Vibe Coding Discoversemantica
View on GitHubGraph-Native Infrastructure for Context and Accountable AI Systems
Semantica is a Python graph-native context and knowledge-graph layer for AI systems: ingest data, build context graphs, and run deterministic graph reasoning with W3C PROV-O provenance. Adds graph RAG, decision audit trails, ontology governance, and MCP/LangChain/CrewAI integrations on top of your existing LLM stack.
Use Cases
Build a context graph / knowledge graph for LLM and agent contextGraph RAG retrieval over structured enterprise dataAgent memory with shared multi-agent intelligence layerDecision provenance and audit trails for regulated AIAI governance with SHACL/OWL/SKOS ontology constraintsDeterministic reasoning (forward chaining, Rete, Datalog, SPARQL)Entity resolution and semantic deduplication across sourcesIngest Databricks/Snowflake/SAP data into a governed KGCompliance export to PROV-O, RDF, CSV, JSONGraph analytics: centrality, community detection, link prediction
Built With
- Language
- Python
- Frameworks
- LangChain · CrewAI · Agno · MCP · Neo4j · Oxigraph · FalkorDB · RDF4J · Jena · Databricks · Snowflake · SAP OData · Datalog
Tags
knowledge-graph · context-graph · graph-rag · provenance · ai-governance · decision-intelligence · ontology · agent-memory · explainable-ai · semantic-search · reasoning-engine · entity-resolution · mcp · sparql · shacl · python