memorykit

# 🧠 MemoryKit

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

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

[Quick Start](docs/QUICKSTART.md) · [Documentation](docs/) · [Architecture](docs/ARCHITECTURE.md) · [API Docs](docs/API.md)

</div>

---

## 🐠 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:

```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](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-c