engram-mcp

@cartisien/engram-mcp

<a href="https://glama.ai/mcp/servers/Cartisien/engram-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/Cartisien/engram-mcp/badge" /> </a>

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

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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.

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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"]
    }
  }
}

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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.

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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 }

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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)

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MIT © Cartisien Interactive