Building Chatbots with Memory

Memori transforms stateless chatbots into intelligent conversational agents that remember user preferences, past interactions, and context across sessions. No more “What’s your account number?” every time — your chatbot recalls everything automatically.

Why Memory Matters for Chatbots

Traditional chatbots lose context between sessions. Users must repeat themselves, and the experience feels frustrating and impersonal. Memori solves this by:

Remembering user preferences — favorite products, communication style, accessibility needs
Recalling past conversations — previous issues, solutions, and outcomes
Building user profiles — automatically extracting facts, preferences, and context over time
Providing continuity — seamless experience across days, weeks, or months

Core Pattern: Entity + Process Attribution

Every chatbot conversation needs two IDs:

Entity ID — The user interacting with your bot (e.g., user_456 or customer_jane_doe)
Process ID — Your chatbot’s identity (e.g., support_bot or sales_assistant)

mem.attribution(
    entity_id="user_456",      # Who is this conversation with?
    process_id="support_bot"    # Which bot is handling it?
)

Memori uses these to create isolated memory spaces. User A never sees User B’s memories, and your support bot maintains different context than your sales bot.

Use Case 1: Customer Support Chatbot

Build a support bot that remembers customer history, preferences, and past issues.

Install Dependencies

pip install memori openai

Set Environment Variables

export MEMORI_API_KEY="your-memori-api-key"
export OPENAI_API_KEY="your-openai-api-key"

Create Your Support Bot

Create a file support_bot.py:

from memori import Memori
from openai import OpenAI

def create_support_bot(customer_id: str):
    """Initialize a support bot for a specific customer."""
    client = OpenAI()
    mem = Memori().llm.register(client)
    
    # Link conversations to this customer and the support bot process
    mem.attribution(
        entity_id=customer_id,
        process_id="support_bot"
    )
    
    return client, mem

def chat(client, user_message: str):
    """Send a message and get a response."""
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful customer support agent. "
                           "Remember customer preferences and history."
            },
            {"role": "user", "content": user_message}
        ]
    )
    return response.choices[0].message.content

if __name__ == "__main__":
    # Customer first interaction
    client, mem = create_support_bot("customer_456")
    
    print("Customer: I'm having trouble logging in. My username is jane_smith.")
    response1 = chat(client, "I'm having trouble logging in. My username is jane_smith.")
    print(f"Support: {response1}\n")
    
    print("Customer: Can you reset my password?")
    response2 = chat(client, "Can you reset my password?")
    print(f"Support: {response2}\n")
    
    # Wait for memory processing
    mem.augmentation.wait()
    
    # Later conversation — new session, same customer
    print("--- Customer returns 3 days later ---\n")
    
    client2, mem2 = create_support_bot("customer_456")
    
    print("Customer: I'm locked out again!")
    response3 = chat(client2, "I'm locked out again!")
    print(f"Support: {response3}")
    # Memori recalls: username is jane_smith, previous login issues

Run the Bot

python support_bot.py

The bot remembers the customer’s username and previous login issues, even in a completely new session!

Use Case 2: E-commerce Shopping Assistant

Create a shopping assistant that learns user preferences and recommends products based on past interactions.

from memori import Memori
from openai import OpenAI

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

# Attribution for this shopper
mem.attribution(
    entity_id="shopper_789",
    process_id="shopping_assistant"
)

# First interaction: User shares preferences
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "system",
            "content": "You are a personal shopping assistant. "
                       "Learn user preferences and make personalized recommendations."
        },
        {
            "role": "user",
            "content": "I'm looking for a laptop. I prefer MacBooks and need 16GB RAM minimum."
        }
    ]
)
print(response.choices[0].message.content)

mem.augmentation.wait()

# Later interaction — Memori recalls preferences
response2 = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "Show me your latest laptop deals."
        }
    ]
)
print(response2.choices[0].message.content)
# Memori injects: "User prefers MacBooks, needs 16GB+ RAM"

Use Case 3: Conversational Chatbot with Agno

Build an Agno-powered chatbot with persistent memory across conversations.

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from memori import Memori

model = OpenAIChat(id="gpt-4o-mini")

mem = Memori().llm.register(openai_chat=model)
mem.attribution(
    entity_id="user_123",
    process_id="conversational_agent"
)

agent = Agent(
    model=model,
    instructions=[
        "You are a friendly conversational assistant.",
        "Remember user preferences and context from previous conversations.",
    ],
    markdown=True,
)

# First conversation
print("User: I love science fiction books, especially by Philip K. Dick")
response1 = agent.run(
    "I love science fiction books, especially by Philip K. Dick"
)
print(f"Agent: {response1.content}\n")

# Later conversation
print("User: Can you recommend a book?")
response2 = agent.run("Can you recommend a book?")
print(f"Agent: {response2.content}")
# Agent recalls: User loves sci-fi, especially Philip K. Dick

mem.augmentation.wait()

Advanced: Session Management

Group related conversations into sessions for better context organization.

from memori import Memori
from openai import OpenAI

client = OpenAI()
mem = Memori().llm.register(client)
mem.attribution(entity_id="customer_456", process_id="support_bot")

# Session 1: Password reset issue
print("Session 1: Password Reset")
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "I need to reset my password"}]
)
print(response.choices[0].message.content)

# Start new session for a different issue
mem.new_session()

print("\nSession 2: Billing Question")
response2 = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Why was I charged twice?"}]
)
print(response2.choices[0].message.content)
# Each session maintains separate conversation context

Web Framework Integration

FastAPI

from fastapi import FastAPI, Depends
from memori import Memori
from openai import OpenAI

app = FastAPI()

def get_chatbot(user_id: str):
    client = OpenAI()
    mem = Memori().llm.register(client)
    mem.attribution(
        entity_id=user_id,
        process_id="web_chatbot"
    )
    return client

@app.post("/chat")
async def chat(
    user_id: str,
    message: str,
    client: OpenAI = Depends(get_chatbot)
):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": message}]
    )
    return {"response": response.choices[0].message.content}

Flask

from flask import Flask, request, jsonify
from memori import Memori
from openai import OpenAI

app = Flask(__name__)

@app.route("/chat", methods=["POST"])
def chat():
    data = request.json
    user_id = data["user_id"]
    message = data["message"]

    client = OpenAI()
    mem = Memori().llm.register(client)
    mem.attribution(
        entity_id=user_id,
        process_id="flask_chatbot"
    )

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

    return jsonify({
        "response": response.choices[0].message.content
    })

Best Practices

Always Set Attribution

Without entity_id and process_id, Memori cannot create or recall memories. Always call mem.attribution() before making LLM calls.

# Required for memory to work
mem.attribution(
    entity_id="user_123",
    process_id="my_chatbot"
)

Use Descriptive IDs

Choose meaningful entity and process IDs:

Entity: user_{id}, customer_{email}, session_{uuid}
Process: support_bot, sales_assistant, onboarding_agent

This makes debugging easier and helps you understand memory patterns in the dashboard.

Handle Memory Processing in Scripts

Memory augmentation runs asynchronously. In short-lived CLI scripts, call mem.augmentation.wait() to ensure processing completes before exit.

# In CLI scripts
response = client.chat.completions.create(...)
print(response.choices[0].message.content)

mem.augmentation.wait()  # Wait for memory processing

In long-running web servers, this is not needed — augmentation happens in the background.

Use Sessions for Conversation Grouping

Group related interactions into sessions:

# Start a new conversation thread
mem.new_session()

# Or restore a previous session
session_id = mem.config.session_id
# ... later ...
mem.set_session(session_id)

What Memori Remembers

Memori’s Advanced Augmentation automatically extracts and stores:

Memory Type	Scope	Example
Facts	Per entity, shared across processes	”Uses PostgreSQL for database”
Preferences	Per entity	”Prefers dark mode”, “Likes sci-fi”
Attributes	Per process	”Support bot handles login issues”
Skills	Per entity	”Python developer”, “FastAPI expert”
Relationships	Per entity	”Works at Acme Corp”, “Reports to Jane”

Next Steps

AI Agents

Build autonomous agents with persistent memory

Multi-Agent Systems

Coordinate multiple agents with shared memory

Dashboard

Explore memories in the Graph Explorer

Advanced Augmentation

Learn how memory extraction works

Use Cases

Examples

Advanced

Contributing

Building Chatbots with Memory

Building Chatbots with Memory

Why Memory Matters for Chatbots

Core Pattern: Entity + Process Attribution

Use Case 1: Customer Support Chatbot

Use Case 2: E-commerce Shopping Assistant

Use Case 3: Conversational Chatbot with Agno

Advanced: Session Management

Web Framework Integration

FastAPI

Flask

Best Practices

What Memori Remembers

Next Steps

AI Agents

Multi-Agent Systems

Dashboard

Advanced Augmentation

Build docs developers (and LLMs) love

Use Cases

Examples

Advanced

Contributing

​Building Chatbots with Memory

​Why Memory Matters for Chatbots

​Core Pattern: Entity + Process Attribution

​Use Case 1: Customer Support Chatbot

​Use Case 2: E-commerce Shopping Assistant

​Use Case 3: Conversational Chatbot with Agno

​Advanced: Session Management

​Web Framework Integration

FastAPI

Flask

​Best Practices

​What Memori Remembers

​Next Steps

AI Agents

Multi-Agent Systems

Dashboard

Advanced Augmentation

Build docs developers (and LLMs) love

Building Chatbots with Memory

Why Memory Matters for Chatbots

Core Pattern: Entity + Process Attribution

Use Case 1: Customer Support Chatbot

Use Case 2: E-commerce Shopping Assistant

Use Case 3: Conversational Chatbot with Agno

Advanced: Session Management

Web Framework Integration

Best Practices

What Memori Remembers

Next Steps