What can you do with this server?

engram-mcp provides persistent semantic memory for AI agents, storing and retrieving memories across sessions using SQLite and local Ollama embeddings (nomic-embed-text) — no API keys or cloud services required. Store memories * remember: Save session memory entries with automatic embedding and graph extraction * remember_user: Store user-scoped memories (facts, preferences) that persist across all sessions Retrieve memories * recall: Semantic search across session memories (falls back to keyword search); can blend in cross-session user facts via userId * recall_user: Search or retrieve all user-scoped memories from any session context * history: Get recent conversation history for a session in chronological order * graph: Query extracted entity relationships and source memories (requires ENGRAM_GRAPH=1) Manage memories * forget: Delete session memories — all, by ID, or before a date * forget_user: Delete user-scoped memories by ID, date, or all at once * consolidate: Compress old working session memories into dense long-term summaries via LLM, archiving originals (supports dry-run preview) * consolidate_user: Consolidate user-scoped working memories into long-term summaries Monitor memories * stats: Session memory counts broken down by role, tier (working/long_term/archived), and graph data * user_stats: Memory statistics for a specific user across all sessions

Which integrations are available for this server?

Integrates with local Ollama instances to generate semantic embeddings for memories, enabling AI agents to perform semantic search and retrieval. Uses SQLite for local persistent storage of agent memories, conversation history, and session statistics.

engram-mcp

by Cartisien

Overview Schema Related Servers Score Discussions

TypeScript

Local

@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.

Related MCP server: Mnemo Cortex

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

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

1Releases (12mo)

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