The gist

Semantica is an open-source Python library that acts as a context and accountability layer for AI systems. Developed by semantica-agi, it sits alongside LLMs and vector stores to solve the problem of unauditable AI decisions. It adds structured knowledge graphs, causal reasoning, and a full audit trail, enabling developers to build AI applications that are explainable and compliant, particularly for enterprise and regulated industries.

What it does

Builds structured context graphs of everything an agent knows, decides, and reasons about.
Records every AI decision as a first-class, queryable object with traceable causal links.
Generates a full audit trail for every fact using W3C PROV-O provenance standards.
Runs explainable inference using reasoning engines like Rete, Datalog, and SPARQL.
Ingests and processes data from dozens of sources, including files, websites, databases, and APIs.
Extracts structured knowledge like entities, relations, and events from raw text.

How it works

As a Python library installed via pip, developers import Semantica's modules to build and query knowledge graphs programmatically. Users provide unstructured or structured data from various sources, and the tool produces a queryable graph with full provenance. It integrates with popular vector stores and can be used with or without an LLM. The project is open-source and available on GitHub, and includes a CLI and REST endpoints for interaction.

Best for

This library is best for developers building AI agents, GraphRAG systems, or enterprise knowledge platforms in regulated domains like finance and healthcare where auditable decision-making is a requirement.

Watch out for

When using the vector store component, avoid mixing vectors from different embedding models within the same index, as this can lead to inconsistent similarity search results.