Semantica is graph-native infrastructure for building the context and knowledge graphs that accountable AI systems need. The open-source framework has more than 14,100 stars on GitHub and was actively developed in 2026.
Semantica ingests enterprise data, extracts the entities and relationships that matter, and organizes them into a context graph and knowledge graph. On top of that graph you can run analytics and causal reasoning, power GraphRAG and semantic search for LLM applications, and — unusually — trace every decision back through its provenance, so AI outputs stay explainable and auditable. It positions itself as a developer-first, open alternative to expensive enterprise knowledge platforms, with ontology management and decision intelligence built in rather than bolted on.
The emphasis on accountability is what sets Semantica apart from the many knowledge-graph libraries aimed only at retrieval. Every extracted fact and every reasoning step carries provenance, so when an AI system makes a recommendation, auditors can walk the graph back to the source data and the transformations applied along the way. Its deterministic reasoning layer complements LLM-based extraction: the language model proposes structure, and graph rules verify and connect it. For regulated industries and enterprise deployments — where “the model said so” is not an acceptable audit trail — that combination of context graphs, ontology management and traceability is the point.
Semantica is free and open source under the MIT license. You run it on your own infrastructure and connect the data sources and models you choose.
Knowledge engineers, enterprise AI teams and GraphRAG builders who need trustworthy, traceable context behind their AI systems.