"Understanding Persistent Memory and Context Management" matching MCP servers:

A memory management system that enables AI assistants to store, search, and visualize persistent conversation contexts using a Neo4j graph database. It provides an MCP server for integration with Claude Desktop along with a web-based dashboard for managing relationship-based knowledge.

A lightweight memory layer for MCP servers that persists user context across LLM sessions with minimal token cost, supporting key-value dedup, multi-tenant namespaces, and optional TTL.

Gives Claude Code long-term memory that persists across sessions via hybrid BM25 and vector semantic search, with multi-project isolation.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

MCP server for multimodal understanding and object grounding (bounding boxes) across images, videos, and documents, with support for multiple AI providers (Zhipu GLM-V, OpenAI GPT-4o, Anthropic Claude, or any OpenAI-compatible endpoint).

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

Enables AI assistants to execute shell commands and manage long-running processes within persistent tmux sessions across isolated workspaces. It features a dual-window architecture to separate raw command execution from interactive terminal output.

Tracks mistakes and solutions from past Claude sessions to avoid repeating failed approaches.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

A self-hostable MCP server that provides permanent memory for AI agents using Postgres + pgvector for semantic search and Markdown file sync.

Enables AI agents to store, read, extend, and share encrypted persistent memory, with pay-per-use access via x402 payments on Base.

Enables AI assistants to create and manage persistent terminal sessions that continue running even after disconnection, with features like smart output management, web UI visualization, and automatic bug fixing with Codex integration.

A local-first MCP server providing persistent, searchable knowledge base via SQLite, enabling AI agents to save and recall facts across sessions without cloud dependencies.

Enables persistent remote terminal sessions that survive dropped client connections, using pty-mcp and ai-tmux for interactive work and native OpenSSH for structured remote operations. Supports SSH config alias resolution, remote execution, file operations, and secure session management.

Enables LLM assistants to store, retrieve, and update user-specific context memory including travel preferences and general information through a chat interface. Provides analytics on tool usage patterns and token costs for continuous improvement.