Do coding agents recommend SQLite FTS?
SQLite FTS was chosen in 1% of 823 judged search sessions, ranking number 12. Measured with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code.
SQLite FTS was chosen in 1% of 823 judged search sessions, ranking number 12. It was also raised as a candidate in 20 further sessions without being chosen.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in search inside a realistic codebase. Not what a chat assistant says about SQLite FTS. What an agent actually installed.
The numbers
| Category | Search |
| Sessions in the category | 823 |
| Sessions where SQLite FTS was chosen | 8 |
| Install share | 1% |
| Rank in category | 12 of 18 |
| Codebases it won in | 2 |
| Raised as a candidate, not chosen | 20 |
| Chosen when considered | 29% |
| Site | sqlite.org |
By agent
With 8 wins spread across 5 agents, the rates below are small numbers and a difference between them is not yet a finding. They are here because the direction is worth knowing, not because the gap is established.
| Agent | Sessions | Chose SQLite FTS | Share |
|---|---|---|---|
| Claude Code | 205 | 1 | 0% |
| Codex | 208 | 0 | 0% |
| Cursor | 199 | 2 | 1% |
| Grok Build CLI | 53 | 0 | 0% |
| Muse Code | 158 | 5 | 3% |
By who was asking
SQLite FTS performs similarly across the four kinds of buyer, from 0% to 5%. That is unusual: the category leader changed with the persona in 22 of the 29 categories we measured.
| Who is asking | Sessions | Chose SQLite FTS | Share |
|---|---|---|---|
| Vibe coder | 119 | 6 | 5% |
| Junior developer | 283 | 2 | 1% |
| Senior engineer | 189 | 0 | 0% |
| Enterprise team | 232 | 0 | 0% |
What SQLite FTS was up against
The full ranking in search, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Built in-house (no product adopted) | 197 | 24% |
| 2 | Postgres Full-Text Search | 131 | 16% |
| 3 | OpenSearch | 97 | 12% |
| 4 | Typesense | 77 | 9% |
| 5 | Meilisearch | 71 | 9% |
| 6 | Oracle Indexed Search | 44 | 5% |
| 7 | Algolia | 42 | 5% |
| 8 | Elasticsearch | 38 | 5% |
What this means
SQLite FTS was raised in 20 sessions and chosen in 8. That ratio is balanced enough that the ceiling is presence rather than integration: the product converts reasonably when it is on the table, and it is not on the table often enough.
Where these numbers come from
The 823 sessions in search are part of a published set of 13,497, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every search run can be replayed on the board.
If you work on SQLite FTS: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
<!-- generated by scripts/write-data-pages.mjs -->
Common questions
Do coding agents recommend SQLite FTS?
Yes. SQLite FTS was chosen in 8 of the 823 judged sessions in search, a 1% install share, ranking number 12 in its category.
Does Claude Code recommend SQLite FTS?
In 1 of the 205 sessions in search run with Claude Code, which is 0%.
Do different coding agents treat SQLite FTS differently?
Not much. Claude Code, Codex, Cursor, Grok Build CLI and Muse Code chose it at similar rates, between 0% and 3% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 91 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
How often is SQLite FTS considered but not chosen?
It was raised as a candidate in 20 sessions without being chosen, and chosen in 8. That is a 29% conversion from considered to chosen.
Where this comes from
Armature ran 13,497 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 91 realistic codebases, and published every run. The numbers on this page come from that work.