Skip to main content

Mem0 home page

Search...

Ctrl KAsk AI

Search...

Navigation

Companion Playbooks

Smart Travel Assistant

Welcome Mem0 Platform Open Source Cookbooks Integrations API Reference Release Notes

• Documentation

Getting Started

• Overview

Essentials

• Build a Companion with Mem0
• Partition Memories by Entity
• Control Memory Ingestion
• Set Memory Expiration
• Tag and Organize Memories
• Export Stored Memories
• Choose Vector vs Graph Memory

Companion Playbooks

• Interactive Memory Demo
• Build a Node.js Companion
• Personalized AI Tutor
• Smart Travel Assistant
• Research Assistant for YouTube
• Voice-First AI Companion
• Self-Hosted AI Companion

Ops & Automations

• Memory-Powered Support Agent
• Automated Email Intelligence
• Content Creation Workflow
• Multi-Session Research Agent
• Collaborative Task Assistant

Integrations & Platforms

• Memory-Powered Agent SDK
• Memory as OpenAI Tool
• Persistent Mastra Agents
• Healthcare Coach with ADK
• Bedrock with Persistent Memory
• Graph Memory on Neptune
• Search with Personal Context

Frameworks & Multimodal

• ReAct Agents with Memory
• Multi-Agent Collaboration
• Visual Memory Retrieval
• Persistent Eliza Characters
• Browser Extension Memory
• Gemini 3 with Mem0 MCP
• MiroFish Swarm Memory

On this page

• Overview
• Setup
• Full Code Example
• Key Components
• Usage
• Conclusion

Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.

​ Overview

The Personalized AI Travel Assistant uses Mem0 to store and retrieve information across interactions, enabling a tailored travel planning experience. It integrates with OpenAI’s GPT-4 model to provide detailed and context-aware responses to user queries.

​ Setup

Install the required dependencies using pip:

pip install openai mem0ai

​ Full Code Example

Here’s the complete code to create and interact with a Personalized AI Travel Assistant using Mem0:

After v1.1

Before v1.1

import os
from openai import OpenAI
from mem0 import Memory

# Set the OpenAI API key
os.environ['OPENAI_API_KEY'] = "sk-xxx"

config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4.1-nano-2025-04-14",
            "temperature": 0.1,
            "max_tokens": 2000,
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-large"
        }
    },
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "test",
            "embedding_model_dims": 3072,
        }
    },
    "version": "v1.1",
}

class PersonalTravelAssistant:
    def __init__(self):
        self.client = OpenAI()
        self.memory = Memory.from_config(config)
        self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]

    def ask_question(self, question, user_id):
        # Fetch previous related memories
        previous_memories = self.search_memories(question, user_id=user_id)

        # Build the prompt
        system_message = "You are a personal AI Assistant."

        if previous_memories:
            prompt = f"{system_message}\n\nUser input: {question}\nPrevious memories: {', '.join(previous_memories)}"
        else:
            prompt = f"{system_message}\n\nUser input: {question}"

        # Generate response using Responses API
        response = self.client.responses.create(
            model="gpt-4.1-nano-2025-04-14",
            input=prompt
        )

        # Extract answer from the response
        answer = response.output[0].content[0].text

        # Store the question in memory
        self.memory.add(question, user_id=user_id)
        return answer

    def get_memories(self, user_id):
        memories = self.memory.get_all(user_id=user_id)
        return [m['memory'] for m in memories['results']]

    def search_memories(self, query, user_id):
        memories = self.memory.search(query, user_id=user_id)
        return [m['memory'] for m in memories['results']]

# Usage example
user_id = "traveler_123"
ai_assistant = PersonalTravelAssistant()

def main():
    while True:
        question = input("Question: ")
        if question.lower() in ['q', 'exit']:
            print("Exiting...")
            break

        answer = ai_assistant.ask_question(question, user_id=user_id)
        print(f"Answer: {answer}")
        memories = ai_assistant.get_memories(user_id=user_id)
        print("Memories:")
        for memory in memories:
            print(f"- {memory}")
        print("-----")

if __name__ == "__main__":
    main()

​ Key Components

• Initialization: The PersonalTravelAssistant class is initialized with the OpenAI client and Mem0 memory setup.
• Asking Questions: The ask_question method sends a question to the AI, incorporates previous memories, and stores new information.
• Memory Management: The get_memories and search_memories methods handle retrieval and searching of stored memories.

​ Usage

1. Set your OpenAI API key in the environment variable.
2. Instantiate the PersonalTravelAssistant.
3. Use the main() function to interact with the assistant in a loop.

​ Conclusion

This Personalized AI Travel Assistant leverages Mem0’s memory capabilities to provide context-aware responses. As you interact with it, the assistant learns and improves, offering increasingly personalized travel advice and information.

---

Tag and Organize Memories \ \ Use categories to organize travel preferences, destinations, and user context.

AI Tutor with Mem0 \ \ Build an educational companion that remembers learning progress and preferences.

Was this page helpful?

YesNo

Suggest edits Raise issue

Personalized AI Tutor\ \ Previous Research Assistant for YouTube\ \ Next

Ctrl+I

Assistant

Responses are generated using AI and may contain mistakes.

Suggestions

How does memory work across trips?How do I set up the assistant?What dependencies do I need?

Contact support