Engram

Give your AI a perfect memory.

Engram remembers everything you tell it—names, relationships, preferences, conversations—and recalls exactly what's relevant when you need it. No cloud. No API keys. Just memory that works.

Works with any LLM that supports MCP (Model Context Protocol)—Claude, GPT, Gemini, local models, and more.

An engram is a unit of cognitive information imprinted in a physical substance—the biological basis of memory.

What It Does

Tell your AI about your world:

"My colleague Sarah is allergic to shellfish and prefers window seats. She's leading the Q1 product launch."

Later, ask:

"I'm booking a team lunch and flights for the offsite—what should I know?"

Engram connects the dots—avoid seafood restaurants, book Sarah a window seat, and she's probably busy with the launch—and gives your AI the context to truly help.

It's not just search. It's understanding.

Quick Start

Install

npm install -g @199-bio/engram

Add to Your MCP Client

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "engram": {
      "command": "npx",
      "args": ["-y", "@199-bio/engram"]
    }
  }
}

Claude Code:

claude mcp add engram -- npx -y @199-bio/engram

Other MCP clients — point to the engram command. It speaks standard MCP over stdio.

That's it. Your AI now has memory.

How to Use

Just talk naturally. Your AI will remember what matters.

Storing Memories

Say things like:

• "Remember that Sarah is allergic to shellfish"
• "My anniversary is March 15th"
• "I prefer morning meetings, never schedule anything before 9am"

Recalling

Just ask:

• "What do you know about Sarah?"
• "When is my anniversary?"
• "What are my meeting preferences?"

Your AI automatically searches its memory and uses what's relevant.

The Knowledge Graph

Engram doesn't just store text—it understands relationships:

• People: Sarah, John, Dr. Martinez
• Places: Office, Conference Room A, Seattle HQ
• Organizations: Acme Corp, Stanford, FDA
• Connections: Sarah → works at → Acme Corp
• Observations: Sarah is allergic to shellfish

This means when you ask about Sarah's work, Engram knows to also surface relevant information about Acme Corp.

What Makes It Special

The Nine Tools

Your AI gets these capabilities:

Examples

Personal Assistant

You: "Remember that my anniversary is March 15th and my partner loves Italian food"
AI: ✓ Stored. I've noted this as an important date with preferences.

You: "I need to plan something special next month"
AI: Your anniversary is coming up on March 15th! Given your partner's love for
    Italian food, perhaps a nice Italian restaurant reservation?

Relationship Context

You: "My friend David lives in Austin and always recommends great coffee shops."
AI: ✓ Stored. Created entity: David (person), with observation about Austin and coffee expertise.

You: "I'm visiting Austin next week"
AI: David is in Austin—he'd probably have great coffee shop recommendations!
    Want me to remind you to ask him?

Professional Memory

You: "John Chen is our VP of Engineering. He prefers async communication and hates meetings before 11am."
Claude: ✓ Stored. Created entity: John Chen (person, VP of Engineering).

You: "I need to schedule a sync with John"
Claude: Given John's preferences, I'd suggest a late morning slot, maybe 11:30am,
        or an async Loom video if it doesn't require real-time discussion.

Privacy

Your memories stay on your machine.

• All data stored locally in ~/.engram/
• No cloud services, no external APIs
• No telemetry, no tracking
• You own your data completely

Technical Details

Query: "What should I know about Sarah?"
  │
  ├── BM25 → finds "Sarah" in memories
  ├── ColBERT → finds semantically related content
  └── Graph → Sarah → works at → Acme Corp → Q1 launch
  │
  └── RRF Fusion → Best combined results

engram/
├── src/
│   ├── index.ts           # MCP server
│   ├── storage/
│   │   └── database.ts    # SQLite + FTS5
│   ├── graph/
│   │   ├── extractor.ts   # Entity extraction
│   │   └── knowledge-graph.ts
│   └── retrieval/
│       ├── colbert.ts     # ColBERT wrapper
│       ├── colbert-bridge.py  # Python RAGatouille
│       └── hybrid.ts      # RRF fusion

git clone https://github.com/199-biotechnologies/engram.git
cd engram
npm install
npm run build

# Install globally from local build
npm install -g .

pip install ragatouille torch

{
  "mcpServers": {
    "engram": {
      "command": "engram",
      "env": {
        "ENGRAM_DB_PATH": "/custom/path/engram.db"
      }
    }
  }
}

Roadmap

• Core MCP server
• Hybrid search (BM25 + ColBERT)
• Knowledge graph
• Entity extraction
• Temporal memory decay
• Memory consolidation
• Export/import
• Web dashboard

Author

Boris Djordjevic Founder, 199 Biotechnologies

License

MIT — use it however you want.

Built with care by 199 Biotechnologies