The context window problem
Every LLM has a context window — a fixed-size buffer that holds the current conversation plus any injected context. When the window fills up, old messages get dropped. When the session ends, everything is lost.
Developers have tried two approaches to solve this: RAG (Retrieval-Augmented Generation) and AI memory. They're complementary but fundamentally different.
How RAG works
RAG retrieves relevant documents from a static knowledge base and injects them into the prompt:
# Traditional RAG pipeline
chunks = vector_db.search("How to deploy?", top_k=5)
context = "\n".join([c.text for c in chunks])
prompt = f"Context: {{context}}\n\nQuestion: How to deploy?"
response = llm.generate(prompt)RAG is great for: Documentation search, knowledge bases, FAQ bots, question-answering over static documents.
RAG falls short when: You need to remember past interactions, learn user preferences, or track decisions made across sessions.
How AI memory works
AI memory learns from conversations and builds a cumulative understanding over time:
# AI memory with Mengram
from mengram import Mengram
m = Mengram(api_key="key")
# Each conversation enriches the memory
m.add("User prefers concise answers with code examples", user_id="bob")
m.add("Bob debugged CORS issue on staging server today", user_id="bob")
# Next session: the AI knows Bob's history
profile = m.profile(user_id="bob")
# "Bob is a developer who prefers concise answers with code examples.
# Recently debugged a CORS issue on staging..."Key differences
Source of truth: RAG draws from documents you upload. AI memory draws from conversations that happen naturally.
Static vs dynamic: RAG knowledge is fixed until you re-index. AI memory continuously evolves with every interaction.
What vs who: RAG answers "what does the documentation say?" AI memory answers "what does this user need?"
Types: RAG stores chunks of text. AI memory stores structured knowledge — facts (semantic), events (episodic), and workflows (procedural).
When to use both
The best AI agents combine RAG and memory. RAG provides domain knowledge. Memory provides user context. Together, you get an agent that knows your product and knows your user.
# Combine RAG + AI memory
docs = rag.search(user_query)
memories = mengram.search(user_query, user_id=user_id)
profile = mengram.profile(user_id=user_id)
prompt = f"""System: {{profile}}
Relevant docs: {{docs}}
User memories: {{memories}}
Question: {{user_query}}"""Getting started
Replace your pure-RAG setup with Mengram in 3 lines: pip install mengram-ai, get a free API key, and call m.add() after each conversation. Your AI will start learning from every interaction.