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 "}}'

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 "
      }
    }
  }
}

Windsurf

`~/.codeium/windsurf/mcp_config.json`:

{
  "mcpServers": {
    "engram": {
      "url": "https://mcp.lumetra.io/mcp/sse",
      "headers": {
        "Authorization": "Bearer "
      }
    }
  }
}

> 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 "
      }
    }
  }
}

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:

> 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: support@lumetra.io