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

# Competitor Profile: Weaviate ## Overview Weaviate is an open-source vector database that stores both objects and vectors, enabling combining vector search with structured filtering. ## Features - Vector search with GraphQL API - Hybrid search (vector + keyword + scalar filtering) - Modular vectorization (bring your own embeddings or use built-in) - Multi-tenancy support - CRUD operations on objects - Real-time indexing ## Architecture - Modular, plugin-based architecture - Distributed deployment support - HNSW index for ANN search - GraphQL query interface ## Memory Model - Object-vector dual storage - Schema-based data modeling - Vector embeddings per object - Support for multiple vector spaces per class ## Pricing/Licensing - Open-source (BSD-3-Clause license) - Weaviate Cloud Services: Starting at $25/month - Self-hosted: Free - Enterprise support: Custom pricing ## Deployment Options - Self-hosted (Docker, Kubernetes) - Weaviate Cloud Services (managed) - Hybrid deployment support ## Integration Capabilities - Python, JavaScript, Go, Java clients - GraphQL API - REST API - LangChain, LlamaIndex integration - Built-in vectorizers (OpenAI, Cohere, HuggingFace) ## Technical Pros - Open-source with active community - Flexible schema design - Strong filtering capabilities - Self-hosting option - Good balance of features and performance ## Technical Cons - GraphQL learning curve - Complex configuration for advanced features - Resource-intensive for large datasets - Limited graph traversal compared to Neo4j ## Citations - [Weaviate Documentation](https://weaviate.io/developers/weaviate) - [Weaviate GitHub](https://github.com/weaviate/weaviate)