Most AI systems today feel intelligent, but only for a moment. They can answer questions, generate text, and even reason —
yet they forget everything once the conversation ends.
This is the biggest limitation of modern AI: lack of long-term memory.
A new open-source project called Memori , currently trending on GitHub, solves this exact problem by giving AI agents real, persistent memory.
If you discovered this article through a YouTube reel, this post is the full professional guide explaining
what Memori is, why memory matters, and how to set it up correctly.
The Real Problem With Today’s AI Systems
Large Language Models (LLMs) are stateless by design. Even advanced models like GPT or Claude do not remember past interactions
unless developers manually pass context every time.
This leads to common issues:
- AI forgets users and preferences
- Support bots repeat the same questions
- Context is lost across sessions
- Applications become expensive and slow as chat history grows
Without memory, AI cannot learn, adapt, or improve over time.
What Is Memori?
Memori is an open-source memory layer designed specifically for AI agents and LLM-powered applications.
You can explore the project and its documentation on GitHub .
Instead of storing raw chat logs, Memori stores structured memories — things like user preferences,
important facts, ongoing projects, and decisions.
These memories can then be recalled intelligently using semantic search and natural language queries.
How Memori Works (Conceptual Overview)
1. Structured Memory Storage
Memori does not blindly save conversations. It stores meaningful information as memory objects, each with metadata such as:
importance, timestamp, user identity, and context.
2. Vector-Based Recall
Memories are embedded and retrieved using semantic similarity, allowing the AI to fetch only relevant information
in milliseconds.
3. Natural Language Memory Queries
Instead of building complex filters, you can ask Memori questions like:
- What did the user say last week?
- What project is the user working on?
- What preferences does this customer have?
4. Automatic Forgetting
Low-importance and outdated memories are automatically removed, keeping the system fast and relevant.
Memori Setup Guide (Step-by-Step)
Prerequisites
- Python 3.9 or higher
- An LLM provider (OpenAI, Anthropic, or local models)
- A database (SQLite for local testing, Postgres/MySQL for production)
Step 1: Install Memori
pip install memoriStep 2: Run the One-Time Setup
This step optimizes Memori for faster execution. If skipped, it will run automatically on first use.
python -m memori setupStep 3: Build Storage Schema
This prepares the database to store memory correctly. In production, this is typically run via CI/CD.
Memori(conn=db_session_factory).config.storage.build()Quick Example: AI That Actually Remembers
import os
import sqlite3
from memori import Memori
from openai import OpenAI
def get_connection():
return sqlite3.connect("memori.db")
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
memori = Memori(conn=get_connection).llm.register(client)
memori.attribution(entity_id="user_123", process_id="ai-agent")
memori.config.storage.build()
client.chat.completions.create(
model="gpt-4.1-mini",
messages=[{"role": "user", "content": "My favorite color is blue"}]
)
memori.augmentation.wait()
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
memori = Memori(conn=get_connection).llm.register(client)
memori.attribution(entity_id="user_123", process_id="ai-agent")
response = client.chat.completions.create(
model="gpt-4.1-mini",
messages=[{"role": "user", "content": "What is my favorite color?"}]
)
print(response.choices[0].message.content)Even though this is a new session, the AI correctly recalls the user’s preference using memory stored via Memori.
Final Thoughts
Reasoning makes AI smart in the moment. Memory is what makes AI intelligent over time.
By introducing a dedicated memory layer, tools like
Memori
enable AI systems to remember users, context, and decisions — and continuously improve with usage.
Coming From YouTube?
If you watched the reel that mentioned Memori, this article is the complete technical and conceptual breakdown behind it.
If you want deeper implementation patterns or production-ready architecture guidance, comment:
MEMORI.