"memory" matching MCP servers:

This MCP server provides a deterministic index of the First Principles Framework (FPF) spec, enabling agents to query patterns, routes, and relations without vector databases.

A local MCP server that accumulates the business-domain knowledge of a software project — flows, integrations, decisions, nuances — and makes it available to any MCP-compatible agent (Claude Code, Cursor, Copilot, Gemini, OpenCode). The goal is to capture the why behind the code: the stuff a new developer would need six months from now to understand why a thing is built the way it is. The what is

Make it easy for agents to build their context about your projects over time The server provides a set of tools to help agents accumulate knowledge about a project over time in a structured way.

Enables semantic search over a software project's Markdown and text documentation by indexing document chunks in Qdrant and exposing retrieval through MCP tools.

Provides a structured documentation system for context preservation in AI assistant environments, helping users create and manage memory banks for their projects.

A deterministic, read-only MCP server that lets coding agents search Git-backed architecture decision records and retrieve decisions governing specific files. It includes rejected and superseded decisions, with no model, network, or write access.

A server for managing project documentation and context across Claude AI sessions through global and branch-specific memory banks, enabling consistent knowledge management with structured JSON document storage.

Enables MCP clients to work with a Memory Bank through routing, section-aware reading, governance validation, dependency graph traversal, full-text search, change tracking, and document creation or promotion.

A plain-files brain template and MCP server that lets models read owned, portable working memory via list, read, and grep-style search tools.

Enables AI coding agents to list, search, and read the official TypeSafe Jev documentation through MCP tools, with in-memory caching and no API key required.

An MCP server that turns the Game Boy Pan Docs into precise, citable lookups for registers, opcodes, memory, cartridges, and CPU behavior, enabling decompilation work.

Gives AI coding agents live, cached access to current Flutter, Dart SDK and pub.dev documentation returned as clean markdown, resolving class names, identifiers and packages on demand instead of relying on a model's memory. Also provides ranked name search over thousands of classes and a politeness layer with robots.txt compliance, throttling, conditional GETs and request budgets.

Memory-efficient MCP server for reading and splitting large PDF files page by page using PyMuPDF.

Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.

Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.

Provides professional C++ code style checking, best practices analysis, and modern C++ upgrade suggestions with tools for naming conventions, memory safety, const correctness, and access to comprehensive C++ style documentation.

Gives coding agents in-memory access to the language's specifications, diagnostic and fault catalogue, verified code recipes, and university curriculum, plus toolchain tools to check, evaluate, and test source when a local binary is installed.

Enables LLM agents to search and retrieve a hybrid knowledge base of manuals, videos, PDFs, DOCX files and images through tools for hybrid BM25-plus-vector search with facet and tag filters, full document retrieval, semantically similar document lookup, and timestamped video transcript segments. It ships with an in-memory backend for instant local use and an identical Elasticsearch backend for production deployment over stdio or authenticated HTTP.

Enables natural-language Q&A over multi-source documents (PDF/Markdown/web) with a self-reflective retrieval-augmented agent that cites sources, retries on poor retrieval or grounding, and supports multi-turn memory.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.