semantica-agi/semantica

Health v2 · maintenance only

Adoption not part of the score

10920 stars · 1195 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Semantica is a Python library providing graph-native infrastructure for building context graphs, knowledge graphs, and decision intelligence layers for AI systems. It ingests enterprise data, extracts entities and relations, and records full decision provenance so AI agent outputs are explainable and auditable.

Use cases

• build a knowledge graph from enterprise documents
• add provenance tracking to AI agent decisions
• implement graph RAG with traceable sources
• give agents structured long-term memory
• extract entities and relations from text
• make LLM outputs auditable for regulated industries
• run causal reasoning over a context graph

When to choose

• you need explainable, auditable AI decisions in regulated domains like finance, healthcare, or legal
• you want a self-hostable, MIT-licensed knowledge graph layer beneath LangChain, LlamaIndex, or CrewAI
• you need entity/relation extraction and ontology management in Python
• you require W3C-standard provenance and RDF/LPG interoperability

When to avoid

• you just need a simple vector store or embedding database without graph structure
• you want explainability of the LLM's internal reasoning rather than system-level traceability
• you need a turnkey managed SaaS with vendor support
• your project is outside Python

Facets

library · maturity active

rag nlp agent-framework search-engine data-science etl artificial-intelligence large-language-models developer-tools python self-hosted cross-platform knowledge-graph context-graph provenance explainable-ai decision-intelligence graph-rag ontology entity-extraction agent-memory ai-governance audit-trail rdf ai-agents retrieval-augmented-generation data-engineering natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="semantica-agi/semantica")