IntentQL: a semantic compiler for natural-language Postgres analytics

Alexander Abakah — Wed, 10 Jun 2026 05:30:44 +0000

I just released IntentQL 0.2.4 - an open-source semantic compiler for natural-language analytics over Postgres.

The problem

Most text-to-SQL systems let the model generate executable SQL directly. When something breaks, it's hard to tell whether the failure was language understanding, schema selection, joins, or safety.

The approach

IntentQL splits the problem:

Model -> lightweight semantic hints (dataset, filters, metrics, etc.)
Compiler -> resolves hints against an allowlisted schema.yaml, builds a typed QueryPlan, validates it, compiles parameterized SQL

The model interprets language. The compiler owns execution.

Filter values become bind parameters - not string-concatenated SQL.

Quick start

pip install intentql
intentql init --db "postgresql://user:pass@host/db"

from sqlalchemy import create_engine
from intentql.agent import QueryAgent

agent = QueryAgent(
    engine=create_engine("postgresql+psycopg2://user:pass@host/db"),
    schema_path="config/schema.yaml",
    llm="mistral",
)

result = agent.ask("Which customers placed the most orders last year?")
print(result["rows"])
print(result["sql"])

Optional extras:

pip install "intentql[memory]"
pip install "intentql[openai]"

What you get

Schema allowlisting (schema.yaml)
Typed intermediate representation (QueryPlan)
Deterministic validation and compilation
Parameterized Postgres SQL via SQLAlchemy
Mistral, Ollama, and OpenAI-compatible providers

DEV Community: Alexander Abakah

IntentQL: a semantic compiler for natural-language Postgres analytics

The problem

The approach

Quick start

What you get

Links