Simon Willison’s Weblog

See Publishing WASM wheels to PyPI for use with Pyodide for details.

It would be neat if arbitrary SQL queries in Datasette could be rendered with additional information based on which columns from which tables were included in the results.

To build that, we would need to be able to look at a SQL query like select users.name, orders.total from users join orders on orders.user_id = users.id and programmatically identify the table.column for each result - navigating not just joins but also more complex syntax like CTEs.

I decided to set Claude Code (Opus 4.8, since Fable is currently banned by the US government) on the problem. It found several promising solutions - one using apsw, another that uses ctypes to access the SQLite sqlite3_column_table_name() C function (which is not otherwise exposed to Python), and one using clever interrogation of the output of EXPLAIN.

This alpha is a significant step on the road to a stable 1.0, finally extending the ?_extra= pattern I introduced in Datasette 1.0a3 to cover queries and rows in addition to tables. That pattern is also now documented!

I wrote a whole lot more about the new release on the Datasette project blog: Datasette 1.0a33 with JSON extras in the API.

Because API explorer tools are almost free to build now I had Claude Fable 5 in Claude Code (for the plan) and GPT-5.5 xhigh in Codex Desktop (for the implementation) build me this custom extras API explorer to help demonstrate the feature:

I built this utility library to support an asyncio dependency injection pattern a few years ago. I was using it with Datasette and Claude Fable 5 spotted some bugs in the dependency which it then fixed for me. It's a very proactive model!

Highlights from the release notes:

• Tools can now ask the user questions mid-execution. Tools that declare a context parameter receive a ToolContext object, and await context.ask_user(...) can ask a yes/no, multiple-choice (options=[...]) or free-text (free_text=True) question. While a question is unanswered the agent turn suspends: the question renders as a form in the chat UI and persists to the internal database, so suspended conversations survive a server restart. Once answered, the tool re-executes from the top with stored answers replayed, so call ask_user() before performing side effects. #20
• New built-in save_query tool: the agent can save SQL it has written as a Datasette stored query. Saving always requires human approval - the agent shows the full SQL plus the proposed name, database and visibility, and nothing is stored until you click Yes. #20

The ask_user() feature was enabled by the new LLM alpha I built yesterday with the help of Claude Fable 5.

Almost entirely written by the new Claude Fable 5, see my write-up for more details.

I'm planning several plugins for Datasette Agent which can make edits to existing pieces of text - things like collaborative Markdown editing, updating large SQL queries, and editing SVG files.

Agentic editing of text is a little tricky to get right. My favorite published design for this is for the Claude text editor, which implements the following tools:

• view - view sections of a file, with line numbers added to every line.
• str_replace - find an exact old_str and replace it with new_str - fail if the original string is not unique
• insert - insert the specified text after the specified line number

Rather than recreate these patterns for every plugin that needs them I decided to create this base plugin, datasette-agent-edit, which implements the core tools in a way that allows them to be adapted for other plugins.

I added a CLI to micropython-wasm (issue #7), inspired by the first draft of the blog entry when I realized it would be a great way to illustrate the Try it yourself section.

I want Datasette Agent to be able to generate and execute Python code safely. This alpha is looking promising so far. GPT-5.5 has so far failed to break out of the sandbox!

Fixes for some limitations that emerged while I was trying to use this to build datasette-agent-micropython.

I really like how you can paste a large volume of text into claude.ai (or the Claude desktop/mobile apps) and it will detect it as a large paste and turn it into a file attachment instead.

I decided to have Codex desktop build me a version of that as a prototype.

You can also open files directly - including images which will be shown as thumbnails - or drag files onto the textarea.

My latest sandboxing experiment: This alpha package bundles a lightly customized WASM build of MicroPython with a wrapper to execute code in it via wasmtime.

A minor bugfix release. Fixes a bug with INSERT ... RETURNING queries via the new /db/-/execute-write endpoint and a bunch of base_url issues which showed up when I was experimenting with Service Workers yesterday.

Datasette Lite is my version of Datasette that runs entirely in the browser using Pyodide in WebAssembly.

When I first built it four years ago I used Web Workers and code that intercepts navigation operations and fetches the generated HTML by running the Python app.

This worked, but had the disadvantage that any JavaScript in <script> tags would not be executed - breaking some Datasette functionality and a whole lot of Datasette plugins.

This morning I set Claude Opus 4.8 the task (in Claude Code for web) of figuring out how to run Python ASGI apps in Pyodide using Service Workers instead, and it seems to work! Here's a basic ASGI FastCGI demo and here's a demo that runs Datasette 1.0a31.

I'm still getting my head around exactly how it works, but once I've done that I plan to upgrade Datasette Lite itself.

Another significant alpha release, with two new headline features.

Datasette now offers users with the necessary permissions the ability to both execute write queries against their database and to save stored queries (renamed from "canned queries") both privately and for use by other members of their Datasette instance.

There's more detail in SQL write queries and stored queries in Datasette 1.0a31 on the Datasette blog, which now has three posts introducing new features since the blog launched two weeks ago.

Here's an animated demo from the blog post showing how the new execute query interface lets people get started with templated insert/update/delete queries from tables they have permission to edit:

• New model: Claude Opus 4.8 (claude-opus-4.8).
• New -o fast 1 option for fast mode, for organizations with that feature enabled on their account.
• Default max_tokens for each model now defaults to that model's maximum output rather than 8,192. #72

See also my notes on Opus 4.8 - I used this new release of llm-anthropic to generate the pelicans.