"Context Memory Systems and Techniques" matching MCP servers:

A local-first MCP server that gives AI coding assistants persistent, structured, human-readable memory for a software project by storing project knowledge as Markdown files in the project's .dev-context-memory/ folder.

Exposes local memory vaults as 6 MCP tools over stdio, enabling clients like Windsurf, Zed, and Claude Desktop to read and write memory.

Enables AI coding agents to persist structured long-term memory (gotchas, architecture, API notes) in a .context folder and sync across devices and agents via Git.

MCP server that exposes agent-memory-daemon to any MCP-compatible client — Kiro (CLI & IDE), Claude Desktop, Cursor, and others. The daemon does the thinking (consolidation + extraction); this server is a thin filesystem bridge so agents can read, append, and search memory through the Model Context Protocol.

Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

A local MCP server that provides agents with tools to list, read, search, inspect history and diffs, and capture unstructured text in a user-owned Git repository of durable memory.

Provides project memory management for Claude Desktop, storing memories as Markdown files with intelligent keyword-based retrieval and automatic creation/update decisions by LLM.

A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).

Provides a local markdown-based memory system for coding agents through MCP, enabling search, add, register, inventory, sync, and ingest operations across user and project memory. It gives any agent tool a durable, provider-agnostic shared memory stored in folders you own.

A self-hosted memory layer for Claude using plain markdown files, enabling persistent context across different Claude accounts.

Enables reading, writing, and searching a local markdown-based memory store using MCP tools, with safety checks and index consistency.

Enables local, bounded routing and evidence judgments for Codex tasks, with tools for reading file context, checking outputs, and selecting registered workflows while keeping execution and review with the client.

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

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.

A thin, local, read-only MCP gateway that exposes governed context documents and producer manifests as logical resources, enabling AI clients to access them without arbitrary filesystem access.

Bridges Claude Code and Claude.ai by exposing project memory files over MCP for shared context across both interfaces.

A Model Context Protocol (MCP) server that provides VSCode context and filesystem operations for AI assistants.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.