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

# Competitor Profile: Qdrant ## Overview Qdrant is an open-source vector similarity search engine with extended filtering support, written in Rust for performance and memory safety. ## Features - High-performance vector search - Rich filtering and payload support - Exact and approximate search - Distributed deployment - Quantization for memory efficiency - Real-time data updates ## Architecture - Rust-based implementation - HNSW algorithm for indexing - Column-oriented payload storage - gRPC and REST APIs - Horizontal scaling support ## Memory Model - In-memory index with disk persistence - Segment-based storage - Product, scalar, and binary quantization - Support for named vectors (multiple vectors per point) ## Pricing/Licensing - Open-source (Apache 2.0 license) - Qdrant Cloud: Free tier, then pay-as-you-go - Self-hosted: Free - Enterprise support: Available ## Deployment Options - Self-hosted (Docker, binary, from source) - Qdrant Cloud (managed service) - Kubernetes deployment - Embedded mode for local development ## Integration Capabilities - Python, JavaScript, Go, Rust clients - REST and gRPC APIs - LangChain integration - LlamaIndex support - OpenAI, Cohere embedding support ## Technical Pros - Rust performance and memory safety - Advanced filtering capabilities - Efficient quantization options - Good balance of features and simplicity - Self-hosting friendly ## Technical Cons - Smaller community compared to Pinecone/Weaviate - Less mature ecosystem - Documentation could be more comprehensive - Limited graph capabilities ## Citations - [Qdrant Documentation](https://qdrant.tech/documentation/) - [Qdrant GitHub](https://github.com/qdrant/qdrant)