Smart Travel Assistant

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:

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-5-mini",
            "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,
        }
    },
}

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-5-mini",
            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(filters={"user_id": user_id})
        return [m['memory'] for m in memories['results']]

    def search_memories(self, query, user_id):
        memories = self.memory.search(query, filters={"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()

import os
from openai import OpenAI
from mem0 import Memory

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

class PersonalTravelAssistant:
    def __init__(self):
        self.client = OpenAI()
        self.memory = Memory()
        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)
        prompt = question
        if previous_memories:
            prompt = f"User input: {question}\n Previous memories: {previous_memories}"
        self.messages.append({"role": "user", "content": prompt})

        # Generate response using gpt-4.1-nano
        response = self.client.chat.completions.create(
            model="gpt-5-mini",
            messages=self.messages
        )
        answer = response.choices[0].message.content
        self.messages.append({"role": "assistant", "content": answer})

        # 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(filters={"user_id": user_id})
        return [m['memory'] for m in memories.get('results', [])]

    def search_memories(self, query, user_id):
        memories = self.memory.search(query, filters={"user_id": user_id})
        return [m['memory'] for m in memories.get('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

Set your OpenAI API key in the environment variable.
Instantiate the PersonalTravelAssistant.
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.

Enjoying Mem0? Star us on GitHub — it takes two seconds and helps more developers discover open-source memory.

Getting Started

Essentials

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Ops & Automations

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Frameworks & Multimodal

Smart Travel Assistant

Overview

Setup

Full Code Example

Key Components

Usage

Conclusion

Tag and Organize Memories

AI Tutor with Mem0

​Overview

​Setup

​Full Code Example

​Key Components

​Usage

​Conclusion

Tag and Organize Memories

AI Tutor with Mem0

Overview

Setup

Full Code Example

Key Components

Usage

Conclusion