Engram MCP

Give your AI agents a memory they can trust. Engram lets your AI remember past conversations, facts, and decisions, so it feels more like a real teammate.

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

What is Engram?

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

• Reliable memory: Agents remember conversations, facts, and decisions with automatic knowledge graph extraction
• Easy setup: Connect via MCP in minutes. Works with Claude Code, Windsurf, Cursor, and other MCP clients
• Built-in controls: Organize memories into buckets, manage retention, and query with natural language

Free during public beta — No credit card required

Quick Setup

1. Get your API key

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

2. Add to your MCP client

Claude Code:

claude mcp add-json engram '{"type":"sse","url":"https://api.lumetra.io/mcp/sse","headers":{"Authorization":"Bearer <your-api-key>"}}'

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

{
  "mcpServers": {
    "engram": {
      "serverUrl": "https://api.lumetra.io/mcp/sse",
      "headers": {
        "Authorization": "Bearer <your-api-key>"
      }
    }
  }
}

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

{
  "mcpServers": {
    "engram": {
      "url": "https://api.lumetra.io/mcp/sse",
      "headers": {
        "Authorization": "Bearer <your-api-key>"
      }
    }
  }
}

3. Restart your client

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

Available Tools

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

Tool	Description
store_memory(content, bucket?)	Store a fact or piece of information
query_memory(question, bucket?)	Search memories using natural language with AI synthesis
list_buckets()	List available memory buckets
delete_memory(memory_id, bucket)	Delete a specific memory by ID
clear_memories(bucket)	Clear all memories in a bucket (destructive!)

Recommended Agent Prompt

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

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

Tools:
- store_memory(content, bucket?) - Store a fact or piece of information
- query_memory(question, bucket?) - Search memories using natural language
- list_buckets() - List available memory buckets
- delete_memory(memory_id, bucket) - Delete a specific memory
- clear_memories(bucket) - Clear all memories in a bucket (destructive!)

Policy:
- Query-first: before answering anything that may rely on prior context, call query_memory. Ground your answers in the results.
- Proactive storing: capture stable preferences, profile facts, project details, decisions, and outcomes. Keep each fact concise (1-2 sentences).
- Use buckets: organize memories by project or context (e.g., "work", "personal", "project-alpha").

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."

REST API

Engram also provides a REST API for programmatic access:

Base URL: https://api.lumetra.io

Authentication: Include your API key in the Authorization header:

curl -X POST https://api.lumetra.io/v1/buckets/default/memories \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"content": "Alice works at TechCorp"}'

Quick Example:

# Store a memory
curl -X POST https://api.lumetra.io/v1/buckets/work/memories \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"content": "Bob is the CEO of Acme Inc"}'

# Query your memories
curl -X POST https://api.lumetra.io/v1/query \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "Who is the CEO of Acme?", "buckets": ["work"]}'

See the full API documentation for all available endpoints.

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://api.lumetra.io — there's no local installation required.

Support

• Product site: lumetra.io
• Documentation: lumetra.io/docs
• Status: Free public beta (no credit card required)