M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Competitor Profile: Neo4j ## Overview Neo4j is a native graph database designed for connected data, with recent additions of vector search capabilities to support AI and ML applications. ## Features - Native graph storage and processing - Cypher query language - Vector similarity search (v5.0+) - ACID transactions - Graph algorithms library - Full-text search ## Architecture - Native graph storage engine - Index-free adjacency - Distributed clustering (Enterprise) - Bolt protocol for client communication ## Memory Model - Graph-based storage (nodes, relationships, properties) - Vector indexes for similarity search - Relationship-first traversal optimization - Support for embeddings as node properties ## Pricing/Licensing - Community Edition: Free (GPLv3) - Enterprise Edition: Custom pricing - AuraDB (cloud): Free tier, then pay-as-you-go - Typical enterprise: $50k-$500k+ annually ## Deployment Options - Self-hosted (Community and Enterprise) - AuraDB (fully managed cloud) - Neo4j Desktop (local development) - Kubernetes deployment ## Integration Capabilities - Drivers for Python, Java, JavaScript, .NET, Go - Bolt protocol - REST API - GraphQL API - LangChain, LlamaIndex integration - Apache Spark connector ## Technical Pros - Industry-leading graph database - Powerful graph traversal and algorithms - Strong ACID guarantees - Mature ecosystem and tooling - Combining graph + vector search ## Technical Cons - Primary focus is graph, not vector search - Vector search features relatively new - Higher licensing costs for enterprise - Performance may not match specialized vector DBs for pure similarity search ## Citations - [Neo4j Documentation](https://neo4j.com/docs/) - [Neo4j Vector Search](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/)