OrionBelt Platform

OrionBelt is an open-source platform of five tools that lets AI agents go from a raw database schema to compiled, dialect-safe analytical SQL in a single conversation — and to scheduled, audit-ready reports on a cron. The Semantic Layer is the governed access point for AI agents (orionbelt.ralforion.com) — agents query consistent, defined metrics, never raw tables. It combines ontology generation, a YAML-based semantic layer, an OWL/SKOS ontology workbench, a scheduled-report runner, and a conversational AI interface — all connected via MCP (Model Context Protocol).

The Platform

OrionBelt Analytics

MCP server that auto-generates RDF/OWL ontologies from database schemas with fan-trap prevention for accurate AI-driven query generation.

MCP RDF/OWL Text-to-SQL 13 tools

OrionBelt Semantic Layer

The governed layer for AI agents to access data. Compiles YAML models (OBML) into optimized SQL across 8 dialects via custom AST — agents get consistent metrics, never raw tables. Freshness-driven cache, agent-facing API.

Live: orionbelt.ralforion.com

AI Governance REST API 8 Dialects MCP Arrow Flight

OrionBelt Runner

Run OBML query batches against the Semantic Layer and emit reports. YAML-defined runs produce self-contained MD/HTML reports plus an audit-grade YAML run-log with compiled SQL, query plans, and timing.

YAML Runs MD/HTML Audit Trail Cron/CI

OrionBelt Ontology Builder

Browser-based OWL & SKOS workbench. Streamlit + rdflib — no Java, no Protégé. Bulk operations, OWL-RL reasoning, gist upper-ontology starters, merge-aware imports.

Live: orionbelt.streamlit.app

OWL SKOS Streamlit OWL-RL

OrionBelt Chat

Conversational AI tying it all together. 300+ models via OpenRouter; Anthropic/OpenAI direct; local via MLX or Ollama. Dual MCP servers, MCP sampling, Plotly & Mermaid inline.

Chainlit Pydantic AI Multi-LLM MCP Sampling

Database Schema

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Analytics (Ontology)

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Semantic Layer (OBML)

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Compiled SQL

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Runner (Reports)

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Charts & Insights

Key Differentiators

Governed AI data access — agents query the semantic layer, not raw tables; consistent metrics, no SQL hallucinations, full audit trail
AST-based SQL generation — custom SQL AST ensures correct, injection-safe SQL (not string templating)
Agent-facing API — model-health on load, query-plan endpoint, structured warning codes; freshness-driven result cache with heartbeat invalidation
Multi-fact queries (CFL) — Composite Fact Layer handles queries spanning independent fact tables via UNION ALL
MCP-native — works with Claude, ChatGPT, Copilot, Cursor, Windsurf via stdio or hosted Streamable HTTP
Scheduled reports & audit trail — OrionBelt Runner emits MD/HTML reports with a YAML run-log capturing compiled SQL, plans, and timing
OSI interoperable — bidirectional with Open Semantic Interchange; OBSL graph + SPARQL on every loaded model

Supported Databases

PostgreSQL Snowflake BigQuery ClickHouse Databricks DuckDB Dremio MySQL

Architecture

REST API (FastAPI) with OpenAPI docs
MCP Server for AI agent integration
Gradio UI for interactive exploration
DB-API 2.0 + Arrow Flight SQL drivers
OBSL Graph + SPARQL querying
Docker images on Docker Hub
Hosted demo on Google Cloud Run (HTTPS, wildcard cert, scale-to-zero)

Live Endpoints — orionbelt.ralforion.com

API: /v1/* — REST + /docs (Swagger), /redoc
UI: /ui/ — Gradio playground with pre-loaded example model
MCP: /mcp — Streamable HTTP, no auth, paste the URL into your MCP client

Use Cases

AI-Assisted Analytics

AI agents query your data through semantic models instead of writing raw SQL. Guaranteed correct joins, aggregations, and dialect-safe output.

Multi-Cloud Data Access

One semantic model, eight SQL dialects. Write once, deploy to Snowflake, BigQuery, Databricks, or any supported warehouse.

Governed AI Data Access

The semantic layer is the gate AI agents go through to reach your data. Consistent metrics, RDF lineage, SPARQL querying, no raw-SQL hallucinations.