Context-Memory-Layer

A persistent, context-aware memory layer for AI agents and applications.

The Problem

Large language models (LLMs) are stateless by nature. Every new prompt is processed in isolation — the model forgets what it said moments ago, what it learned in prior sessions, and what worked or failed before. This forces applications to re-inject context manually, results in fragmented and repetitive reasoning, and makes agents incapable of building on past experience.

The Solution

The Context Memory Layer bridges this gap by giving AI agents a persistent, structured memory that is actively retrieved and injected at inference time. Instead of a single stateless prompt, agents get a curated slice of relevant context drawn from everything the system has ever seen or learned — just like how a human draws on working memory, experience, and learned facts to make decisions.

Memory & Context Layer

The memory layer is organized into four complementary memory types:

• Short-term: Current conversation context, working memory
• Long-term: Vector embeddings, knowledge bases, conversation history
• Episodic: Past interactions and outcomes
• Semantic: Facts, skills, world knowledge

These are complemented by retrieval mechanisms that select and surface the most relevant context for any given query.

Why It Matters

• Continuity: Agents maintain coherent, informed behavior across sessions
• Contextual relevance: Retrieval surfaces the right knowledge at the right time
• Learning over time: Systems improve by accumulating and applying past outcomes
• Reduced redundancy: Relevant context is injected once, precisely, rather than dumped wholesale

Repository Structure

RAG-Labs/
├── Agentic-RAG/           # Agentic RAG with tool use and multi-step reasoning
├── Graph-and-Vector/      # Hybrid graph + vector retrieval
├── Graph-RAG/             # Graph-based retrieval augmented generation
├── HybridRAG/             # Hybrid RAG combining sparse + dense retrieval
├── LLM-Wiki/              # LLM knowledge base with Wikipedia-style retrieval
├── MultiVector-RAG/       # Multi-vector retrieval (LangChain + ColBERT)
├── OCR-RAG/               # OCR-powered RAG for scanned documents
└── Vectorless-RAG/        # RAG without vector embeddings

MongoDB-Labs/
└── Lab 1 - Student Records Lookup/  # MongoDB Atlas CRUD, queries, indexing

License

MIT