# semantica-agi/semantica

Graph-Native Infrastructure for Context and Accountable AI Systems

Repository: https://github.com/semantica-agi/semantica
Canonical: https://ross.abutalabs.com/products/semantica
Homepage: https://getsemantica.ai
Language: Python
License: MIT
License Family: permissive
Topics: ai, ai-governance, artificial-intelligence, context-engineering, context-graphs, decision-intelligence, explainable-ai, generative-ai, graph-rag, knowledge-graph, llm, ontology, provenance, python, reasoning, ai-infrastructure, data-engineering, developer-tools, semantic-search, agent-memory
Last push: 2026-08-26T19:48:04+00:00

## Health v2 (maintenance only)
Score: 85/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 31
- inputs: {"age_days": 434, "days_push": 7, "days_rel": 13, "gap_med": 6.0, "n_releases_24m": 19}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10920, forks 1195 (observed 2026-08-28T04:10:44.646171+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: rag, nlp, agent-framework, search-engine, data-science, etl
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: python, self-hosted, cross-platform
- tags: 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
- semantica-agi/semantica (main) score 85

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.646171+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:23.189458+00:00, confidence not recorded.
  - readme: https://github.com/semantica-agi/semantica (fetched 2026-08-28T04:10:44.646171+00:00, sha e81698e3803d)
  - homepage: https://getsemantica.ai (fetched 2026-08-29T08:16:14.639672+00:00, sha d5d27faf229d)
  - site_page: https://docs.getsemantica.ai/ (fetched 2026-08-29T08:16:14.642976+00:00, sha 02630b7899d6)
  - site_page: https://getsemantica.ai/about (fetched 2026-08-29T08:16:14.645552+00:00, sha ac7cff86e1dd)
  - registry_pypi: https://pypi.org/pypi/semantica/json (fetched 2026-08-29T08:16:14.647602+00:00, sha 360fcf2af0eb)
- Data as of 2026-08-30T08:39:29.467469+00:00.