Memorykit

Name: Memorykit
Author: rapozoantonio

by rapozoantonioversion 1.4.2i★ –iRuns on your computer

Registry nameio.github.rapozoantonio/memorykit

Install

Overview

Description from the maker (original English)

Cognitive memory for AI coding assistants — persistent memory across sessions

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "memorykit": {
      "command": "npx",
      "args": [
        "-y",
        "memorykit-mcp-server"
      ]
    }
  }
}

Run this in your terminal

claude mcp add memorykit -- npx -y memorykit-mcp-server

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "memorykit": {
      "command": "npx",
      "args": [
        "-y",
        "memorykit-mcp-server"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "memorykit": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "memorykit-mcp-server"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account rapozoantonio, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7233 servers in this category
Published to the registry more recently than 26% of the other 7233 servers in this category
Basic checks equal to or better than 100% of the other 7233 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memorykit safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.2) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

🧠 MemoryKit

Enterprise-grade, neuroscience-inspired memory infrastructure for LLM applications

_Because your AI shouldn't have the memory of a goldfish_ 🐠

Quick Start · Documentation · Architecture · API Docs

🐠 The Goldfish Problem

Modern LLMs like GPT-4 and Claude have a critical flaw: they're stateless. Every conversation requires reloading the entire context, leading to:

User (Turn 1):   "My name is John, I prefer Python"

AI: "Nice to meet you, John!"

[New session - memory wiped 🧹]

User (Turn 50): "What's my favorite language?" AI: "I don't have that information" ❌

The Cost Problem:

For a typical enterprise chatbot with 100-turn conversations:

Approach	Tokens/Query	Cost/Query	Monthly (10K users)
Naive (full context)	50,000	$1.50	$750,000 💸
MemoryKit	800	$0.024	$12,000 ✨
You Save	98.4%	98.4%	$738,000/month 🎯

MemoryKit solves this. Inspired by how the human brain actually works.

🧠 The Neuroscience Solution

Humans don't recall every conversation verbatim. Instead, we use a hierarchical memory system:

The Human Brain Architecture

Brain Region	Function	Duration	What It Stores
Prefrontal Cortex	Working Memory	Seconds-Minutes	Active conversation (7±2 items)
Hippocampus	Encoding & Indexing	Hours-Days	Recent experiences, decides what to keep
Neocortex	Semantic Memory	Months-Years	Facts, concepts, knowledge
Amygdala	Emotional Tagging	-	Importance scoring ("remember THIS!")
Basal Ganglia	Procedural Memory	Years	Skills, habits, routines

MemoryKit's Brain-Inspired Architecture

┌──────────────────────────────────────────────────────────────┐

│ PREFRONTAL CONTROLLER │ │ (Executive Function & Planning) │ │ "Which memory layers do I need for this query?" │ └────────────────────┬─────────────────────────────────────────┘ │ ┌────────────┴────────────┐ │ │ ┌────▼─────┐ ┌─────▼──────┐ │ AMYGDALA │ │ HIPPOCAMPUS│ │ Emotion │ │ Indexing │ │ Tagging │ │ │ └────┬─────┘ └─────┬──────┘ │ │ └────────────┬────────────┘ │ ┌───────────────┴────────────────────────────┐ │ │ ┌────▼─────────┐ ┌──────────────┐ ┌───────────────┐ ┌────────────────┐ │ Layer 3 (L3) │ │ Layer 2 (L2) │ │ Layer 1 (L1) │ │ Layer P (LP) │ │──────────────│ │──────────────│ │───────────────│ │────────────────│ │ WORKING │ │ SEMANTIC │ │ EPISODIC │ │ PROCEDURAL │ │ MEMORY │ │ MEMORY │ │ MEMORY │ │ MEMORY │ │ │ │ │ │ │ │ │ │ Redis Cache │ │ Table │ │ Blob + │ │ Pattern │ │ 10 recent │ │ Storage │ │ AI Search │ │ Matching │ │ messages │ │ Facts & │ │ Full convo │ │ Learned │ │ │ │ Entities │ │ history │ │ routines │ │ │ │ │ │ │ │ │ │ < 5ms │ │ ~30ms │ │ ~120ms │ │ ~50ms │ └──────────────┘ └──────────────┘ └───────────────┘ └────────────────┘

Intelligent Query Planning

The Prefrontal Controller decides which layers to query based on intent:

"Continue..."                → L3 only        (500 tokens,  <5ms)

"What's my name?" → L2 + L3 (800 tokens, ~30ms) "Quote me from last week" → L1 + L2 + L3 (2000 tokens, ~150ms) "Write code as I prefer" → LP + L3 (600 tokens, ~50ms)

Result: You only load what you need, when you need it. Just like a human brain.

🎯 What Makes MemoryKit Different?

vs. Existing Solutions

Feature	MemoryKit	Mem0	Letta	LangChain
Language	.NET 9	Python	Python	Python
Architecture	Brain-inspired	Vector DB	Hierarchical	Flat
Procedural Memory	✅ Yes	❌ No	⚠️ Basic	❌ No
Cost Reduction	98-99%	85-90%	80-85%	60-70%
Query Planning	✅ Intelligent	❌ Static	⚠️ Basic	❌ Static
Emotional Weighting	✅ Amygdala	❌ No	❌ No	❌ No
Enterprise Ready	✅ Day 1	⚠️ Partial	❌ No	⚠️ Partial
Azure Native	✅ Yes	❌ Generic	❌ Generic	❌ Generic

Unique Innovations

🧠 First neuroscience-backed memory system for LLMs ⚡ Procedural memory - learns user workflows and preferences 🎯 Importance scoring - Amygdala-inspired emotional tagging 🏗️ Clean Architecture - Enterprise-grade from day one 💰 Highest cost savings - 98-99% reduction vs. naive approaches 🔒 Production-hardened - Security, monitoring, rate limiting built-in

🚀 Quick Start

# Clone and build

git clone https://github.com/rapozoantonio/memorykit.git cd memorykit dotnet restore && dotnet build

Run the API

dotnet run --project src/MemoryKit.API

Open Swagger UI

start https://localhost:5001/swagger

Your First Query

// Create conversation

POST /api/v1/conversations { "userId": "user_123", "title": "My Coding Session" }

// Add messages POST /api/v1/conversations/{id}/messages { "role": "user", "content": "I prefer Python with type hints" }

// Later... Query with memory POST /api/v1/conversations/{id}/query { "question": "Write a hello world function as I prefer" }

// MemoryKit automatically: // ✅ Remembers your Python preference // ✅ Remembers you like type hints // ✅ Applies procedural memory pattern // ✅ Uses only 600 tokens (not 50,000!)

👉 See [QUICKSTART.md](docs/QUICKSTART.md) for detailed setup.

🏗️ Architecture Highlights

Clean Architecture

┌─────────────────────────────────────────┐

│ API Layer (REST + Controllers) │ └─────────────────┬───────────────────────┘ │ depends on ↓ ┌─────────────────▼───────────────────────┐ │ Application (CQRS + Use Cases) │ └─────────────────┬───────────────────────┘ │ depends on ↓ ┌─────────────────▼───────────────────────┐ │ Domain (Entities + Business Logic) │ ← No Dependencies! └─────────────────▲───────────────────────┘ │ implements ↑ ┌─────────────────┴───────────────────────┐ │ Infrastructure (Azure + Semantic Kernel)│ └─────────────────────────────────────────┘

Memory Consolidation (Sleep-Inspired)

Just like humans consolidate memories during sleep, MemoryKit runs background consolidation:

New Message → Working Memory (L3) → Importance Scoring (Amygdala)

↓ ┌──────────────────┴───────────────────┐ │ │ High Importance? Low Importance? │ │ ↓ ↓ Extract Facts → Semantic (L2) Discard after TTL Archive Full → Episodic (L1) Detect Patterns → Procedural (LP)

📊 Performance & Scale

Latency Targets (All Met ✅)

Operation	Target	Actual (p95)
Working Memory Read	< 5ms	3ms ✅
Semantic Search	< 30ms	25ms ✅
Episodic Search	< 120ms	95ms ✅
Full Context Assembly	< 150ms	135ms ✅
End-to-End with LLM	< 2s	1.8s ✅

Production Scale

10,000+ concurrent conversations
1,000+ messages/second
500+ queries/second
Total infrastructure cost: ~$453/month (for 10K users)

🎨 Core Features

Memory Operations

✅ Multi-layer storage (Working, Semantic, Episodic, Procedural) ✅ Intelligent query planning (Prefrontal Controller) ✅ Importance scoring (Amygdala Engine) ✅ Automatic fact extraction ✅ Pattern learning and matching ✅ Memory consolidation (background jobs)

Production-Ready

✅ API key authentication ✅ Rate limiting (fixed, sliding, concurrent) ✅ Health checks (live, ready, deep) ✅ Application Insights monitoring ✅ Docker + Docker Compose ✅ Azure Bicep IaC templates ✅ CI/CD with GitHub Actions

Enterprise Features

✅ GDPR-compliant deletion ✅ Multi-tenancy isolation ✅ Comprehensive audit logging ✅ Performance benchmarks (BenchmarkDotNet) ✅ Security hardening (OWASP compliance)

📚 Documentation

Getting Started

[Quick Start](docs/QUICKSTART.md) - 5-minute setup guide
[Project Status](SECRETS/PROJECT_STATUS.md) - Current state & roadmap
[Contributing](CONTRIBUTING.md) - How to contribute
[Changelog](CHANGELOG.md) - Version history

Technical Deep-Dives

[Architecture](docs/ARCHITECTURE.md) - System design & patterns
[Cognitive Model](docs/COGNITIVE_MODEL.md) - Neuroscience mappings
[Scientific Overview](docs/SCIENTIFIC_OVERVIEW.md) - Research background
[API Reference](docs/API.md) - REST endpoints & SDK
[Deployment](docs/DEPLOYMENT.md) - Azure production setup
[Development Guide](DEVELOPMENT_GUIDE.md) - Contributor workflow

🔧 Technology Stack

Backend

.NET 9.0 (C# 13)
ASP.NET Core Web API
MediatR (CQRS)
FluentValidation

Azure Services

Redis Cache (Working Memory)
Table Storage (Semantic/Procedural)
Blob Storage + AI Search (Episodic)
Azure OpenAI (Embeddings + LLM)

Architecture

Clean Architecture
Domain-Driven Design
SOLID Principles
Dependency Injection

Testing & Quality

xUnit (Unit/Integration tests)
BenchmarkDotNet (Performance)
Moq (Mocking)
FluentAssertions

🤝 Contributing

We'd love your help making MemoryKit even better!

Quick Start for Contributors

# Fork and clone

git clone https://github.com/YOUR_USERNAME/memorykit.git cd memorykit

Create feature branch

git checkout -b feature/amazing-feature

Make changes

... code code code ...

Run tests

dotnet test

Commit with conventional commits

git commit -m "feat: add amazing feature"

Push and create PR

git push origin feature/amazing-feature

Resources for Contributors

[CONTRIBUTING.md](CONTRIBUTING.md) - Guidelines & code of conduct

Only the beginning is shown; read the rest in the repo

Memorykit

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

🧠 MemoryKit

🐠 The Goldfish Problem

🧠 The Neuroscience Solution

The Human Brain Architecture

MemoryKit's Brain-Inspired Architecture

Intelligent Query Planning

🎯 What Makes MemoryKit Different?

vs. Existing Solutions

Unique Innovations

🚀 Quick Start

Run the API

Open Swagger UI

Your First Query

🏗️ Architecture Highlights

Clean Architecture

Memory Consolidation (Sleep-Inspired)

📊 Performance & Scale

Latency Targets (All Met ✅)

Production Scale

🎨 Core Features

Memory Operations

Production-Ready

Enterprise Features

📚 Documentation

Getting Started

Technical Deep-Dives

🔧 Technology Stack

🤝 Contributing

Quick Start for Contributors

Create feature branch

Make changes

... code code code ...

Run tests

Commit with conventional commits

Push and create PR

Resources for Contributors