Mengram MCP Server

Give your AI agents memory that actually learns

Website · Get API Key · Docs · Console · Examples

pip install mengram-ai   # or: npm install mengram-ai

from mengram import Mengram
m = Mengram(api_key="om-...")           # Free key → mengram.io

m.add([{"role": "user", "content": "I use Python and deploy to Railway"}])
m.search("tech stack")                  # → facts
m.episodes(query="deployment")          # → events
m.procedures(query="deploy")            # → workflows that evolve from failures

Claude Code — Zero-Config Memory

Two commands. Claude Code remembers everything across sessions automatically.

pip install mengram-ai
mengram setup              # Sign up + install hooks (interactive)

Or manually: export MENGRAM_API_KEY=om-... → mengram hook install

What happens:

Session Start  →  Loads your cognitive profile (who you are, preferences, tech stack)
Every Prompt   →  Searches past sessions for relevant context (auto-recall)
After Response →  Saves new knowledge in background (auto-save)

No manual saves. No tool calls. Claude just knows what you worked on yesterday.

mengram hook status     # check what's installed
mengram hook uninstall  # remove all hooks

Why Mengram?

Every AI memory tool stores facts. Mengram stores 3 types of memory — and procedures evolve when they fail.

Get Started in 30 Seconds

1. Install

pip install mengram-ai

2. Setup (creates account + installs Claude Code hooks)

mengram setup

Or get a key manually at mengram.io and export MENGRAM_API_KEY=om-...

3. Use

from mengram import Mengram

m = Mengram(api_key="om-...")

# Add a conversation — auto-extracts facts, events, and workflows
m.add([
    {"role": "user", "content": "Deployed to Railway today. Build passed but forgot migrations — DB crashed. Fixed by adding a pre-deploy check."},
])

# Search across all 3 memory types at once
results = m.search_all("deployment issues")
# → {semantic: [...], episodic: [...], procedural: [...]}

# Upload a PDF — auto-extracts memories using vision AI
result = m.add_file("meeting-notes.pdf")
# → {"status": "accepted", "job_id": "job-...", "page_count": 12}

# Poll for completion
m.job_status(result["job_id"])

// Node.js — pass a file path
await m.addFile('./report.pdf');

// Browser — pass a File object from <input type="file">
await m.addFile(fileInput.files[0]);

# REST API
curl -X POST https://mengram