Engram Memory MCP

🚀 Engram MCP

Give your AI agents a memory they can trust. Engram enables your AI to recall past conversations, facts, and decisions, making it feel more like a real teammate.

This repository holds configuration templates for connecting MCP clients to Engram, a hosted memory service for AI agents.

🚀 Quick Start

1. Get your API key

Sign up at lumetra.io to obtain your API key.

2. Add to your MCP client

Claude Code:

claude mcp add-json engram '{"type":"http","url":"https://engram.lumetra.io","headers":{"X-API-Key":"<your-api-key>"}}'

Windsurf (~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "engram": {
      "serverUrl": "https://engram.lumetra.io",
      "headers": {
        "X-API-Key": "<your-api-key>"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json or .cursor/mcp.json):

{
  "mcpServers": {
    "engram": {
      "url": "https://engram.lumetra.io",
      "headers": {
        "X-API-Key": "<your-api-key>"
      }
    }
  }
}

3. Restart your client

Your MCP client will now have access to Engram memory tools.

✨ Features

Engram is a hosted MCP server that offers reliable memory for AI agents:

• Reliable memory: Agents can remember conversations, facts, and decisions and can explain why certain results were chosen.
• Easy setup: Connect via MCP in minutes. It's compatible with Claude Code, Windsurf, Cursor, and other MCP clients.
• Built-in controls: Manage retention and cleanup with simple tools, no additional plumbing needed.

Free during public beta • No credit card required

💻 Usage Examples

Basic Usage

Once connected, your agent will have access to these memory tools:

• store_memory(content) — Store a single memory
• store_memories(contents[]) — Store multiple memories at once
• search_memories(query, max_candidates?, hints?) — Search stored memories
• memory_index(page?, limit?) — Browse all memories
• delete_by_event(event_id, dry_run?) — Delete specific memories
• explain_retrieval(retrieval_id, verbosity?) — Understand why results were chosen

Advanced Usage

Add this to your agent's system prompt to encourage effective memory usage:

You have Engram Memory. Use it aggressively to improve continuity and personalization.

Tools:
- store_memory(content)
- store_memories(contents[])
- search_memories(query, max_candidates?, hints?)
- memory_index(page?, limit?)
- delete_by_event(event_id, dry_run?)
- explain_retrieval(retrieval_id, verbosity?)

Policy:
- Retrieval-first: before answering anything that may rely on prior context, call search_memories (use max_candidates 20–80 for broad queries). Ground answers in results.
- Aggressive storing: capture stable preferences, profile facts, recurring tasks, decisions, and outcomes. Keep each item ≤1–2 sentences. Batch at end of turn with store_memories; use store_memory for single critical facts.
- Cleanup: when info changes, find and delete the old event (memory_index or search_memories → delete_by_event), then store the corrected fact.

Style for stored content: short, declarative, atomic facts.
Examples:
- "User prefers dark mode."
- "User timezone is US/Eastern."  
- "Project Alpha deadline is 2025-10-15."

If asked how results were chosen, call explain_retrieval with the retrieval_id returned by search_memories.

📚 Documentation

Use Cases

Teams use Engram for:

• Support with prior context: Carry forward last ticket, environment, plan, and promised follow‑ups
• Code reviews with context: Store ADRs, owner notes, brittle areas, and post‑mortems as memories
• Shared metric definitions: Keep definitions, approved joins, and SQL snippets in one place
• On‑brand content, consistently: Centralize voice and approved claims for writers

About This Repository

This repository contains:

• This README with setup instructions for popular MCP clients
• server.json - MCP server manifest following the official schema

The server.json file uses the official MCP server schema and can be used by MCP clients that support remote server discovery. For manual configuration, use the client-specific examples above.

The actual Engram service runs at https://engram.lumetra.io — there's no local installation required.

📄 Support

• Product site: lumetra.io
• Documentation: See setup instructions above
• Status: Free public beta (no credit card required)