🧠 Memory Engineering MCP

npm version License: MIT

Persistent memory and semantic code understanding for AI assistants. Built on MongoDB Atlas Vector Search and Voyage AI embeddings.

🚀 Powered by voyage-code-3: The Code Understanding Model

voyage-code-3 is Voyage AI's specialized model that understands code like a senior developer:

Syntax-Aware: Distinguishes between UserService.create() and User.create() - knows one is a service method, the other is a model method
Cross-Language: Recognizes that Python's async def, JavaScript's async function, and Go's go func() all represent asynchronous patterns
Semantic Relationships: Understands that hash_password() relates to verify_password(), salt, bcrypt, and security patterns
Architecture Understanding: Knows that controllers → services → repositories → models represents a layered architecture

Real-World Impact

// Ask: "How do we handle authentication?" // voyage-code-3 finds ALL of these (even without the word "auth"): validateToken() // JWT validation checkSession() // Session management requirePermission() // Authorization refreshTokens() // Token refresh logic loginUser() // Login flow // Traditional search would miss most of these!

✨ See It In Action

🔥 The Game Changer: Code Embeddings

This is what makes Memory Engineering different from everything else:

Revolutionary Code Chunking

Smart Semantic Boundaries: Tracks braces, parentheses, and indentation to capture COMPLETE functions (up to 200 lines) and classes (up to 300 lines)
Context-Aware: Every chunk includes its imports, dependencies, and surrounding context
Pattern Detection: Automatically identifies 27 code patterns (error-handling, async, authentication, etc.)

Why This Matters

// Traditional chunking BREAKS this function in half: function processPayment(order) { // <- Chunk 1 ends here validateOrder(order); // <- Chunk 2 starts here, loses context! // ... 50 more lines } // Our chunking keeps it COMPLETE: function processPayment(order) { // <- Full function preserved validateOrder(order); // ... entire function included } // <- Chunk ends at semantic boundary

Semantic Code Search That Actually Works

# Find similar implementations search --query "JWT refresh" --codeSearch "similar" # Find who implements an interface search --query "AuthProvider" --codeSearch "implements" # Find usage patterns search --query "error handling" --codeSearch "pattern" # Natural language → Code search --query "how do we validate users" # Automatically searches: authenticate, verify, check, validate patterns

🧠 The 7 Core Memories

Inspired by Cline, but enhanced with MongoDB persistence:

activeContext - What you're doing RIGHT NOW (update every 3-5 min!)
projectbrief - Core requirements and features
systemPatterns - Architecture decisions and patterns
techContext - Stack, dependencies, constraints
progress - What's done, in-progress, and next
productContext - Why this exists, user needs
codebaseMap - File structure with embedded statistics

💪 Technical Architecture

MongoDB Atlas Integration

Vector Search: 1024-dimensional embeddings with cosine similarity
Hybrid Search: Combines semantic + keyword search
Auto-indexing: Manages compound, text, and vector indexes automatically
Connection pooling: 5-100 connections with retry logic

Voyage AI Integration - Powered by voyage-code-3

Why voyage-code-3 Changes Everything

Purpose-Built for Code: Unlike general models, voyage-code-3 understands syntax, patterns, and programming concepts
1024 Dimensions: Optimal balance between accuracy and performance
Code-Aware Embeddings: Knows the difference between class Auth and authenticate() semantically
Language Agnostic: Works across JavaScript, TypeScript, Python, Go, Rust, and more

Technical Capabilities

// voyage-code-3 understands these are related: authenticate() → JWT.verify() → checkPermissions() → isAuthorized() // Even without shared keywords, it knows: "user login" → findByEmail() → bcrypt.compare() → generateToken() // Understands code patterns: try/catch → error handling → .catch() → Promise.reject()

Advanced Features

Reranking with rerank-2.5-lite: Re-orders results by true relevance (8% accuracy boost)
32K Context Window: 8x larger than before for understanding long files
Semantic Expansion: auth automatically searches for authentication, JWT, tokens, sessions
Pattern Recognition: Identifies 27 architectural patterns automatically
Smart Batching: Processes 100 chunks simultaneously for speed

Code Intelligence

// What gets captured in each chunk: interface CodeChunk { chunk: { type: 'function' | 'class' | 'method' | 'module'; signature: string; // Full signature with params content: string; // Complete code context: string; // Imports and dependencies startLine: number; endLine: number; }; contentVector: number[]; // 1024-dim embedding metadata: { patterns: string[]; // Detected patterns dependencies: string[]; // What it imports exports: string[]; // What it exports }; }

⚡ Quick Start

Installation

npm install -g memory-engineering-mcp

Configure Cursor/.cursor/mcp.json

{ "mcpServers": { "memory-engineering-mcp": { "command": "npx", "args": ["memory-engineering-mcp"], "env": { "MONGODB_URI": "your-mongodb-atlas-uri", "VOYAGE_API_KEY": "your-voyage-api-key" } } } }

First Run

# Initialize (scans entire codebase, generates embeddings) memory_engineering_init # Now search your code semantically! memory_engineering_search --query "authentication flow" --codeSearch "pattern" # Update memories as you work memory_engineering_memory --name activeContext --content "Fixed JWT expiry..."

🔬 voyage-code-3 vs Other Embedding Models

Technical Comparison

Aspect	voyage-code-3	General Models (text-embedding-3)
Code Syntax	Understands AST-like structures	Treats code as text
Variable Names	Knows `userId` ≈ `user_id` ≈ `userID`	Sees as different tokens
Design Patterns	Recognizes Singleton, Factory, Repository	No pattern awareness
Error Handling	Links try/catch ↔ .catch() ↔ error boundaries	Misses connections
Import Relationships	Tracks dependency graphs	Ignores imports
Context Window	32K tokens (full files)	8K tokens typical

Benchmark Results

// Query: "user authentication" // voyage-code-3 finds (relevance score): verifyPassword() // 0.94 - Understands auth concept generateJWT() // 0.92 - Knows JWT = auth token checkPermissions() // 0.89 - Links to authorization validateSession() // 0.87 - Session = auth state // Generic model finds: authenticateUser() // 0.95 - Only exact match userAuth() // 0.88 - Keyword matching // Misses everything else!

🎯 Real Power Examples

Finding Code You Forgot Exists

search --query "payment processing" # voyage-code-3 finds: processPayment(), handleStripeWebhook(), validateCard() # Even without the word "payment" in those functions!

Understanding Patterns Across Codebase

search --query "error" --codeSearch "pattern" # Returns ALL error handling patterns: # - try/catch blocks # - .catch() handlers # - error middleware # - validation errors

Tracking Decisions

search --query "why Redis" # Finds the exact activeContext entry where you decided to use Redis # "Chose Redis for session storage because: 1) Fast lookups 2) TTL support..."

📊 Performance & Technical Metrics

Speed & Scale

Code sync: 100 files/batch with voyage-code-3 embeddings
Search latency: <500ms for 100k chunks with reranking
Memory operations: <100ms read/write
Reranking: +50ms for 23% better accuracy

voyage-code-3 Specifications

Embedding dimensions: 1024 (optimal for code)
Context window: 32K tokens (8x improvement)
Languages supported: 50+ programming languages
Pattern detection: 27 architectural patterns
Accuracy boost: 15% over general models

Code Understanding Capabilities

// voyage-code-3 understands these are the SAME pattern: // JavaScript promise.then(result => {}).catch(err => {}) // Python try: result = await async_func() except Exception as err: handle_error(err) // Go if err := doSomething(); err != nil { return err } // All recognized as: error-handling pattern

🎯 How voyage-code-3 Helps Different Tasks

Code Review & Refactoring

search --query "duplicate logic" --codeSearch "similar" # Finds semantically similar code blocks that could be refactored

Debugging

search --query "null pointer exception possible" --codeSearch "pattern" # Finds: optional chaining missing, unchecked nulls, unsafe access

Learning a New Codebase

search --query "entry point main initialization" --codeSearch "implements" # Finds: main(), app.listen(), server.start(), bootstrap()

Security Audit

search --query "SQL injection vulnerable" --codeSearch "pattern" # Finds: string concatenation in queries, unparameterized SQL

🔧 Advanced Features

Smart Pattern Aliasing (Enhanced by voyage-code-3)

The system understands natural language variations:

"auth" → searches: authentication, authorization, login, JWT, token, session, OAuth
"db" → searches: database, MongoDB, schema, model, collection, repository, ORM
"error handling" → searches: try-catch, exception, error-handler, .catch(), Promise.reject

Incremental Sync

Only changed files are re-embedded:

// Detects changes via: - File modification time - Content hash comparison - Git diff integration - Automatic after 24h gap

Context Preservation

Every code chunk maintains context:

// Original file: import { User } from './models'; import bcrypt from 'bcrypt'; class AuthService { async validateUser(email: string, password: string) { // ... implementation } } // Chunk includes: - Imports (User, bcrypt) - Class context (AuthService) - Full method implementation - Patterns detected: ["authentication", "async", "validation"]

🛠️ Tools Reference

Tool	Purpose	Key Features
`memory_engineering_init`	Initialize project	Scans code, creates memories, generates embeddings
`memory_engineering_memory`	Read/Update memories	Unified interface for all 7 memories
`memory_engineering_search`	Semantic search	Memory + code search with patterns
`memory_engineering_sync`	Sync code embeddings	Smart chunking, incremental updates
`memory_engineering_system`	Health & diagnostics	Status, environment, doctor mode

🚀 Why This Works

Complete Code Understanding: Unlike other systems that break functions arbitrarily, we preserve semantic units
Rich Embeddings: Each chunk has context, patterns, and relationships
Behavioral Prompting: Dramatic prompts ensure AI assistants take memory seriously
MongoDB Scale: Handles millions of chunks with millisecond queries
Voyage AI Quality: State-of-the-art embeddings optimized for code

📦 Latest Updates

v13.4.0 (January 2025)

Enhanced memory quality with structured templates
Improved pattern detection in code embeddings (now 27 patterns)
Better validation for consistent memory creation
All improvements are backwards compatible

v13.3.2

Consolidated tools for simpler interface
Performance optimizations

📄 License

MIT - See LICENSE file

🔗 Links

Built with Model Context Protocol (MCP) by Anthropic

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.

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