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Persistent, searchable, versioned memory for AI agents, backed by Valkey and exposed as an MCP server over HTTP.

A production-grade memory server for LibreChat that stores conversation turns and distills durable memories like decisions, constraints, and assumptions. It provides hybrid retrieval and consistency auditing to ensure AI plans remain aligned with established context and previous decisions.

Provides persistent AI agent memory using a local vector database for long-term semantic storage and short-term session scratchpads. It enables low-latency memory operations including search, storage, and bulk management without external cloud dependencies.

A local-first MCP server that exposes personal notes and files as unified semantic context for AI agents via vector search and file monitoring.

Provides persistent memory storage with advanced features like tagging, content search, and expiration settings. It enables users to create directed links between stored memories to build structured relationships and knowledge graphs.

Provides tools for AI agents to manage long-term memories, daily notes, and TODO lists through a structured markdown file system. It enables context awareness by allowing agents to read, write, and search entries for persistent information storage.

Exposes local persistent memory as an MCP server with Markdown storage, PARA organization, Zettelkasten linking, and SQLite FTS5 search. Enables Claude and other MCP-compatible agents to store, organize, and retrieve knowledge with context-based recommendations through the Olima association engine.

Enables agents to maintain persistent memory through three-tiered architecture: short-term session context with TTL, long-term user profiles and preferences, and searchable episodic event history with sentiment analysis. Provides comprehensive memory management for personalized AI interactions.

Enables persistent storage and retrieval of user preferences, context, and decisions across AI sessions using a structured JSON-based memory system. It provides tools for storing, searching, updating, and managing memories organized by namespaces and tags.

A lightweight MCP server that provides persistent key-value storage for AI agents using SQLite and Bun. It enables agents to store, retrieve, and manage memories with optional JSON metadata for long-term context retention.

Provides cross-device access to a persistent knowledge graph via Cloudflare Workers, enabling memory storage and retrieval through both MCP protocol and REST API with full-text search capabilities.

A universal, local-first MCP hub that indexes personal files (documents, code, etc.) and provides private semantic search via hybrid dense+BM25 retrieval, enabling agents like Claude Desktop to query your data without sending it to the cloud.

Persistent memory and handoff intelligence layer for MCP agents. Most memory servers retrieve text — Memory Nexus compounds operational context, learning from usage and progressively synthesizing observations into higher-order intelligence across sessions and tools.

Persistent memory system for AI agents that records episodic memories with care-weighting and emotional valence, and provides full-text search with temporal chaining and automatic consolidation.

A lightweight, stateless MCP server utilizing Puppeteer for web searches, returning structured JSON results, easily integratable with other MCP-enabled systems.

An MCP server that gives AI agents persistent, narrative memory: concepts as nodes, relationships as typed "because" edges, so anything filed can be pulled back by association rather than by address.

A Model Context Protocol integration for the MemOS memory system, optimized for personal AI assistant scenarios with intelligent memory management and retrieval capabilities.

A local AI memory system that stores all conversations verbatim and organizes them into navigable structures. It provides 19 MCP tools for AI assistants to search and retrieve past decisions, debugging sessions, and architecture debates automatically.

Enables AI coding assistants to access grounded, branch-scoped codebase context via semantic search, git tracking, change ledger, and structured feature management with Project Tracks.

Provides persistent memory for AI models by enabling the storage and retrieval of episodic, semantic, and procedural information through the memro protocol. It allows assistants to maintain long-term context via semantic search and chronological memory management.