OrionBelt: Semantic Sidecar for Agentic AI

The complete AI-native analytics stack: Semantic Sidecar for governed agent access, MCP servers, knowledge graphs & scheduled reports
RALFORION d.o.o.
ralforion.com · BSL 1.1 Open Source

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. At its core, the OrionBelt Semantic Layer (OBSL) is a Semantic Sidecar for AI, analytics, and governed data systems (orionbelt.ralforion.com): it injects governed business semantics into existing platforms with no architecture change. Agents query in business concepts, never raw schemas. The platform 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
1

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
2

OrionBelt Semantic Layer

Open-source Semantic Sidecar: injects governed business semantics into existing AI, analytics, and data platforms with no architecture change. Compiles YAML models (OBML) into optimized SQL across 8 dialects via custom AST. Agents query in business concepts, never raw schemas. Freshness-driven cache, agent-facing API.

Live: orionbelt.ralforion.com
Semantic Sidecar Analytics as Code AI Governance REST API 8 Dialects MCP Apache Arrow Flight
3

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
4

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
5

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
→
Analytics (Ontology)
→
Semantic Layer (OBML)
→
Compiled SQL
→
Runner (Reports)
→
Charts & Insights
Key Differentiators
  • Semantic Sidecar pattern: drop-in governed semantics for existing AI, analytics, and data systems; no architecture change, no dedicated semantic infrastructure
  • Analytics as Code: version-controlled YAML compiled into executable SQL, DQ rules, KPIs, and semantic context; one model powers AI agents, analytics, DQ checks, and reporting
  • 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/PDF 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
  • PostgreSQL Wire Protocol endpoint (psql, JDBC, ODBC, BI tools)
  • Gradio UI for interactive exploration
  • DB-API 2.0 + Apache 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
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.

Ready to try OrionBelt?

Docker one-liner · Open source · No account required

Live Demo
GitHub · Docs · Contact
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