"Information about memory" matching MCP servers:

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

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

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.

A lightweight, local memory server for AI coding assistants that stores durable knowledge (decisions, facts, gotchas) in a tree structure, enabling persistent context across sessions with efficient retrieval via summaries and FTS5 search.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Enables coding agents to persist notes, decisions, requirements, and learnings per project in SQLite and retrieve them across sessions via search and status tools.

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

A local-first MCP server that gives AI coding assistants persistent, structured, human-readable memory for a software project by storing project knowledge as Markdown files in the project's .dev-context-memory/ folder.

Enables AI coding agents to store and retrieve durable project decisions, conventions, environment notes, and bug fixes in Notion, with optional Obsidian Vault dual-write.

Enables AI assistants to search, compare, and get detailed information about MCP servers from the mcpcatalogs.com directory.

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.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Provides Claude Code with cross-session long-term memory via Letta, allowing it to read and store project context, learnings, and decisions.

A minimal MCP server that exposes a single tool to query local hardware information (OS, CPU, memory, architecture).

Provides persistent memory for OpenCode coding agents, allowing them to store and recall architecture decisions, conventions, and gotchas across sessions via semantic search.

Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.

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.

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

Automatically records AI conversation turns and code changes to local Markdown files to provide persistent context across chat sessions. It enables AI agents to search history through MCP tools and provides a web viewer for browsing past discussions.

Enables coding agents to query and modify a SQLite database through twelve SQL-based tools, with safety enforced server-side via statement policies, protected tables, result caps, and snapshots. It also provides an append-only, hash-chained memory log with checkpoints so agents can record events and resume context across sessions.