"Understanding Memory or Memory-Related Topics" matching MCP servers:

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

A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep

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 portable, user-owned memory for MCP-capable agents in VS Code, enabling cross-agent recall and memory creation.

A local markdown memory and cross-agent context engine for AI coding assistants. It provides an MCP server with tools to search, add, retrieve, and distill persistent memory across tools like Claude Code, Cursor, and Zed.

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 local, persistent memory system for AI coding assistants that stores decisions, patterns, and session context via MCP tools. It enables cross-session memory management using SQLite and optional vector search without external dependencies or cloud storage.

Provides a persistent memory layer for Stellar and Soroban projects, scanning repositories to create a knowledge graph of contracts, storage, and on-chain state. Exposes this memory to AI agents via MCP, enabling project overview, search, and context recovery.

Provides a local-first episodic memory layer for AI coding clients, enabling persistent cross-session context, automated memory consolidation, and token optimization through an MCP daemon and stateless proxy.

A persistent, project-scoped memory layer for AI coding agents, providing 39 tools for capturing, searching, deduplicating, relating, and maintaining memory records across long-running coding sessions.

An MCP server that stores project memory in Supabase, allowing AI tools to persist context, notes, and decisions across sessions. Enables Claude Code, Codex, and similar assistants to maintain long-term project continuity through persistent memory storage.

MCP memory server for AI coding agents to remember decisions, patterns, and bugs between sessions. Provides persistent memory with 37 MCP tools, multi-session coordination, and token-efficient recall.

Provides a local-first persistent memory layer for coding assistants, enabling cross-project user preferences, per-project durable and working memory, session archives, and reflective recommendations through MCP tools.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

Hand a project over — to your next session, to Cursor/Codex/Gemini, to your successor — with the reasons attached. Local-first memory MCP for coding agents: decisions re-injected before the agent acts, drift detection across every project you run. One SQLite file, nothing leaves your machine.

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

Long-term memory for AI coding agents, stored as a tree of plain local files. Agents search it before answering and save what lasts; changed facts are kept as history and flagged as outdated.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

MCP server for managing project memory in AGENTS.md. Allows agents to record and remove durable facts using save/forget tools.

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.