Engram Mcp Server

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, explainable memory for AI agents:

• Reliable memory: Agents remember conversations, facts, and decisions, with automatic knowledge graph extraction
• Explainable retrieval: Every answer cites the memories and graph edges that justified it
• Three-engine retrieval: BM25 + vector search + knowledge graph, fused and reranked
• Bring your own model: All LLM calls route through your provider — no inference markup
• Built-in controls: Organize memories into buckets, manage retention, and query with natural language

Free tier: 10K stored memories and 50K retrievals per month — no credit card required. See pricing for paid tiers.

Quick Setup

1. Get your API key

Sign up at lumetra.io to create an account and generate an API key.

Some clients (Claude.ai web, ChatGPT) use OAuth instead of a pasted key — see those sections below.

2. Add Engram to your MCP client

MCP endpoint: https://mcp.lumetra.io/mcp/sse

Claude Code

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

Claude.ai web (OAuth — no key paste)

In Claude settings → Connectors → Add custom connector, paste:

https://mcp.lumetra.io/mcp/sse

You'll be redirected through Lumetra to authorize the connection. No API key required.

ChatGPT web (OAuth — Connector-capable plans)

In ChatGPT settings → Add custom MCP connector, paste:

https://mcp.lumetra.io/mcp/sse

Same OAuth flow as Claude.ai.

Cursor

~/.cursor/mcp.json or .cursor/mcp.json:

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

Windsurf

~/.codeium/windsurf/mcp_config.json:

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

Windsurf accepts both url and serverUrl for remote MCP servers. We use url here to match the other clients on this page.

OpenCode

opencode.json:

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

OpenClaw

Once the skill is live on ClawHub:

openclaw skill add lumetra-engram
# or
clawhub install lumetra-engram

For now, install manually from lumetra-io/engram-openclaw-skill:

mkdir -p .openclaw/skills
curl -fsSL https://codeload.github.com/lumetra-io/engram-openclaw-skill/tar.gz/refs/heads/main \
  | tar -xz --strip-components=2 -C .openclaw/skills engram-openclaw-skill-main/skills/engram
export ENGRAM_API_KEY="eng_live_..."

3. Restart your client

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

Available Tools

Once connected, your agent has these memory tools:

Tool	Description
store_memory(content, bucket?)	Store a fact or piece of information (defaults to bucket "default")
query_memory(question, bucket?)	Search memories using natural language, with AI synthesis and per-memory explanations
list_memories(bucket, limit?)	List memories in a bucket, newest first (limit 1–100, default 20)
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!)

Multi-bucket query fusion (passing several buckets in one call) is available on the REST /v1/query endpoint and in the official SDKs. The MCP query_memory tool currently accepts a single bucket per call.

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_memories(bucket, limit?) - List memories in a bucket, newest first
- 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 2026-10-15."

REST API

Engram also provides a REST API for programmatic access from any HTTP client (Vercel AI SDK, LangChain, LlamaIndex, Mastra, CrewAI, AutoGen, n8n, your own scripts).

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 $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://mcp.lumetra.io (MCP) and https://api.lumetra.io (REST) — there's no local installation required.

Support

• Product site: lumetra.io
• Documentation: lumetra.io/docs
• Pricing: lumetra.io/pricing
• Contact: [email protected]