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Semantica

Python toolkit that builds knowledge graphs and decision audit trails to give AI systems structured, explainab

GitHub Download v0.7.0
Stars
14.4k
Forks
1.7k
Licence
MIT
Language
Python
Last update
4 days ago
Type
Library

What it is

Semantica is a Python library for turning scattered data into a structured knowledge graph, which it calls a Context Graph, that AI agents can query. It records relationships, ontologies, and decision provenance so you can trace what context fed into an AI decision. The README says it is meant to sit under an LLM, vector store, or agent framework rather than replace them.

  • Build a knowledge graph from tables or documents and query its relationships
  • Record each AI decision with its inputs, applied policies, and execution trail for audit
  • Define ontologies using OWL, SHACL, or SKOS to describe business meaning of entities
  • Use optional LLM providers through the semantica.llms module

Great for

  • AI/ML platform teams building agents that need structured context beyond vector search
  • Data engineers building a knowledge graph from multi-source data with conflict detection
  • Teams in regulated domains who want self-hosted, auditable decision trails
  • Developers who want an open-source alternative to enterprise knowledge platforms

Not ideal for

  • People who only need simple retrieval over documents; a vector database or basic RAG setup is likely enough
  • Anyone wanting explainability of the LLM's internal reasoning, since the README says Semantica explains only what is outside the model
  • Non-technical users looking for a ready-made end-user app, since the README centers on a Python install
Step-by-step guide: how to use SemanticaInstall, first run, examples, tips and FAQs
Semantica alternativesLlamaIndex vs SemanticaPageIndex vs SemanticaLangGraph vs Semantica

How to get it

  1. 1

    Install with a package manager

    You'll need: Python 3.11+

    Install from PyPI

    pip install semantica
  2. 2

    Run it

    • Install the package with pip install semantica
    • Check the Quick Start section of the README for the first example
    • Pick a data source and build a small Context Graph from it
    • Query the graph and inspect the provenance it records
  3. 3

    Get the most out of it

    • Use ontologies (OWL, SHACL, SKOS) to make entity meanings explicit
    • Use the semantica.llms module if you want an LLM provider, since it is optional
    • Review the Recipe: Audit Trail section for a regulated-decision example
    • Explore the Module Reference and CLI sections for the full feature set

Downloads

Source code (.zip)main branch · straight from GitHubRelease v0.7.04 files incl. semantica-0.7.0.tar.gz.sigstore.jsonOfficial website & docsgetsemantica.ai

Strengths

  • Deterministic graph construction and reasoning; the README says no LLM is required for these steps
  • Open source under MIT with self-hostable, swappable storage backends
  • Supports standard formats such as RDF, OWL, SHACL, and SKOS

Trade-offs

  • The README is truncated and long, so the full scope and maturity of each module is hard to verify from it alone
  • Project is marketed heavily as enterprise-grade; claims in the README are self-reported and not independently verified here
  • Requires familiarity with knowledge graphs and ontologies to get real value

How it compares

ProjectStarsTypeLevelUpdatedBest for
semantica (this)14.4kLibraryadvanced4 days agoAI/ML platform teams building agents that need structured context beyond vector search
llama_index52.8kLibraryintermediate10 days agoPython developers building retrieval-augmented generation (RAG) apps over private documents
awesome-ai-engineering22.5kCurated listbeginner16 days agoDevelopers who want an overview of the AI engineering topics covered in the description
hexstrike-ai13.3kAI model / agentadvanced3 months agoSecurity professionals who want to script or speed up recon with an AI assistant
Trail-Code11kDev toolbeginner4 days agoDevelopers who use Claude Code, Cursor, Codex or similar agents on a medium or large repository
LightRAG40.5kLibraryintermediate8 days agoDevelopers building question-answering over their own document collections

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.