> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wazoo.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# TextQL

> Worlds vs TextQL: RDF fact ledger vs enterprise data warehouse context engine.

## At a glance

|               | Worlds                                                | [TextQL](https://textql.com)                          |
| :------------ | :---------------------------------------------------- | :---------------------------------------------------- |
| Primary role  | Authoritative RDF fact ledger & SPARQL context engine | AI data analyst & warehouse semantic layer platform   |
| Data model    | RDF triples in an append-only graph ledger            | SQL warehouse tables, semantic metrics, & catalog     |
| Retrieval     | SPARQL graph queries, full-text, & vector fusion      | SQL generation, semantic search over warehouse schema |
| Target domain | Persistent agent memory & verifiable domain graphs    | Business intelligence, SQL analytics, & warehouse RAG |
| Deployment    | Edge-ready adapters (LibSQL, Postgres) or Cloud       | Managed Cloud SaaS / Enterprise Warehouse integration |

## Overlap and shared capabilities

Both Worlds and [TextQL](https://textql.com) make structured enterprise data
accessible to AI agents without relying on ungrounded LLM guesses. Both
platforms emphasize schema accuracy, metadata preservation, and verifiable data
retrieval over pure vector similarity.

The core distinction is target substrate and query language:

* TextQL focuses on corporate data warehouses (Snowflake, BigQuery, Databricks).
  It acts as an AI data analyst, translating natural language into SQL queries
  against relational tables and semantic business metrics.
* Worlds is an RDF knowledge graph context engine. It manages persistent triple
  assertions, subject-predicate-object graphs, and SPARQL queries with immutable
  quad provenance.

TextQL brings natural language analytics to existing SQL data warehouses,
whereas Worlds acts as a persistent, verifiable memory layer for software
agents.

## When Worlds fits

* You need an append-only RDF fact ledger to store agent memories and enterprise
  domain graphs.
* You require W3C standard SPARQL graph traversal across multi-hop entity
  relationships.
* You want portable, edge-deployable graph storage via LibSQL, Turso, or
  Postgres.

## When TextQL fits

* You want natural language Q\&A and SQL generation over existing SQL warehouses
  (Snowflake, BigQuery).
* You need automated data cataloging, metric definitions, and BI dashboard
  integration for human analysts.
* Your primary data assets are stored in relational analytical tables rather
  than RDF graphs.

## Coexistence and integration

Worlds and TextQL serve distinct layers in an enterprise AI stack. TextQL
handles BI analytics and SQL generation over relational data warehouses, while
Worlds provides agent fleets with persistent memory, SPARQL entity graphs, and
verifiable context retrieval.