semantica-agi/semantica
An open-source graph infrastructure that lets AI agents show their work, not just their answers
Semantica is an open-source Python framework that turns enterprise data into a knowledge graph and records every AI agent decision as a traceable, auditable object. Unlike vector-database RAG, which only finds things that look similar, it lets you traverse a graph to see how things are actually connected and why. It targets regulated industries like finance, healthcare, and legal where decisions need to survive an auditor's questions.
What it does
- Ingests data from many sources (files, web, databases, Databricks, Snowflake, etc.), builds it into a knowledge graph, and automatically extracts entities, deduplicates them, and flags conflicting facts instead of silently overwriting them
- Records every AI agent decision as a graph node via record_decision(), links decisions with causal relationships, and lets you replay the full history with functions like trace_decision_chain()
- Ships deterministic reasoning engines (Rete, Datalog, SPARQL) that work without any LLM, so the inference logic is rule-based and explainable rather than a black box
- Attaches W3C PROV-O provenance to every fact and can export audit trails as JSON, CSV, or RDF in formats accepted for regulator submission
- Reports a 6,000x speedup in node search on a 118,000-node benchmark graph compared to its prior version
Why it matters
As AI agents start making consequential decisions like loan approvals or medication checks, failing to explain why a decision was made becomes a compliance liability, not just an inconvenience. This project doesn't expose what happens inside the LLM itself, but it does make everything around it — the input data, the decision, and its provenance — traceable.
Terms in this repo
- Knowledge Graph · a data structure representing entities and their relationships as nodes and edges
- W3C PROV-O · a web standard for recording where data came from and how it was produced
- SHACL · a W3C standard for validating that graph data follows defined rules
- RDF/SPARQL · a standard way to store data as subject-predicate-object triples and query them
- Rete network · a rule-matching algorithm designed to efficiently evaluate many rules at once
Repository description (English)
Graph-Native Infrastructure for Context and Accountable AI Systems
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