Why LLMs forget everything

Large language models like GPT-4, Claude, and Gemini are stateless by default. Every conversation starts from scratch. Ask the same question twice, and the model has no idea you asked before. This is a fundamental limitation — the context window is temporary storage, not memory.

Context windows have grown (128K+ tokens), but they still reset between sessions. RAG (Retrieval-Augmented Generation) helps by fetching relevant documents, but it only retrieves static information — it doesn't learn from interactions.

What is AI memory?

AI memory is a persistent storage layer that lets LLMs and AI agents remember information across conversations. Instead of resetting every session, AI memory continuously extracts, stores, and retrieves knowledge from past interactions.

Think of it like the difference between a goldfish and a human. Without memory, every conversation is new. With memory, your AI builds a cumulative understanding of users, projects, and context over time.

Three types of AI memory

Human memory isn't one thing — it's three distinct systems. The most effective AI memory systems mirror this structure:

1. Semantic memory (facts)

What the user knows, prefers, and believes. Examples: "User prefers Python over JavaScript", "User is a senior engineer at Acme Corp", "User is allergic to peanuts."

Most AI memory tools only implement this type. Mem0, for instance, is primarily a semantic memory store.

2. Episodic memory (events)

What happened, when, and in what context. Examples: "User debugged a Redis connection error on Feb 12", "User decided to migrate from AWS to GCP last week."

Episodic memory captures the narrative of interactions — not just facts, but the story of what happened.

3. Procedural memory (workflows)

How to do things, step by step. Examples: "When deploying, run tests first, then build, then push to staging." Procedural memory captures learned workflows that evolve from experience.

This is the rarest type — learn more about all three types.

How AI memory works in practice

Here's how you add AI memory to any LLM application with Mengram:

from mengram import Mengram

m = Mengram(api_key="your-key")

# After each conversation, add to memory
m.add("I prefer dark mode and use VS Code", user_id="alice")

# Before generating a response, search memory
results = m.search("What IDE does Alice use?", user_id="alice")

# Or generate a full Cognitive Profile
profile = m.profile(user_id="alice")
# Returns a ready-to-use system prompt with everything known about Alice

The profile() call is unique to Mengram — it generates a complete system prompt from all stored memories, making any LLM instantly personalized. Read more about Cognitive Profile.

AI memory vs RAG

RAG and AI memory solve different problems. RAG retrieves from static document collections. AI memory learns from dynamic conversations. You often need both — read our detailed comparison.

Getting started

The fastest way to add AI memory to your application:

pip install mengram-ai

Get a free API key at mengram.io and start building. Works with any LLM — OpenAI, Anthropic, Google, open-source models. Also available as an MCP server for Claude Desktop.