redis-memory-marketplace

redis-memory-mcp

Persistent cross-session memory for AI agents — semantic search + KV store with auto-expiry

Long-term self-managing memory for LLM agents (Cursor, Claude Code, etc.) via MCP.

Features

• Semantic search (mem_*) — save facts with vector embeddings, find by meaning
• Key-value store (kv_*) — instant O(1) lookup for named facts
• Auto-expiry — TTL resets on every read; unused facts expire, popular ones live forever
• Multi-project — tag-based isolation between projects
• Self-contained — Docker stack: Redis Stack + HuggingFace TEI embeddings + MCP server

Quick Start

# 1. Clone
git clone https://github.com/yourname/redis-memory-mcp
cd redis-memory-mcp

# 2. Start infrastructure
docker compose up -d

# 3. Add to your AI tool's MCP config

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "redis-memory-mcp": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "REDIS_URL=redis://host.docker.internal:6379/0",
        "-e", "EMBED_URL=http://host.docker.internal:8081",
        "-e", "INDEX_NAME=idx:memories",
        "redis-memory-mcp"
      ]
    }
  }
}

Claude Code

Works automatically via .mcp.json in the repo root when using as a Claude plugin.

Tools (8 total)

Key-Value Storage — instant lookup

Tool	Description
kv_set(key, value, tags?, ttl_days?)	Store a named fact
kv_get(key)	Retrieve by exact key (refreshes TTL)
kv_delete(key)	Delete by key
kv_list(tag?, pattern?)	List entries with filtering

Semantic Memory — vector search

Tool	Description
mem_save(text, code?, tags?, ttl_days?)	Save fact with embedding
mem_search(query, tags?, top_k?)	Find by meaning (refreshes TTL on hits)
mem_list(limit?, tag?)	Browse by recency
mem_delete(memory_id)	Delete by ID

TTL & Auto-Expiry

TTL	Use case
ttl_days=90 (default)	Normal facts — expire if unused for 90 days
ttl_days=0	Permanent — API keys, critical config
ttl_days=7	Short-lived context

• TTL resets on every read — frequently accessed facts never expire
• Redis volatile-lru evicts least-recently-used facts under memory pressure
• Only facts with TTL can be evicted; permanent facts (ttl_days=0) are safe

Architecture

┌─────────────────┐     ┌────────────────────┐     ┌───────────────────┐
│  Cursor / Claude │────▶│  redis-memory-mcp  │────▶│   Redis Stack     │
│  (MCP client)    │ MCP │  (Python, stdio)   │     │   + RediSearch    │
└─────────────────┘     └────────┬───────────┘     │   + HNSW index    │
                                 │                  └───────────────────┘
                                 ▼
                        ┌────────────────────┐
                        │  HuggingFace TEI   │
                        │  (embeddings, CPU) │
                        └────────────────────┘

• Redis Stack — RediSearch module with HNSW vector index (768 dim, cosine)
• TEI — paraphrase-multilingual-mpnet-base-v2 (multilingual, runs on CPU)
• MCP server — Python FastMCP over stdio

Environment Variables

Variable	Default	Description
REDIS_URL	redis://localhost:6379/0	Redis connection URL
EMBED_URL	http://localhost:8081	TEI embeddings endpoint
INDEX_NAME	idx:memories	Redis search index name
DEFAULT_TTL	7776000 (90 days)	Default TTL in seconds

Redis UI

RedisInsight is included at http://localhost:8001 — browse keys, run queries, analyze memory usage.

Plugin Structure

redis-memory-mcp/
├── .claude-plugin/marketplace.json   # Marketplace registry
├── .claude/settings.json             # Auto-load config
├── redis-memory-mcp/                 # Claude plugin
│   ├── .claude-plugin/
│   │   ├── plugin.json               # Plugin metadata
│   │   └── mcp.json                  # MCP server docs
│   ├── .mcp.json                     # Runtime MCP config
│   ├── hooks/project-init.json       # Session start hook
│   └── skills/persistent-memory/
│       └── SKILL.md                  # Memory management skill
├── server/                           # MCP server source
│   ├── memory_mcp.py
│   ├── Dockerfile
│   └── pyproject.toml
├── docker-compose.yaml               # Full stack
└── README.md

License

MIT