semantica

Graph-native context infrastructure for accountable AI: ingest enterprise data, extract a knowledge/context graph (RDF + LPG), run graph analytics and causal reasoning with decision provenance.

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Curator's take

The accountability angle is the real differentiator — decision provenance and deterministic reasoning aimed at regulated domains, where 'the agent decided' isn't an acceptable audit trail. W3C-standards + both RDF and LPG is rare breadth. When NOT: the README's marketing density ('open-source Palantir') outruns its documentation depth — prototype the core path before betting a compliance program on it; ★2.3k and the platform surface is huge for the team size.

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README.md

Graph-Native Infrastructure for Context and Accountable AI Systems

The Open Source Palantir for AI Agents

Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.

Decision Intelligence · Context Management · Deterministic Reasoning · Ontology Management · Knowledge Modeling · End-to-End Traceability

Open Source · Self-Hostable · Auditable · Governed · Zero Vendor Lock-In

Polyglot Graph Storage · RDF & LPG Support · W3C Standards · Interoperable

Built for High-Stakes, Regulated Domains

pip install semantica

Semantica Knowledge Explorer: live graph, decisions, entity resolution, ontology hub

Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub

▶ Watch the full platform walkthrough

Most AI agents act without a trail. They store embeddings, not meaning: context that can't be explained, decisions that can't be audited. In lending, that gap is a compliance exposure, not an inconvenience:

Continue your stack

What teams reach for next — and why each earns a place beside semantica. Ranked by curator confidence.

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Hyper-Extract

Knowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export.

Why it fitsBoth LLM-extract knowledge graphs from enterprise sources; hyper-extract stops at extraction with an MCP server, Semantica wraps extraction in governance, analytics and causal reasoning.

omnigraph

Lakehouse graph database for agent context — graph, vector and full-text retrieval fused in one runtime on branchable Lance/S3 storage; agent fleets write on isolated branches and merge Git-style.

Why it fitsGraph context for agents: omnigraph is a storage-first lakehouse runtime; Semantica is a reasoning-first platform where provenance and causality are the product.

OpenMetadata

Open metadata platform turned AI context layer: 130+ connectors feed a unified knowledge graph of lineage, quality, ownership, glossaries and contracts — served to agents via MCP and APIs.

Why it fitsEnterprise context layers for AI: OpenMetadata catalogs what data IS across 130+ systems; Semantica reasons over what it MEANS with provenance and causality. Catalog-first vs reasoning-first.