Release: alchemy-utils 0.1a0
Simon Willison’s Weblog
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12th August 2026
Release
alchemy-utils 0.1a0 — Cross-database sqlite-utils built on SQLAlchemy
I've long pondered what a database agnostic version of my sqlite-utils Python library and CLI utility might look like. This morning (literally a shower project) I tasked Codex and GPT-5.6 Sol Ultra with building a prototype:
Do a research spike to see what it would take to build a library with the same core API as SQLite-utils - in particular the insert and upsert and insert_all and upsert_all and create and update methods, and the table introspection stuff - but backed by SQLalchemy so it works for multiple database engines
Test against PostgreSQL and SQLite and duckdb
Use ~/dev/sqlite-utils for reference
Create a git repo for this and commit and early and often - use uv init to start the project - use red/green TDD and pytest, see ~/dev/django-sql-dashboard for one idea as to how the PostgreSQL tests could work
It took very few follow-up prompts to produce this project in a state good enough to release as an alpha.
Here's a one-liner I can use to list the rows in a table in my local PostgreSQL copy of my blog's database:
uvx --with 'alchemy-utils[postgresql]' alchemy-utils rows 'postgresql+psycopg://simon@localhost:5432/simonwillisonblog' redirects_redirect
The output from that starts like this:
[ { "id": 2328, "domain": "simonwillison.net", "path": "2020/May/21/apple-photos-sqlite/", "target": "/2020/May/21/dogsheep-photos/", "created": "2020-05-21T13:03:46.591692-07:00" }, { "id": 3, "domain": "feeds.simonwillison.net", "path": "swn-links", "target": "https://simonwillison.net/atom/links/", "created": "2017-10-01T14:12:54.820729-07:00" }
Or if you'd like a DuckDB database with every tree in San Francisco, schema created automatically to match the file:
curl 'https://raw.githubusercontent.com/simonw/sf-tree-history/refs/heads/main/Street_Tree_List.csv' | uvx --with 'alchemy-utils[duckdb]' alchemy-utils insert 'duckdb:////tmp/trees.db' trees - --csv
(That one took nearly an hour the first time I ran it, so I had Codex optimize it and got it down to around 35 seconds.)
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