"Understanding or Using Memory Lists" matching MCP servers:

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.

Minimal long-term memory MCP server for AI assistants backed by SQLite FTS5 full-text search, optional hybrid vector retrieval, scoped memory graphs, and VSCode extension.

Governed multi-agent memory for AI agents. Hybrid markdown + SQLite store with full-text search, vector retrieval, and LLM reranking. Three transports: MCP stdio, HTTP JSON-RPC, and MCP SSE. One Go binary

Manages an agent's long-term memories as versioned markdown files with searchable SQLite indexes, supporting lifecycle states, conflict-gated recall, revision, and explicit forgetting without automatic deletion.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

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.

An MCP server that provides persistent memory for AI agents, enabling context retention across sessions and supporting vibe-loop workflows where agent promises and deliverables are tracked and validated.

Local-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.

Enables LLM agents to maintain structured long-term memory and knowledge using SQLite, with graph storage and relational query tools for persistent context.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

Provides coding agents with persistent local memory by exposing tools to save, search, and retrieve decisions, bugs, and context as Markdown with hybrid keyword and semantic search.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.

Enables LLMs to read and search X posts, manage users and lists, upload media, and publish posts using the X API v2 with OAuth user context.

An MCP server for codebase context that gives AI coding agents structural understanding through symbol graph, semantic search, blast radius, and convention detection tools.

MCP server to manage your GitHub starred repositories and lists

Enables interaction with Trello boards, lists, and cards through MCP-compatible clients, allowing read and update operations on allowlisted boards.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

MCP server for Lightpanda headless browser enabling ultra-fast HTML extraction, markdown conversion, and JavaScript execution with 16x lower memory footprint than Chrome.