Memorykit

Cognitive memory for AI coding assistants — persistent memory across sessions

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

🧠 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:

Code

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

Code

┌──────────────────────────────────────────────────────────────┐
│                   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:

csharp

"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

bash

# 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

csharp

// 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 for detailed setup.

🏗️ Architecture Highlights

Clean Architecture

Code

┌─────────────────────────────────────────┐
│    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:

Code

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Memorykit

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "memorykit": { "command": "npx", "args": ["-y", "memorykit"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/memorykit?style=directory)](https://allmcps.com/mcp/memorykit)

HTML Embed

<a href="https://allmcps.com/mcp/memorykit"><img src="https://allmcps.com/api/badge/memorykit?style=directory" alt="Memorykit on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

This directory is pre-filled from public sources. Claim via GitHub README, site badge, or DNS TXT to unlock edit access and the Official badge and attach your website — proof is checked automatically, then reviewed by our team.

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Memorykit →Install in Claude Desktop Install in Cursor Install in VS Code

Memorykit

Cognitive memory for AI coding assistants — persistent memory across sessions

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

🧠 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:

Code

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

Code

┌──────────────────────────────────────────────────────────────┐
│                   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:

csharp

"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

🚀 Quick Start

bash

# 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

csharp

// 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 for detailed setup.

🏗️ Architecture Highlights

Clean Architecture

Code

┌─────────────────────────────────────────┐
│    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:

Code

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

Production-Ready

Enterprise Features

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Memorykit

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "memorykit": { "command": "npx", "args": ["-y", "memorykit"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/memorykit?style=directory)](https://allmcps.com/mcp/memorykit)

HTML Embed

<a href="https://allmcps.com/mcp/memorykit"><img src="https://allmcps.com/api/badge/memorykit?style=directory" alt="Memorykit on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Memorykit →Install in Claude Desktop Install in Cursor Install in VS Code