# llm 631 Published articles for llm 631. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## llm 0.35 DevFeed: [llm 0.35]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-07T23:54:54Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [Large Language Model](), [gpt-6-astra](), [OpenAI]() Tags: [gpt-6-astra](), [gpt-6-astra-9](), [llm](), [llm-631](), [openai](), [openai-463](), [release]() ### AI overview llm 0.35 is a release that adds the OpenAI model gpt-6-astra for GPT-6 Astra. ### Source excerpt Release: llm 0.35 New OpenAI model: gpt-6-astra for GPT-6 Astra. Tags: openai, llm, gpt-6-astra ## New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging DevFeed: [New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-04T23:58:24Z Content type: release Language: en Sources: [Simon Willison]() Topics: [Tool](), [Command-line interface](), [API](), [Large Language Model](), [SQLite](), [Logging](), [OpenAI](), [Claude](), [Model Context Protocol](), [gemma4]() Tags: [ai](), [ai-2-235](), [anthropic](), [anthropic-336](), [cli](), [gemma](), [generative-ai](), [generative-ai-1-981](), [llm](), [llm-631](), [llm-reasoning](), [llm-reasoning-103](), [llm-tool-use](), [llm-tool-use-75](), [llms](), [llms-1-947](), [logging](), [mcp](), [model-context-protocol](), [model-context-protocol-35](), [openai](), [openai-463](), [projects](), [projects-553](), [release](), [releases](), [releases-31](), [responses](), [server](), [sqlite]() ### AI overview LLM 0.32 adds visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, support for the GPT-5.6 model family, and features enabled by the OpenAI Responses API. The release also updates the llm-anthropic plugin and adds Python API changes. ### Source excerpt I released LLM 0.32 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 a new version of the llm-anthropic plugin with substantial updates of its own. Headline features for LLM CLI users 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 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 tool. The llm-anthropic plugin adds WebSearch, WebFetch, CodeExecution, and AnthropicMCP, 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 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 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 API, via uvx (no LLM installation required) and mixing in the llm-tools-q ## Stateless MCP has recaptured my interest (and inspired mcp-explorer and datasette-mcp) DevFeed: [Stateless MCP has recaptured my interest (and inspired mcp-explorer and datasette-mcp)]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-07-31T23:13:22Z Content type: article Language: en Sources: [Simon Willison]() Topics: [Model Context Protocol](), [Large Language Model](), [anthropic](), [HTTP](), [Command-line interface](), [JSON](), [Python](), [web applications]() Tags: [2026](), [agent](), [ai](), [ai-2-235](), [anthropic](), [anthropic-336](), [cli](), [codex](), [context](), [curl](), [datasette](), [datasette-1-542](), [generative-ai](), [generative-ai-1-981](), [http](), [json](), [llm](), [llm-631](), [llms](), [llms-1-947](), [mcp](), [mermaid](), [mermaid-5](), [model-context-protocol](), [model-context-protocol-35](), [projects](), [projects-553](), [shell](), [stateless](), [terminal]() ### AI overview This commentary examines the 2026-07-28 stateless Model Context Protocol specification. It argues that stateless MCP simplifies client and server implementation, improves auditability and control compared with shell-based agent access, and better supports scalable web applications by removing session state and session-affinity requirements. The article also introduces the mcp-explorer CLI tool and mentions datasette-mcp. ### Source excerpt Tuesday was Stateless MCP day - the rollout of MCP 2.0, or the 2026-07-28 Model Context Protocol specification to use the more formal but less memorable name. This is the most significant change to the MCP spec since it first launched, and has also served to reignite my personal interest in the protocol. For background: MCP is the Model Context Protocol, which describes a standard way to expose new tools to LLM-powered agent frameworks. It was introduced by Anthropic back in November 2024, had a huge spike of interest through much of 2025, and then became somewhat eclipsed by Skills (another Anthropic invention) when it became apparent that an agent harness with access to a terminal and curl could do most of what MCP did in a more flexible way. I wrote about that in my review of 2025. I'm coming back around to MCP now. Giving an agent a shell environment with the ability to access the internet is fraught with risk, and requires a strong model that is capable of effectively driving such an environment. MCP tools are easier to audit and control, and simple enough that smaller models that run on a laptop can still drive them reasonably well. The new stateless MCP specification also greatly decreases the complexity of implementing both clients and servers for the protocol. I built three of those this week! What's easier with stateless MCP The best demonstration of the difference between stateful and stateless MCP is in this May 21st blog post that introduced the RC for the new specification. It included a clear before-and-after example. The older stateful MCP (I'm going to call it "legacy MCP") required two HTTP requests - the first to initialize a session and obtain a Mcp-Session-Id, and the second to actually call the tool: POST /mcp HTTP/1.1 Content-Type: application/json { "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2025-11-25", "capabilities": { }, "clientInfo": { "name": "my-app", "version": "1.0" } } } POST /mcp HTTP/1.1 Mcp