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 (auto-generated UUID)
Knowledge Graph	Per entity

Examples

Multi-User Patterns

from memori import Memori
from openai import OpenAI

client = OpenAI()
mem = Memori().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. Works with Flask, FastAPI, Django, or any web framework.

from memori import Memori
from openai import OpenAI

def handle_chat(user_id: str, message: str):
    client = OpenAI()
    mem = Memori().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 entity, but each agent maintains its own conversation history.

from memori import Memori
from openai import OpenAI

def create_agent(user_id: str, agent_name: str):
    client = OpenAI()
    mem = Memori().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")