"Building a solid memory base for AI development" matching MCP servers:

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

MCP server for reading, searching, and optionally writing data in Solid Pods via LDP/RDF with headless client-credentials authentication.

Gives agents a stable interface for durable memory, combining exact, lexical, semantic, and graph search results.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

Provides AI coding agents with a local, Markdown-first shared memory, enabling them to record decisions, failures, progress, and handoffs and retrieve them across sessions via MCP tools.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

Turn editor chat history (Cursor/Claude Code/Windsurf/Copilot/Codex CLI) into typed Markdown memories + AGENTS.md + Cursor Rules via MCP. Local-first, git-trackable.

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.

Enables MCP clients to retrieve relevant memories and save confirmed durable facts through authenticated access backed by PostgreSQL provenance and Qdrant semantic search.

Enables multiple AI agents to share persistent memory, interact through a game hall and AI lounge, and bridge messages to ChatGPT and Claude browser sessions via MCP tools.

Enables AI clients to access a shared, authenticated memory and project management system with durable storage, task tracking, roadmaps, and semantic search, deployed on Cloudflare.

Enables AI assistants to retain memory across sessions by storing conversations locally in Markdown files, allowing personalization and continuity without external servers.

Provides AI-assisted long-term memory storage and retrieval with exact-phrase and semantic hybrid search, enabling save, read, delete, and admin recovery via MCP tools.

A persistent, searchable memory layer for AI assistants, exposed as an MCP server. It supports CRUD, namespaces, hybrid ranked search, relationships, chat-context assembly, import/export, and optional LLM-powered auto-extraction.

Enables AI agents across devices and tools to share one persistent, private memory layer, saving decisions, facts, preferences, and handoffs, then retrieving them via fast BM25-ranked full-text search with large attachments stored in object storage. Responses are capped and hardened against prompt injection, so agents can recall prior context at session start instead of starting from zero.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

A local-first MCP server that manages developer memory for coding agents, enabling shared project context, permissions, and audit trails across different agents.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

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.

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 knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

MCP server providing permanent, intelligent memory for AI coding assistants with retrieval, Hebbian learning, and governance, plus personal memory modules for accounts, habits, experiences, knowledge, and lifestyle. Enables AI assistants to recall project context across sessions and manage personal data securely.

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

Gives AI agents a shared, encrypted memory store accessible over MCP, with per-agent keys, per-namespace permissions, history and revert, attachments, and semantic search that handles Czech as well as English. Memories, agents, network access, and HTTPS can be managed through a self-hosted admin web on the same port, with backups being simply copies of the encrypted data file.

Enables MCP-compatible AIs to read a user's local three-layer memory of their own typed chat messages, including raw text, conversation summaries, and theme indexes, so responses can be grounded in the user's past decisions and preferences.

Enables authorized AI assistants to search, retrieve, and match approved career memories through a read-only MCP interface. It provides access to a private, source-linked knowledge base for grounded job-application support.

Enables MCP-capable AI clients to read and write structured, project-scoped memory through a Cloudflare Worker backed by D1 and R2.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

Enables local full-text and semantic search over saved content, providing search and retrieval capabilities via MCP for AI assistants.

Provides semantic search over a self-building personal technical knowledge base that automatically collects and distills content from GitHub Trending, AI news, and arXiv.

Auto-captured, auto-recalled, path-scoped memory for AI coding agents and teams.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic

Slipstream - Built by Vektor Memory - a persistent multi-layered memory architecture. 4-layer associative graph memory (MAGMA) with autonomous REM cycle, CLI, DXT-MCP Cloak Tools.

Multi-user, file-backed MCP memory server enabling isolated namespaces and persistent memory across sessions and AI clients.

A personal knowledge base MCP server that allows AI assistants to manage notes, tasks, and ideas through tools, resources, and prompts.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

基于MCP协议的知识库服务器，内置LLM层，提供结构化知识存储、语义搜索和主动知识推送，支持通过工具进行知识学习、搜索和关联推理。