SQLSpec is a SQL execution layer for Python. You write the SQL -- as strings, through a builder API, or loaded from files. SQLSpec handles connections, parameter binding, and dialect translation. It maps results back to typed Python objects. It uses sqlglot under the hood. Queries are parsed, validated, and optimized before they hit the database.

pip install sqlspec

from dataclasses import dataclass

from sqlspec import SQLSpec
from sqlspec.adapters.sqlite import SqliteConfig


@dataclass
class Greeting:
    message: str

spec = SQLSpec()
db = spec.add_config(SqliteConfig(connection_config={"database": ":memory:"}))

with spec.provide_session(db) as session:
    greeting = session.select_one(
        "SELECT 'Hello, SQLSpec!' AS message",
        schema_type=Greeting,
    )
    print(greeting.message)  # Output: Hello, SQLSpec!

Write SQL, define a schema, get typed objects back. The getting started guide covers adapter installation and the query builder.

• Session lifecycle -- sync and async sessions with pooling where the adapter supports it
• Parameter binding and dialect translation -- powered by sqlglot, with a fluent query builder and .sql file loader
• Result mapping -- map rows to Pydantic, msgspec, attrs, or dataclass models, or export to Arrow tables for pandas and Polars
• Storage layer -- read and write Arrow tables to local files, fsspec, or object stores
• Framework integrations -- Litestar plugin with DI, Starlette/FastAPI/Sanic middleware, Flask extension
• Google ADK -- SQLSpec-backed session, event, memory, and artifact services
• Observability -- OpenTelemetry and Prometheus instrumentation, structured logging with correlation IDs
• Event channels -- LISTEN/NOTIFY, Oracle AQ/TxEventQ, and durable table-backed queues with polling fallback
• Migrations -- native schema migration CLI backed by SQLSpec's SQL file loader

• Getting Started -- installation, adapter selection, first steps
• Usage Guides -- adapters, configuration, SQL file loader, and more
• Examples Gallery -- working code for common patterns
• API Reference -- full API docs
• CLI Reference -- migration and management commands

Want to try it without installing anything? The interactive playground runs SQLSpec in your browser with a sandboxed Python runtime.

• PostgreSQL + Vertex AI Demo -- Vector search with pgvector and real-time chat using Litestar and Google ADK. Shows connection pooling, migrations, type-safe result mapping, vector embeddings, and response caching.
• Oracle + Vertex AI Demo -- Oracle 23ai vector search with semantic similarity using HNSW indexes. Demonstrates NumPy array conversion, large object handling, and real-time performance metrics.

Contributions are welcome -- whether that's bug reports, new adapter ideas, or pull requests. Take a look at the contributor guide to get started.

MIT