Qdrant
Qdrant is a high-performance, composable vector search engine built in Rust for production-grade semantic, hybrid, and agentic workloads.
Combine dense vectors, sparse vectors, metadata filters, multi-vector representations, and custom scoring as primitives at query time. Written in Rust for memory efficiency, SIMD optimization, and predictable performance without garbage collection pauses. No wrappers, no bolt-ons, no legacy compromises — just a custom HNSW implementation and storage engine built specifically for vector workloads.
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Membase
Membase is a unified AI memory layer platform designed to help AI agents and tools share and persist context so they “understand you” across sessions without forced repetition or isolated memory silos, enabling consistent conversational experiences and shared knowledge across AI assistants. It provides a secure, centralized memory layer that captures, stores, and syncs context, conversation history, and relevant knowledge across multiple AI agents and integrations with tools such as ChatGPT, Claude, Cursor, and others, so all connected agents can access a common context and avoid repeating user intents. Designed as a foundational memory service, it aims to maintain consistent context across your AI ecosystem, reducing friction and improving continuity in multi-tool workflows by keeping long-term context available and shared rather than locked within individual models or sessions, and letting users focus on outcomes instead of re-entering context for each agent request.
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CMEM Cloud
CMEM Cloud is the cloud sync layer for claude-mem, built to link AI agent memory everywhere through one private MCP link. claude-mem is the open source engine that takes notes while an agent works, and CMEM Cloud mirrors that local memory so agents can recall it across every session, machine, editor, and MCP-compatible client. Instead of making users re-explain context, paste old notes, or restart from zero, the system captures decisions, bug fixes, dead ends, environment notes, architecture choices, and other structured observations as the agent works. Those observations are stored in a temporal database, searched by meaning through vector recall, and made available through a private MCP endpoint that any compatible agent can read and write through. It starts with installing the local engine, letting a second model write structured notes out of band, syncing the local database to CMEM Cloud, and then recalling that memory anywhere.
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claude-mem
claude-mem is an offline-first cloud memory for AI agents, built around an open source engine and a cloud sync layer that links agent memory everywhere through one private MCP link. It is designed so coding agents and AI assistants do not start from zero every session, every machine, or every editor. claude-mem takes notes while an agent works, capturing decisions, fixes, dead ends, environment notes, architecture choices, and other structured observations in a temporal database. CMEM Cloud then mirrors that local memory behind a private Model Context Protocol endpoint, allowing any compatible agent or IDE to read and write the same memory across tools such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. It works locally first, with or without a network, while keeping memory synchronized when cloud access is available.
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