A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.
Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
A static, read-only MCP server that exposes a database knowledge base (schema, relationships, workflows, and reasoning patterns) so Claude Code / Copilot can reason about a database without a live connection.
A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.
A self-hosted MCP server that gives AI agents shared, long-term memory over a git-backed folder of markdown, enabling persistent knowledge search, read, and write without a database.
An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.
A personal digital twin MCP server that manages professional context, including skills, experience, and project learnings. It allows AI assistants to retrieve and update user preferences and career interests across different development environments.
An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.
A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.
A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.
Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.
An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.
A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.
A productivity coaching assistant designed for ADHD support that provides intelligent task management, goal tracking, and personalized recommendations. It utilizes a persistent memory system to learn user patterns and preferences, helping to reduce decision paralysis through actionable suggestions.
A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.