"Understanding Persistent Memory in Cursor Functionality" matching MCP servers:

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

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.

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.

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.

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.

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.

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.

An MCP server that gives AI agents persistent, narrative memory: concepts as nodes, relationships as typed "because" edges, so anything filed can be pulled back by association rather than by address.

Persistent memory for AI agents as a single Go binary: hybrid recall, self-building knowledge graph, and a live TUI dashboard. Zero infra, offline-first — works with Claude Code, Cursor, Codex, OpenCode, and any MCP client.

Persistent, consensus-validated institutional memory for AI agents. Gives LLMs real memory that survives across sessions - validated through BFT consensus, not just dumped to a file.

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.

Coordination layer for AI coding agents working on the same codebase. Adds file locks, shared project memory, and cross-machine file sync so Claude Code, Cursor, Windsurf, and other MCP agents stop overwriting each other.

Connect your Umami Analytics to any MCP client to derive insights from natural language.

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

Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.