MCP AutoMem

AutoMem MCP: Give Your AI Perfect Memory

One command. Infinite memory. Perfect recall across all your AI tools.

npx @verygoodplugins/mcp-automem install

Your AI assistant now remembers everything. Forever. Across every conversation.

https://github.com/user-attachments/assets/fd79112b-5158-4320-a054-8c18ab1ea314

The guided installer — npx @verygoodplugins/mcp-automem install walks you through local, hosted, or existing-endpoint setup.

Works with Claude Desktop, Cursor IDE, Claude Code, GitHub Copilot (coding agent), ChatGPT, ElevenLabs, OpenAI Codex, OpenClaw, Hermes, Grok Build, Google Antigravity - any MCP-compatible AI platform.

The Problem We Solve

Every AI conversation starts from zero. Claude forgets your coding style. Cursor can't learn your patterns. Your assistant doesn't remember yesterday's decisions.

Until now.

AutoMem MCP connects your AI to persistent memory powered by [AutoMem](https://github.com/verygoodplugins/automem) - a graph-vector memory service.

What You Get

🧠 Persistent Memory Across Sessions

• AI remembers decisions, patterns, and context forever
• Works across all MCP platforms - Claude Desktop, Cursor, Claude Code, OpenAI Codex, OpenClaw, Hermes, Grok Build, Google Antigravity
• Cross-device sync - same memory on Mac, Windows, Linux

🏆 Graph-Vector Architecture

• 11 public authorable relationship types between memories (recall results may also include read-only system/internal relations that are not valid associate_memories inputs)
• Research-validated approach (HippoRAG 2: 7% better associative memory)
• Sub-second retrieval even with millions of memories

🚀 Works Everywhere You Code

See It In Action

Claude Desktop with Personal Preferences

_Claude automatically recalls memories using the Personal Preferences template_

Cursor IDE with Memory Rules

_Cursor uses AutoMem project rules to recall context and save durable memories during work_

Claude Code with Session Memory

_Session-start recall plus LLM-judged storage: Claude decides what's durable and stores it via the memory tools_

More platform walkthroughs (Codex, Hermes, Antigravity, remote MCP) live in the [Installation Guide](INSTALLATION.md).

Quick Start

1. Set Up AutoMem Service

You need a running AutoMem service (the memory backend). Choose one:

Option A: Local Development (fastest, free)

git clone https://github.com/verygoodplugins/automem.git

cd automem make dev

Service runs at http://localhost:8001 - perfect for single-machine use.

Option B: Railway Cloud (recommended for production)

One-click deploy with $5 free credits. Typical cost: ~$0.50-1/month after trial.

👉 [AutoMem Service Installation Guide](https://github.com/verygoodplugins/automem/blob/main/INSTALLATION.md) - Complete setup instructions for local, Railway, Docker, and production deployments.

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2. Install MCP Client

Claude Desktop - One-Click Install

Download the extension, then open Settings → Extensions → Advanced settings → Install Extension in Claude Desktop and select the file:

[⬇️ Download AutoMem for Claude Desktop (.mcpb)](https://github.com/verygoodplugins/mcp-automem/releases/latest/download/mcp-automem.mcpb)

After installing:

1. Claude Desktop will prompt you for your AutoMem API URL (http://127.0.0.1:8001 for local)
2. Optionally enter your API Key (required for Railway, skip for local)
3. Click Enable

The bundle includes a fixed version of the MCP server. To update, download and install the latest `.mcpb` again; restarting Claude or updating the npm package does not upgrade an installed bundle. See updating Claude Desktop for version checks and migration from JSON configuration. The AutoMem backend runs separately.

Then add the paste-ready Personal Preferences starter from templates/CLAUDE_DESKTOP_INSTRUCTIONS.md. That's it: Claude now has persistent memory and knows when to use it.

Other Platforms

Connect your AI tools to the AutoMem service you just started.

# Guided install - pick where AutoMem runs, verify it, write .env, and

configure your agents (Codex, Claude Code, Cursor, OpenClaw, Hermes)

npx @verygoodplugins/mcp-automem install

Every change is shown in a review plan before anything is written, and each modified file keeps a .bak backup. Add --dry-run to preview, --yes to apply non-interactively. See the Installation Guide for all flags.

setup is an alias of install. To print config snippets again later, use npx @verygoodplugins/mcp-automem config.

3. Platform-Specific Setup

For Claude Code (plugin — recommended):

# In Claude Code:

/plugin marketplace add verygoodplugins/mcp-automem /plugin install automem@verygoodplugins-mcp-automem

Claude Code prompts for your AutoMem URL and API key at enable time, bundles the MCP server and silent recall/store-tracking hooks, and auto-updates. Prefer hooks and permissions written directly into ~/.claude/ instead? Run npx @verygoodplugins/mcp-automem claude-code.

On Windows, the hook payload assumes a POSIX shell environment such as Git Bash, MSYS2, or WSL — only bash is required (the hooks are pure bash+sed).

For Cursor IDE:

Run the guided installer from your project and select Cursor:

npx @verygoodplugins/mcp-automem install

By default this installs the AutoMem Cursor plugin into ~/.cursor/plugins/local/automem (MCP server, session-start recall hook, and always-on rule); then open Cursor → Customize → Automem → Configure and enter your endpoint and key. --cursor-mode files instead writes ~/.cursor/mcp.json plus project memory rules and a session-start hook. Already have an endpoint? Use npx @verygoodplugins/mcp-automem cursor --endpoint https://your-automem.example.

The one-click button configures only the MCP server for a local AutoMem instance. Update its endpoint/key in Cursor if needed, then run the CLI above to add project rules and the hook:

Other platforms — Claude Desktop (one-click .mcpb above, plus the Personal Preferences template), OpenAI Codex, Hermes Agent, Grok Build, OpenClaw, Google Antigravity, and GitHub Copilot:

👉 [Full Installation Guide](INSTALLATION.md) for every platform's setup and verification steps

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Remote MCP via HTTP

An optional sidecar service (deployable to Railway or any Docker host) connects AutoMem to platforms that support remote MCP over Streamable HTTP or SSE — ChatGPT (Developer Mode connectors), Claude.ai web and Claude Mobile, and ElevenLabs Agents.

👉 [Remote MCP setup](INSTALLATION.md#remote-mcp-via-http-sidecar) for deployment, connect URLs, and per-platform screenshots.

Architecture

┌─────────────────────────────────────────────┐

│ Your AI Platforms │ │ Claude Desktop │ Cursor │ Claude Code │ └──────────────┬──────────────────────────────┘ │ MCP Protocol ▼ ┌──────────────────────────────────────────────┐ │ @verygoodplugins/mcp-automem (this repo) │ │ • Translates MCP calls → AutoMem API │ │ • Platform integrations & rules │ │ • Handles authentication │ └──────────────┬───────────────────────────────┘ │ HTTP API ▼ ┌──────────────────────────────────────────────┐ │ AutoMem Service (separate repo) │ │ github.com/verygoodplugins/automem │ │ ┌────────────┐ ┌────────────┐ │ │ │ FalkorDB │ │ Qdrant │ │ │ │ (Graph) │ │ (Vectors) │ │ │ └────────────┘ └────────────┘ │ └──────────────────────────────────────────────┘

This repo (mcp-automem):

• MCP client that connects AI platforms to AutoMem
• Platform-specific integrations (Cursor rules, Claude Code hooks, etc.)
• Setup wizards and configuration tools

[AutoMem service](https://github.com/verygoodplugins/automem):

• Backend memory service with graph + vector storage
• Deployment guides (local, Railway, Docker, production)
• API server with FalkorDB + Qdrant

Features

Core Memory Operations

• `store_memory` — Save memories with content, tags, importance, metadata. Two modes:
• Single (default): top-level content plus optional fields, including embedding, t_valid, t_invalid, custom id.
• Batch: memories: [...] (≤500 items) for bulk ingestion. Per-item id/embedding/t_valid/t_invalid are not supported in batch mode.
• `recall_memory` — Three modes selected by which params you pass:
• ID fetch: memory_id → fetches one memory by ID; updates last_accessed.
• Tag enumeration: tags + exhaustive: true → paginated exact-match listing for cleanup/audit workflows where ranked recall undercounts. Pair with limit (≤200) and offset; returns has_more, plus next_offset for the next page while more remain.
• Ranked retrieval (default): hybrid search across vector, keyword, tags, recency/state controls, score filters, and graph expansion. Supports state_mode, recency_bias, scope_fallback, expand_respect_tags, min_score, adaptive_floor, and diagnostics such as tag_scope, score_filter, query_time_ms, vector_search, and per-result outside_tag_scope/state_replaces.
• `associate_memories` — Create relationships (11 public authorable types; recall results may also include read-only system relations). Supports single-pair mode