Cartisien/engram-mcp
📇 🏠 🍎 🪟 🐧 - Persistent semantic memory for AI agents. SQLite-backed, local-first, zero config. Semantic search via Ollama embeddings (nomic-embed-text) with keyword fallback. remember, recall, history, forget, and stats tools. Works with Claude Desktop, Cursor, and any MCP client.
Quick Install
{
"mcpServers": {
"cartisien-engram-mcp": {
"command": "npx",
"args": [
"-y",
"cartisien-engram-mcp"
]
}
}
}Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.
Documentation Overview
@cartisien/engram-mcp
Persistent semantic memory for AI agents — MCP server powered by @cartisien/engram
Give any MCP-compatible AI client (Claude Desktop, Cursor, Windsurf) persistent memory that survives across sessions.
npx -y @cartisien/engram-mcp
What it does
Exposes 5 tools to any MCP client:
| Tool | Description |
|---|---|
remember | Store a memory with automatic embedding |
recall | Semantic search across stored memories |
history | Recent conversation history |
forget | Delete one memory, a session, or entries before a date |
stats | Memory statistics for a session |
Memories are stored in SQLite. Semantic search uses local Ollama embeddings (nomic-embed-text) — no API key, no cloud. Falls back to keyword search if Ollama isn't available.
Quick Start
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"engram": {
"command": "npx",
"args": ["-y", "@cartisien/engram-mcp"],
"env": {
"ENGRAM_DB": "~/.engram/memory.db"
}
}
}
}
Restart Claude Desktop. You'll see remember, recall, history, forget, and stats available as tools.
Cursor / Windsurf
Add to your MCP config:
{
"mcpServers": {
"engram": {
"command": "npx",
"args": ["-y", "@cartisien/engram-mcp"]
}
}
}
Configuration
| Env Var | Default | Description |
|---|---|---|
ENGRAM_DB | ~/.engram/memory.db | SQLite database path |
ENGRAM_EMBEDDING_URL | http://localhost:11434 | Ollama base URL for embeddings |
Local Embeddings (Recommended)
Install Ollama and pull the embedding model:
ollama pull nomic-embed-text
Semantic search activates automatically. Without Ollama, keyword search is used.
Example Usage
Once connected, your agent can:
remember(sessionId="myagent", content="User prefers TypeScript over JavaScript", role="user")
recall(sessionId="myagent", query="what are the user's coding preferences?", limit=5)
# Returns: [{ content: "User prefers TypeScript...", similarity: 0.82 }, ...]
history(sessionId="myagent", limit=10)
stats(sessionId="myagent")
# { total: 42, byRole: { user: 20, assistant: 22 }, withEmbeddings: 42 }
Part of the Cartisien Memory Suite
@cartisien/engram— core memory SDK@cartisien/engram-mcp— this package, MCP server@cartisien/extensa— vector infrastructure (coming soon)@cartisien/cogito— agent identity & lifecycle (coming soon)
MIT © Cartisien Interactive