Use Cases

Memori is designed for any application where AI agents need to remember context across conversations. Here are the most common use cases.

Customer Support Chatbots

Build support bots that remember customer history, preferences, and past issues. No more "Can you repeat your account number?" — Memori recalls context automatically.

Benefits:

Remember customer preferences and history
Recall previous support tickets and resolutions
Personalize responses based on past interactions
Track issues across multiple sessions

from memori import Memori
from openai import OpenAI

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

# Each customer gets their own memory space
mem.attribution(
    entity_id="customer_456",
    process_id="support_bot"
)

# Memori automatically recalls relevant context
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{
        "role": "user",
        "content": "I'm having that issue again"
    }]
)
# Memori injects: "Customer previously reported
# login timeout issues on 2024-01-15"

Personalized AI Assistants

Create AI assistants that learn and adapt to each user over time. Memori builds a profile of preferences, skills, and context that makes every interaction more relevant.

Benefits:

Learn coding preferences and tech stack
Remember project context across sessions
Adapt communication style to user preferences
Build long-term user profiles automatically

from memori import Memori
from anthropic import Anthropic

client = Anthropic()
mem = Memori().llm.register(client)

mem.attribution(
    entity_id="developer_789",
    process_id="code_assistant"
)

# Over time, Memori learns:
# - "Uses Python 3.12 with FastAPI"
# - "Prefers type hints and dataclasses"
# - "Works on e-commerce platform"
response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "How should I structure this endpoint?"
    }]
)

Multi-Agent Workflows

Coordinate multiple AI agents that share context through Memori. Each agent contributes to a shared memory space while maintaining its own process identity and conversation history.

Benefits:

Share context between specialized agents
Track which agent contributed what information
Maintain conversation continuity across handoffs
Build collective knowledge graphs

from memori import Memori
from openai import OpenAI

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

# Research agent gathers information
mem.attribution(
    entity_id="project_alpha",
    process_id="research_agent"
)
client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{
        "role": "user",
        "content": "Research competitor pricing"
    }]
)

# Analysis agent recalls research findings
mem.attribution(
    entity_id="project_alpha",
    process_id="analysis_agent"
)
# Memori shares context across agents
# for the same entity

For runnable versions of these examples and more, see the examples folder on GitHub.