sqlsure
AI writes your SQL. sqlsure makes sure it's right.
A query can be perfectly valid, run without error, and return a number
that's silently wrong — revenue double-counted by a join, an average
summed, a patient identifier exposed. Databases don't catch this.
Linters don't catch this. LLMs reviewing their own SQL don't catch this.
sqlsure does — deterministically, in 0.1 ms, before the query runs.
Proof, not promises: we ran sqlsure over the gold answers of the two
benchmarks every text-to-SQL model is graded on. 2,568 expert-written
queries, 45 flags, zero false alarms — including a BIRD dev gold answer
that is provably wrong by 8× from the exact
bug class sqlsure targets, and a schema defect
now filed upstream.
How it works
sqlsure judges SQL against facts your team already declared — dbt unique
tests become grain, relationships tests become join cardinality, one-line
meta tags mark what's safe to sum. No new language to learn, no model to
maintain by hand. Rules are dictionary lookups, not LLM calls: same input,
same verdict, every time, offline.
Every rejection carries a machine-actionable fix, so AI agents
self-repair: draft → check → fix → check → execute. In our benchmark,
applying the fix verbatim produced a passing query 10/10 times.
Quick start
Or clone and run the 30-second demo:
Three doors, one engine
- CI gate — blocks the merge when a PR double-counts:
- MCP server — your AI agent must pass inspection before executing:
See docs/MCP.md for tool reference and agent-loop patterns.
- Library — embed check() inside any text-to-SQL product or agent
framework. A drop-in SemanticGate wraps
Vanna/WrenAI-style generators; a
semantic eval metric scores NL2SQL output
where execution-accuracy is blind.
The rules (v0.1)
When sqlsure can't verify something, it says "can't verify" — never "looks
fine." Honest uncertainty is a feature.
Trust properties
Deterministic — same SQL + same rulebook = same verdict, always;
rules are dictionary lookups, auditable line by line
Offline — zero network calls; your SQL never leaves your machine
No data access — parses query text; never connects to a database
No telemetry — nothing collected, ever (SECURITY.md)
Supply chain — releases ship exclusively via PyPI Trusted Publishing
(OIDC) from tagged commits with public CI runs; two runtime deps
Where the rulebook comes from
dbt (works today): manifest.json or schema.yml — the tests teams
already wrote become enforceable semantics, zero config
dbt (works today): manifest.json or schema.yml — the tests teams
already wrote become enforceable semantics, zero config
Plain PK/FK declarations (works today — powered the benchmark audits)
Plain PK/FK declarations (works today — powered the benchmark audits)
The live database itself (works today): no semantic layer at all?
sqlsure.introspect builds the rulebook from the catalog — SQLite
PRAGMAs or information_schema PK/FK (postgres/mysql). Introspecting
BIRD's own database files recovered 2 foreign keys missing from the
benchmark's published schema
(bird-bench/mini_dev#37)
from sqlsure.introspect import model_from_sqlite
model = model_from_sqlite("app.db") # PK -> grain, FK -> join edges
The live database itself (works today): no semantic layer at all?
sqlsure.introspect builds the rulebook from the catalog — SQLite
PRAGMAs or information_schema PK/FK (postgres/mysql). Introspecting
BIRD's own database files recovered 2 foreign keys missing from the
benchmark's published schema
(bird-bench/mini_dev#37)
Hand-written JSON — model.example.json
Hand-written JSON — model.example.json
OSI and WrenAI MDL (working loaders in
integrations/): OSI
demonstrated on the spec's published examples;
WrenAI MDL demonstrated on WrenAI's own
shipped example manifest — primaryKey → grain, relationship
joinType + condition → join edges, cube measures → additivity
OSI and WrenAI MDL (working loaders in
integrations/): OSI
demonstrated on the spec's published examples;
WrenAI MDL demonstrated on WrenAI's own
shipped example manifest — primaryKey → grain, relationship
joinType + condition → join edges, cube measures → additivity
Cube, Snowflake Semantic Views — adapters on the roadmap; the
engine only ever sees one SemanticModel
Cube, Snowflake Semantic Views — adapters on the roadmap; the
engine only ever sees one SemanticModel
Validated on
16/16 rule tests, 100% recall / 0% false positives on the paired
benchmark (docs/METRICS.md)
Real production repos (Mattermost's warehouse, Fivetran packages,
dbt's jaffle shop) — docs/TEST-REPORTS.md
Spider + BIRD gold queries — the zero-noise external audit above
Learn more
docs/EVIDENCE.md — what it does for you, every
claim linked to a rerunnable measurement
docs/ARCHITECTURE.md — how it physically works,
ELI5 → god level, with real intermediate outputs
docs/FOR-DUMMIES.md — every concept from zero
docs/INTEGRATIONS.md — GitHub Action, pre-commit,
MCP, Snowflake UDF / Cortex Agent tool, query-history audit
docs/MCP.md — MCP server documentation
CONTRIBUTING.md — adding rules and loaders
Apache-2.0 · sqlsure.ai
mcp-name: io.github.sqlsure/sqlsure