Skip to main content

Local, persistent memory for AI development workflows

Project description

Engram

Local, persistent memory for AI development workflows.

Your AI assistant finally remembers your entire codebase.


The Problem

  • Cursor forgets your codebase after 5 minutes
  • Claude loses context mid-conversation
  • Copilot suggests code that conflicts with your architecture
  • You keep re-explaining the same things

The Solution

Engram creates a persistent memory layer that any AI can access via MCP.

# Index your project
engram init ~/my-project

# Configure Claude/Cursor
engram setup

# Done. Your AI now remembers everything.

Features

  • Local-first — Your code never leaves your machine
  • GPU accelerated — Fast indexing with MPS (Mac) or CUDA
  • MCP native — Works with Claude Desktop, Cursor, and any MCP client
  • Incremental updates — Only re-indexes what changed
  • Crash-proof — Saves progress, resumes if interrupted
  • Portable — Copy your "memory cartridge" anywhere

Supported Files

Type Extensions
Code .py .js .ts .tsx .go .rs .java .rb .php .swift .c .cpp
Docs .pdf .txt .md .rst
Config .json .yaml .yml .toml
Images .png .jpg .jpeg (OCR)

Quick Start

Install

pip install engram-dev

Or from source:

git clone https://github.com/Tobbiloba/engram.git
cd engram
pip install -e .

Index Your Project

# Index current folder
engram init

# Index specific folder
engram init ~/projects/my-app

# Custom output name
engram init ~/projects/my-app -o my_brain

Configure MCP

engram setup

This auto-detects Claude Desktop and Cursor, and configures them.

Query From Terminal

engram query "how does authentication work"

Watch for Changes

engram watch ~/projects/my-app

Runs in background, auto-updates index when files change.


How It Works

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Files     │────▶│   Chunks    │────▶│  Vectors    │────▶│   FAISS     │
│  Code/Docs  │     │ 1000 chars  │     │ 384 dims    │     │   Index     │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
                                                                   │
                                                                   ▼
┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│     AI      │◀────│   Results   │◀────│  Semantic   │◀────│ MCP Server  │
│   Claude    │     │  Top 5      │     │   Search    │     │             │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
  1. Index — Engram scans your files and creates chunks
  2. Embed — Each chunk becomes a 384-dimension vector (local model, no API)
  3. Store — Vectors saved to FAISS index (fast similarity search)
  4. Query — MCP server exposes query_memory tool to AI assistants
  5. Search — AI asks questions, gets relevant code/docs back

CLI Commands

Command Description
engram init [folder] Index a folder
engram query "..." Search from terminal
engram watch [folder] Start background watcher
engram status Show index stats
engram setup Auto-configure MCP

Manual MCP Configuration

If engram setup doesn't work, add manually:

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "engram": {
      "command": "/path/to/engram/venv/bin/python",
      "args": ["/path/to/engram/server.py", "/path/to/your_engram"]
    }
  }
}

Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "engram": {
      "command": "/path/to/engram/venv/bin/python",
      "args": ["/path/to/engram/server.py", "/path/to/your_engram"]
    }
  }
}

Requirements

  • Python 3.9+
  • ~2GB disk space (for model + index)
  • For OCR: brew install tesseract poppler

Tech Stack

  • Embeddings: sentence-transformers/all-MiniLM-L6-v2 (runs locally)
  • Vector Store: FAISS
  • GPU: MPS (Apple Silicon) / CUDA (NVIDIA) / CPU fallback
  • Protocol: MCP (Model Context Protocol)

Roadmap

  • Local vector indexing
  • MCP server
  • GPU acceleration
  • Incremental updates
  • Background watcher
  • Temporal memory (what changed recently?)
  • Git-aware context
  • Architecture insights
  • Desktop app

See ROADMAP.md for details.


Contributing

PRs welcome! See CONTRIBUTING.md for guidelines.


License

MIT — Use freely, modify freely, share freely.


Built for developers who are tired of re-explaining their codebase.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

engram_dev-0.1.3.tar.gz (27.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

engram_dev-0.1.3-py3-none-any.whl (29.7 kB view details)

Uploaded Python 3

File details

Details for the file engram_dev-0.1.3.tar.gz.

File metadata

  • Download URL: engram_dev-0.1.3.tar.gz
  • Upload date:
  • Size: 27.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for engram_dev-0.1.3.tar.gz
Algorithm Hash digest
SHA256 7f3d04c208ebc5a6ee5177c5335e55f39d8d96ca5d36a5d1bf028a191698c0cd
MD5 482f88ae740b4a6ca8435c77654fe412
BLAKE2b-256 d7ec034b107b92eeaa80a4435e4321306d948bac4bd502aa27eac5b9a06626ce

See more details on using hashes here.

File details

Details for the file engram_dev-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: engram_dev-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 29.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for engram_dev-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 79de01d384660c6f8fcc46373c677eefa0d4627b1ef60460268dc452bf9acb6a
MD5 bf2adf8f4c7ee34910c869e2ccff9cc7
BLAKE2b-256 b2949d5aac42dd2e7892953f2701f46dc51079438ff53e316f35953996b3d3ec

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page