Multi-User Support

Memori provides built-in multi-user and multi-process isolation through its attribution system. Each combination of entity, process, and session creates an isolated memory space — User A never sees User B's memories, and your support bot has different context than your sales bot.

Isolation Model

"Memori - Multi User Support"

What's Shared vs Isolated

Data	Scope
Facts	Per entity — shared across all processes
Preferences	Per entity
Skills	Per entity
Attributes	Per process
Conversations	Per entity + process + session
Sessions	Per entity + process
Knowledge Graph	Per entity

Examples

Multi-User Patterns

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from memori import Memori
from openai import OpenAI

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

client = OpenAI()
mem = Memori(conn=SessionLocal).llm.register(client)

# User A's conversations
mem.attribution(entity_id="user_alice", process_id="support_bot")
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "I prefer dark mode"}]
)

# User B's conversations — completely isolated
mem.attribution(entity_id="user_bob", process_id="support_bot")
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What are my preferences?"}]
)
# Bob will NOT see Alice's preferences

Common Patterns

Web Application

Set the entity ID from the authenticated user's session. Works with Flask, FastAPI, Django, or any framework.

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from memori import Memori
from openai import OpenAI

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

def handle_chat(user_id: str, message: str):
    client = OpenAI()
    mem = Memori(conn=SessionLocal).llm.register(client)
    mem.attribution(entity_id=user_id, process_id="web_assistant")

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

Multi-Agent System

Give each agent a unique process ID. Facts are shared across agents for the same user, but each maintains its own conversation history.

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from memori import Memori
from openai import OpenAI

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

def create_agent(user_id: str, agent_name: str):
    client = OpenAI()
    mem = Memori(conn=SessionLocal).llm.register(client)
    mem.attribution(entity_id=user_id, process_id=agent_name)
    return client

# Three agents, one user, shared facts
support = create_agent("user_alice", "support_agent")
sales = create_agent("user_alice", "sales_agent")
onboard = create_agent("user_alice", "onboarding_agent")