I Built a Code Archaeology Engine for AI — Here's Why Claude and Cursor Keep Forgetting Your Architecture
TL;DR: AI coding assistants have zero architectural memory.
Every session starts from scratch. I built LORE — an open-source MCP server with 13 analyzers that gives your AI deep understanding of your codebase structure. Works with Claude Desktop, Cursor, and Windsurf.
The Problem: AI Has Amnesia
Every time you start a new AI coding session, the same ritual happens.
You explain: "We use PostgreSQL because..."
Then: "Auth uses JWT with 24h expiry..."
And: "Our API follows REST with /api/v1/..."
The AI nods, understands, writes some code. Session ends.
Next session? Complete amnesia. You repeat everything. Again. And again.
After the 50th time, I stopped explaining and started building.
What I Built: LORE MCP Server
LORE (Layout-Oriented Reverse Engineering) is a code archaeology engine that reads your TypeScript/JavaScript codebase and extracts deep architectural intelligence — automatically.
No manual documentation. No prompts to paste. No CLAUDE.md files to maintain.
npx lore-mcp init
That's the setup. One command. LORE scans your entire project, runs 13 parallel analyzers, and feeds structured results to your AI assistant through the Model Context Protocol.
What Does LORE Actually Analyze?
LORE isn't a simple dependency checker. It runs 13 deep analyzers in parallel:
| # | Analyzer | What It Finds |
|---|---|---|
| 1 | AST Parser | Full TypeScript/TSX structure via ts-morph |
| 2 | Dependency Graph | Every import, export, re-export in your project |
| 3 | Circular Dependencies | Import cycles ranked by severity |
| 4 | Dependency Direction | Layer violations (controller importing DB code) |
| 5 | Shannon Entropy | Complexity scoring per file |
| 6 | Hotspot Analysis | Files that change too often (git churn) |
| 7 | Import Impact | Downstream blast radius of every import |
| 8 | Type Safety Scorer |
any usage, explicit types, strictness grades |
| 9 | Hidden Coupling | Implicit dependencies through shared types |
| 10 | AI Recommendations | Prioritized fix suggestions (P0–P3) |
| 11 | Tooling Config | ESLint, Prettier, tsconfig validation |
| 12 | Breaking Changes | High-risk deprecation patterns |
| 13 | Gap Analysis | Missing error handling, testing gaps |
Here's what lore status looks like on a real project:
$ lore status
LORE MCP Server v0.1.6
────────────────────────────────────
Architecture Analysis Complete
├─ Overall Score: 87/100
├─ Type Safety: 92/100
├─ Tooling Config: 78/100
└─ Architecture: 91/100
Circular Dependencies: 3 found (2 critical)
Hotspot Modules: 5 detected
Hidden Coupling: 8 links
AI Recommendations: 12 suggestions
Analysis complete — 0 errors, 0 crashes
Real output from lore status on Express.js project
How It Works: 3 Steps
Step 1: Scan Your Codebase
LORE recursively walks your project tree, parsing every .ts and .tsx file. It reads your AST, maps imports/exports, tracks types, and understands your configuration files.
Step 2: Run 13 Analyzers in Parallel
All 13 analyzers fire simultaneously through a plugin pipeline:
- Coupling matrices are computed
- Dependency graphs are mapped
- Hotspot scoring runs against your git history
- Type safety is evaluated across every file
- Circular dependencies are detected and ranked
Step 3: Feed Results to Your AI
Via MCP, Claude Desktop, Cursor, or Windsurf queries LORE on-demand. Your AI now reasons about real structural data — not guesses.
Ask Claude:
- "What are the hidden coupling risks in my codebase?"
- "Which modules are the biggest hotspots?"
- "Show me all circular dependencies and their severity."
- "What are the P0 recommendations?"
LORE runs the analysis and returns structured data. Claude interprets it and gives you actionable answers.
MCP Integration (60-Second Setup)
Add LORE to your MCP client configuration:
Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"lore": {
"command": "npx",
"args": ["-y", "lore-mcp"]
}
}
}
Cursor — Add to your MCP settings:
{
"mcpServers": {
"lore": {
"command": "npx",
"args": ["-y", "lore-mcp"]
}
}
}
Restart your AI tool. That's it. Your AI now has architectural memory.
LORE generates dependency graphs showing module relationships
Battle-Tested on Real Projects
LORE isn't a toy. I tested it on 16 major open-source TypeScript projects:
| Project | Files Analyzed | Result |
|---|---|---|
| Express | 42 | 100% Pass |
| Next.js | 68 | 100% Pass |
| NestJS | 38 | 100% Pass |
| Fastify | 55 | 100% Pass |
| Prisma | 45 | 100% Pass |
| Zod | 35 | 100% Pass |
| TypeORM | 60 | 100% Pass |
| React | 73 | 100% Pass |
16 projects. 100% pass rate. Zero crashes.
Cross-Platform: Runs Everywhere
LORE is built on Node.js with zero native dependencies. If Node.js runs on your system, LORE runs too.
- macOS (Intel + Apple Silicon M1/M2/M3/M4)
- All Linux distros (Ubuntu, Debian, Kali, Fedora, Arch, CentOS, Alpine)
- Windows 10/11
- CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
Only requirement: Node.js 18+. No Docker, no VM, no Rosetta.
The Tech Stack
- Language: TypeScript 5.5+
- AST Parsing: ts-morph 21
- Protocol: Model Context Protocol (MCP) SDK 1.0
- Transport: Stdio (Claude Desktop / IDE compatible)
- Validation: Zod schemas
- Output: ANSI terminal, Markdown, SARIF
CLI Commands
lore [path] # Analyze project (default: cwd)
lore init # Extract architectural decisions
lore status # View decisions by category
lore diff # Diff against saved baseline
lore doctor # Environment + tooling check
lore doctor --fix # Auto-fix project setup
lore watch # Watch + re-analyze on change
lore mcp inspect # Inspect MCP server setup
lore mcp config # Claude Desktop config snippet
lore version # Show version
Open Source & Local-First
LORE is 100% open source under MIT license. No data leaves your machine. No cloud. No API keys. No telemetry.
Everything runs locally. Your code never gets sent anywhere.
What's Next
- [ ] LORE INTEGRITY — verify decisions are actually implemented
- [ ] VS Code Extension
- [ ] LORE NETWORK — share anonymous architectural patterns
- [ ] Plugin API — write your own analyzers
Try It Now
# No install needed
npx lore-mcp init
# Or install globally
npm install -g lore-mcp
lore status
GitHub: github.com/EliotShift/lore-mcp
npm: npmjs.com/package/lore-mcp
Docs: eliotshift.github.io/lore-mcp
Built with care from Morocco.
If you found this useful, give LORE a star on GitHub. It helps more than you think.
Top comments (2)
The amnesia framing is right, but there is a layer below it I keep hitting. Even with LORE feeding architectural context, different agents working on the same project (Claude Code, Cursor, a background CI agent) each build their own model from scratch. They do not share LORE's output or write back when one of them notices something new. Are you treating LORE as a read-only index, or could it become the write path too, where agents post findings back so the next session starts from an updated graph instead of re-scanning?
This is exactly the right question. LORE today is read-only —
but you're describing LORE NETWORK, which is the next major
milestone. The plan is:
You're not the only one hitting this wall. It's the biggest gap
in AI-native development right now. LORE NETWORK is designed
to solve exactly this. Stay tuned it's coming in v0.2