Connect ideas.
Discover meaning.
Atomixi is a semantic knowledge graph engine. Build knowledge as connected atoms, explore through contextual lenses, and let intelligence emerge from structure.
Features
Semantic Triples
Entity A → Relation → Entity B. The fundamental unit of knowledge. Bidirectional traversal lets you explore in any direction.
Context Lenses
View the same graph through different perspectives. "Apple" means fruit in nutrition, company in tech. Contexts filter without destroying.
Temporal Knowledge
Facts change over time. Every relation carries validity windows. Ask: "What did I know about X in March 2024?"
Inference Engine
Transitive reasoning: if Apple is_a Fruit and Fruit is_a Food, Atomixi infers Apple is_a Food. Symmetry, inverse rules built in.
Vector Search
Semantic similarity through embeddings. Find concepts related by meaning, not just explicit links. Powered by SurrealDB's native vector index.
Self-Hosted & Private
Your knowledge stays yours. Single binary database, no cloud dependency. Run locally or on your own server. Full data sovereignty.
How it works
Build knowledge graphs with simple, intuitive operations
# Create atoms of knowledge Network.create_entity("Apple", "fruit", "A red fruit") Network.create_entity("Fruit", "category", "Food category") Network.create_entity("Food", "category", "Sustenance") # Connect them with meaningful relations Network.connect("Apple", "Fruit", "is_a", contexts: ["nutrition", "biology"]) Network.connect("Fruit", "Food", "is_a", contexts: ["nutrition"]) # Query through a context lens Network.get_connections("Apple", context: "nutrition") # Transitive inference Inference.is_related?("Apple", "Food", "is_a") # => true (via Apple → Fruit → Food)