VibeSQL

SQL:1999 compliant database in Rust, 100% AI-generated

Live Demo | CLI Guide | Python Bindings | Conformance Report

Highlights

• 100% SQL:1999 Core compliance - 739/739 sqltest tests passing
• 100% SQLLogicTest conformance - 624 files (~5.9M tests)
• Full-featured CLI with PostgreSQL-compatible commands
• Python bindings with DB-API 2.0 interface
• WebAssembly - runs in the browser
• 215,000+ lines of Rust across 11 crates

Built entirely by AI agents using Claude Code and Loom.

Quick Start

# Clone and build
git clone --recurse-submodules https://github.com/rjwalters/vibesql.git
cd vibesql
cargo build --release

# Run the CLI
cargo run --release --bin vibesql

# Or try the web demo
cd web-demo && npm install && npm run dev

CLI Example

$ cargo run --release --bin vibesql
vibesql> CREATE TABLE users (id INTEGER, name VARCHAR(50));
vibesql> INSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');
vibesql> SELECT * FROM users;
+----+-------+
| id | name  |
+----+-------+
| 1  | Alice |
| 2  | Bob   |
+----+-------+
vibesql> \q

See CLI Guide for meta-commands, output formats, and import/export.

Python

pip install maturin
maturin develop

import vibesql

db = vibesql.connect()
cursor = db.cursor()
cursor.execute("CREATE TABLE t (id INTEGER, name VARCHAR(50))")
cursor.execute("INSERT INTO t VALUES (1, 'Hello')")
cursor.execute("SELECT * FROM t")
print(cursor.fetchall())  # [(1, 'Hello')]

See Python Bindings Guide for full API reference.

Features

SQL Support

• Queries: SELECT, JOINs (INNER/LEFT/RIGHT/FULL/CROSS), subqueries, CTEs, UNION/INTERSECT/EXCEPT
• DML: INSERT, UPDATE, DELETE, TRUNCATE
• DDL: CREATE/ALTER/DROP TABLE, views, indexes, schemas
• Aggregates: COUNT, SUM, AVG, MIN, MAX with GROUP BY/HAVING
• Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
• Transactions: BEGIN, COMMIT, ROLLBACK, savepoints
• Security: GRANT/REVOKE with full privilege enforcement

Advanced Features

• Views with OR REPLACE and column lists
• Stored procedures and functions (IN/OUT/INOUT parameters)
• Full-text search (MATCH AGAINST)
• Spatial functions (ST_* library)
• Triggers (BEFORE/AFTER)

Performance

• Columnar execution with SIMD acceleration
• Cost-based join reordering
• Hash joins for equi-joins
• Predicate pushdown
• Expression caching

Development

# Full build, test, and benchmark (runs in background)
make all            # Starts in background, shows monitoring instructions
make status         # Check progress
make logs           # Follow full output

# Individual targets
make build          # Build all crates
make test           # Run all tests (unit + integration + sqllogictest)
make benchmark      # Run TPC-H/TPC-C/TPC-DS/Sysbench benchmarks
make all-fg         # Run everything in foreground (blocking)
make help           # Show all targets

Documentation

Project Background

This project originated from a challenge about AI capabilities: implement a NIST-compatible SQL database from scratch. Core SQL:1999 compliance was achieved in under 2 weeks (Oct 25 - Nov 1, 2025).

Inspired by posix4e/nistmemsql.

License

MIT OR Apache-2.0. See LICENSE-MIT and LICENSE-APACHE.

Contributing

See CLAUDE.md for development workflow with Loom AI orchestration.

Try the Live Demo →