Datasette Agent
Datasette Agent has been officially released as an AI assistant for the Datasette platform. This tool allows users to interact with their stored data through a conversational interface and supports various plugins for enhanced functionality. The project aims to integrate large language models with Datasette, opening up new possibilities for data interaction and analysis.
- ▪Datasette Agent provides a conversational interface for querying data stored in Datasette.
- ▪The tool is extensible with plugins, including features for generating charts and images.
- ▪A live demo showcases the capabilities of Datasette Agent using example databases.
Simon Willison files mainly under blogs. We currently carry 54 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Simon Willison |
| Canonical URL | https://simonwillison.net/2026/May/21/datasette-agent/#atom-everything |
| Publication time | 2026-05-21T19:52:19+00:00 |
| Retrieval time | 2026-05-21T20:01:35.710Z |
| Last seen | 2026-05-21T20:01:35.710Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Datasette Agent 21st May 2026 We just announced the first release of Datasette Agent, a new extensible AI assistant for Datasette. I’ve been working on my LLM Python library for just over three years now, and Datasette Agent represents the moment that LLM and Datasette finally come together. I’m really excited about it! Datasette Agent provides a conversational interface for asking questions of the data you have stored in Datasette. Add the datasette-agent-charts plugin and it can generate charts of your data as well.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Simon Willison.
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