Persistent Sqlite Knowledge Graph With Cypher Query Engine

via “knowledge graph generation from unstructured text via llm-driven entity and relationship extraction”

Knowledge Engine for AI Agent Memory in 6 lines of code

Unique: Implements a dual-storage architecture where extracted triplets are simultaneously indexed in both graph and vector databases (cognee/infrastructure/databases/), enabling hybrid queries that combine structural graph traversal with semantic vector similarity. Supports custom graph models via Pydantic schemas, allowing developers to define domain-specific entity types and relationship types without modifying core extraction logic.

Outperforms single-database RAG systems (like Pinecone-only or Neo4j-only) because it preserves both structural relationships (for reasoning) and semantic similarity (for relevance), reducing hallucination through multi-path validation; more flexible than LlamaIndex's graph RAG because custom schemas are first-class citizens.