Skip to main content
Glama
PraveenJayaprakash-JP

RepoMemory

by PraveenJayaprakash-JP
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

🎯 The Problem

AI coding agents are only as good as the context they receive. Yet the files that control that context — CLAUDE.md, .claudeignore, AGENTS.md, CURSOR.md — are maintained manually, inconsistently, and with no tooling support. When your code evolves but your context files don't, your AI agent works with outdated information.

RepoMemory solves this. It's the first tool that treats AI context quality as a product — automatically scanning, auditing, generating, and monitoring the intelligence layer of your repositories.

🎥 Demo

✨ Features

🔍 Multi-Agent Audit

Score your AI context files across 7 quality dimensions (Architecture, Commands, Conventions, Off-limits, Testing, Deployment, Freshness) for 6 AI coding agents:

Agent

File

Score

CLAUDE.md

0–100

.cursor/rules.mdc

0–100

.windsurf/rules.md

0–100

GEMINI.md

0–100

AGENTS.md

0–100

AIDER.md

0–100

🧠 Core Capabilities

🔌 Integrations

Interface

What it does

Quick Start

🌐 Web App

Full-featured dashboard with scan, audit, generate, compare

npm run dev → localhost:3000 · Live demo

💻 CLI

Run scans, generate, and check from terminal

npm run cli scan .

🤖 MCP Server

Call RepoMemory tools from any AI agent

8 tools, stdio transport

🖥️ VS Code

Sidebar panel with project tree + webview audit

Open vscode/, F5

🔄 GitHub Action

Auto-check PRs for context drift

uses: ./github/actions/repomemory-check

🚀 Quick Start

# Clone
git clone https://github.com/PraveenJayaprakash-JP/repomemory.git
cd repomemory

# Install & configure
npm install
cp .env.example .env.local   # Add your AI API key

# Start the web app
npm run dev                   # → http://localhost:3000

# Live demo: https://repomemory-omega.vercel.app

# Or use the CLI
npm run cli scan /path/to/your/repo
npm run cli generate /path/to/your/repo --apply

🐳 One-Click Fix

# Generate + apply all context files in one command
npm run cli generate /path/to/repo --apply

🤖 MCP for AI Agents

Add to your opencode.json or claude_desktop_config.json:

{
  "mcpServers": {
    "repomemory": {
      "command": "npx",
      "args": ["tsx", "mcp/src/index.ts"],
      "cwd": "/path/to/repomemory"
    }
  }
}

Available MCP tools:

Tool

Description

scan_repo

Scan + audit a repo folder

generate_pack

Generate context files via AI

check_drift

Detect stale context

apply_pack

Generate + write to repo

explain_repo

Explain repo architecture

suggest_context_updates

Get recommendations

repo_health

Overall health score

explain_codebase

Comprehensive codebase explanation

📸 Screenshots

⚙️ Configuration

Variable

Default

Description

AI_PROVIDER_API_KEY

—

Your AI provider API key

AI_PROVIDER_BASE_URL

https://opencode.ai/zen/go/v1

OpenAI-compatible endpoint

AI_MODEL

deepseek-v4-flash

Model for generation tasks

🏗️ Architecture

repomemory/
├── app/              # Next.js 15 — 16 routes across 7 pages
├── lib/              # 15 core modules
│   ├── scanner.ts    # File system traversal + detection
│   ├── auditor.ts    # 7-dimension quality scoring
│   ├── generator.ts  # AI context pack generation
│   ├── drift.ts      # Drift detection + risk scoring
│   ├── graph.ts      # Architecture graph builder
│   ├── discovery.ts  # Deep architecture discovery
│   ├── adr.ts        # ADR generation from git history
│   ├── brain.ts      # Persistent knowledge store
│   ├── changelog.ts  # Git change summaries
│   ├── ai.ts         # OpenAI-compatible API wrapper
│   ├── prompts.ts    # AI prompt templates
│   ├── storage.ts    # File-based persistence
│   ├── export.ts     # ZIP export
│   └── types.ts      # Shared TypeScript types
├── cli/              # CLI tool (3 commands)
├── mcp/              # MCP server (8 tools)
├── vscode/           # VS Code extension
├── .github/          # GitHub Action + CI workflow
└── tests/            # 24 unit tests

🔄 CI Integration

Add to your GitHub Actions workflow:

- name: Check AI Context
  uses: PraveenJayaprakash-JP/repomemory/.github/actions/repomemory-check@main
  with:
    ai-api-key: ${{ secrets.AI_PROVIDER_API_KEY }}
    fail-on-drift: true
    min-score: 60

This checks every PR for:

  • ✅ AI Readiness score

  • ✅ Context drift detection

  • ✅ Risk level assessment

  • ✅ Automatic PR comments with findings

  • ✅ CI/CD enforcement (optional min-score gate)

🛠️ Development

npm test              # 24 unit tests
npm run build         # Production build (2.5s, 16 routes)
npm run cli -- scan . # Scan this repo
npm run cli -- generate . --apply  # Generate context for this repo

🗺️ Roadmap

  • Multi-agent context generation (6 AI agents)

  • Advanced drift detection with risk scoring

  • Architecture discovery engine

  • ADR generator

  • Project Brain (persistent knowledge)

  • explain_codebase MCP tool

  • Public hosted version

  • Team dashboard with multi-repo support

  • VSCode marketplace release

  • GitHub marketplace action

🤝 Contributing

PRs welcome! RepoMemory is built with OpenCode Go — the same AI coding agent it helps configure.

  1. Fork the repo

  2. Create your feature branch: git checkout -b feat/amazing

  3. Commit: git commit -m 'feat: add amazing feature'

  4. Push: git push origin feat/amazing

  5. Open a PR

📄 License

MIT © PraveenJayaprakash-JP

This server cannot be installed

A
license - permissive license
-
quality - not tested
A
maintenance

How are these scores calculated?

Maintenance

–Maintainers
<1hResponse time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/PraveenJayaprakash-JP/repomemory'

If you have feedback or need assistance with the MCP directory API, please join our Discord server