#1818th of 18 in Memory

semantica

Python library for building context graphs with decision provenance and reasoning

Stars
13.8k
License
MIT
Last commit
Oct 2026
Last release
Sep 2026
Language
Python

Missed the latest GitHub check; these facts may be older.

Overview

Semantica is a Python library that ingests data from files, databases, Databricks, Snowflake and SAP, extracts entities and relations, and builds a knowledge graph with W3C PROV-O provenance. It records agent decisions as graph nodes with causal links, and runs rule-based reasoning (Rete, Datalog, SPARQL) and SHACL/OWL governance. It stores graphs in RDF or property-graph backends and exposes a CLI, REST API and MCP server.

Who it is for: Teams building auditable agent context and knowledge graphs in regulated domains

Strengths

  • Graph construction, reasoning and provenance are deterministic; no LLM required
  • Swappable storage: Oxigraph, Blazegraph, Jena, RDF4J, Neo4j, FalkorDB, AGE, Neptune
  • Decisions stored as graph nodes with causal tracing, precedent search and PROV-O export
  • Connectors for Databricks, Snowflake, SAP OData, Kafka, Kinesis, plus CrewAI, LangChain, Agno integrations

Weaknesses

  • Broad scope (ontology, reasoning, ingestion, analytics) means a large surface to learn
  • Explains context and decisions outside the LLM only, not the model's internal reasoning
  • Resource requirements and benchmark figures not stated in the visible README
  • Aimed at regulated enterprise use; heavy for simple agent memory needs

What it needs

  • no GPU
  • Docker + Compose
  • Needs Optional graph stores: Neo4j, FalkorDB, Apache AGE, AWS Neptune, Blazegraph, Jena, RDF4J, Optional vector store: FAISS, Optional LLM provider: OpenAI, Anthropic, Gemini
  • Models: OpenAI, Anthropic, Gemini

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