CodeExecution , and AnthropicMCP server-side tools.Quite a few of the lower-level tools changes in this release were driven by the needs of Datasette Agent https://agent.datasette.io/ . When I started work on LLM, the term "agent" had such a vague definition that I refused to use it. In September 2025 https://simonwillison.net/2025/Sep/18/agents/ I came around to the idea that " An LLM agent runs tools in a loop to achieve a goal " is well established enough now that I could stop avoiding the term entirely. Tool chains can now pause for human approval https://llm.datasette.io/en/stable/python-api.html python-api-tools-pause and resume from a stored message history https://llm.datasette.io/en/stable/python-api.html python-api-tools-resume - both needed by Datasette Agent. Looking at LLM today it's beginning to look very agent-shaped to me. There's something neat about having a CLI utility that can mix and match different tools from different sources with different models all as a one-liner, and that includes a Python library powerful enough to build systems like Datasette Agent https://agent.datasette.io/ and llm-coding-agent https://github.com/simonw/llm-coding-agent . Maybe the next version of LLM will bake the concept of an "agent" into the core library. I'm still trying to figure out what that would look like. 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