### Graph-Native Infrastructure for Context and Accountable AI Systems #### *Developer-first, knowledge infrastructure for AI, alternative to expensive enterprise platforms.* > 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. **Context Management  ·  Knowledge Modeling  ·  Deterministic Reasoning  ·  Ontology Management  ·  Decision Intelligence  ·  End-to-End Traceability** **Open Source  ·  Governed  ·  Zero Vendor Lock-In** **Polyglot Graph Storage  ·  RDF & LPG Support  ·  W3C Standards  ·  Interoperable** #### Built for High-Stakes, Regulated Domains [](https://github.com/semantica-agi/semantica) [](https://github.com/semantica-agi/semantica/network/members) [](https://github.com/semantica-agi/semantica/graphs/contributors) [](https://pypi.org/project/semantica/) [](https://pepy.tech/project/semantica) [](https://www.python.org/) [](https://opensource.org/licenses/MIT) [](https://github.com/semantica-agi/semantica/actions) [](https://github.com/semantica-agi/semantica/actions/workflows/install-matrix.yml) [](https://scorecard.dev/viewer/?uri=github.com/semantica-agi/semantica) [](https://deepwiki.com/semantica-agi/semantica) [](https://getsemantica.ai/) [](https://docs.getsemantica.ai/) [](https://discord.gg/sV34vps5hH) [](https://x.com/BuildSemantica) [](https://www.youtube.com/watch?v=QfnNZg4-dZA)

pip install semantica

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*Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub* **[▶ Watch the full platform walkthrough](https://www.youtube.com/watch?v=QfnNZg4-dZA)**

Most AI agents run on embeddings, not meaning: similarity scores with no structure, no relationships, and no way to explain why a result came back.

Semantica is the semantic/context layer underneath your LLM, vector store, and agent framework: deterministic infrastructure (no LLM required for graph construction, reasoning, or provenance; where an LLM is used, it's optional and vendor-neutral, every major provider supported, OpenAI, Anthropic, Gemini, and more, via semantica.llms) that turns fragmented enterprise data into a structured, queryable Context Graph and knowledge graph that carries the business context, not just the data structure. Ontologies and controlled vocabularies (OWL, SHACL, SKOS) make what an entity means to your business, its definitions, relationships, and rules, as explicit as the data itself, not just its embedding.

Decision provenance and audit trails aren't the product. They fall out of that structure for free, and in domains a regulator can question, the same structure that makes your agent smarter also gives you a straight answer to "why."

[!NOTE] System-level explainability, not foundation-model explainability. Semantica doesn't expose or reconstruct what happens inside the LLM: its internal reasoning stays opaque, like it does for any external system. Semantica explains what's outside the model: the context fed in, the decision produced, its provenance, relevant relationships, applied policies, and the full execution trail.

Who it's for:

• AI/ML platform teams shipping agents that make consequential decisions and need structured, queryable context, not just a vector index
• Enterprise data teams on Databricks, Snowflake, or SAP turning tables already in the lakehouse or warehouse into a governed, lineage-tracked knowledge graph, without exporting to a third-party SaaS
• Compliance, risk, and audit teams who need a straight answer to "why did the AI do that?" in a format a regulator accepts
• Regulated enterprises (finance, healthcare, legal, government, defense) that can't ship a black box or hand their data to someone else's SaaS to get one
• Platform and infra engineers who want the KG, reasoning, and provenance stack self-hosted and swappable, not locked to one vendor's backend
• Data and knowledge engineers building a KG from messy, multi-source data, where conflicting facts get flagged and duplicates get merged, not silently overwritten

Quick Start  ·  Architecture  ·  What You Get  ·  Why Semantica  ·  Decision Intelligence  ·  Context Graphs  ·  Recipe: Audit Trail  ·  Module Reference  ·  Integrations  ·  CLI  ·  Performance  ·  Install

What Semantica Gives You

• Context Graphs: A structured, queryable graph of everything your agent knows, decides, and reasons about
• Decision Intelligence: Every decision is a first-class object: traceable, searchable by precedent, and causally linked
• AI Governance & Ontology: SHACL constraints, conflict detection, compliance rules, OWL generation, and SKOS vocabularies, all with a visual editor
• Full Auditability: W3C PROV-O provenance on every fact, exportable to JSON, CSV, or RDF
• Deterministic Reasoning: Forward chaining, Rete network, Datalog, and SPARQL, with fully explainable paths, not black boxes
• Knowledge Pipeline: Multi-source ingestion, entity-aware chunking, NER/relation/event extraction, and graph construction, with semantic dedup and provenance-preserving merges built in
• Enterprise Data Platforms: Native connectors for Databricks (Unity Catalog + Delta Lake), Snowflake, and SAP OData, so data already in your lakehouse or warehouse becomes graph nodes with provenance, no export/import hop
• Graph Analytics: Centrality, community detection, link prediction, and shortest-path queries over the graph you just built
• Polyglot Graph Storage: RDF (Oxigraph, Blazegraph, Jena, RDF4J) and Labeled Property Graphs (Neo4j, FalkorDB, AGE, Neptune), plus vector stores, all swappable without touching your code
• Visualization: Explore any graph, ontology, or timeline in an interactive browser workbench
• Drop-in Integrations: Agno, CrewAI, and LangChain support, a full MCP server, a CLI, a REST API, and plugins across major editors

Why Semantica