Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt

Use this file to discover all available pages before exploring further.

Imagine asking a search assistant for “coffee shops nearby” and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for “lunchbox ideas for kids” it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy. That’s what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and Tavily for real-time search. Most assistants treat every query like they’ve never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
  • With Mem0, your assistant builds a memory of the user’s world.
  • With Tavily, it fetches fresh and accurate results in real time.
Together, they make every interaction smarter, faster, and more personal.

​
Prerequisites

Before you begin, make sure you have:
  1. Installed the dependencies:
pip install langchain mem0ai langchain-tavily langchain-openai
  1. Set up your API keys in a .env file:
OPENAI_API_KEY=your-openai-key
TAVILY_API_KEY=your-tavily-key
MEM0_API_KEY=your-mem0-key

​
Code Walkthrough

Let’s break down the main components.

​
1: Initialize Mem0 with Custom Instructions

We configure Mem0 with custom instructions that guide it to infer user memories tailored specifically for our usecase. 
from mem0 import MemoryClient

mem0_client = MemoryClient()

mem0_client.project.update(
    custom_instructions='''
INFER THE MEMORIES FROM USER QUERIES EVEN IF IT'S A QUESTION.

We are building personalized search for which we need to understand about user's preferences and life
and extract facts and memories accordingly.
'''
)
Now, if a user casually mentions “I need to pick up my daughter” or “What’s the weather at Los Angeles”, Mem0 remembers they have a daughter or the user is interested in or connected with Los Angeles in terms of location. These details will be referenced for future searches.

​
2. Simulating User History

To test personalization, we preload some sample conversation history for a user: 
def setup_user_history(user_id):
    conversations = [
        [{"role": "user", "content": "What will be the weather today at Los Angeles? I need to pick up my daughter from office."},
         {"role": "assistant", "content": "I'll check the weather in LA for you."}],
        [{"role": "user", "content": "I'm looking for vegan restaurants in Santa Monica"},
         {"role": "assistant", "content": "I'll find great vegan options in Santa Monica."}],
        [{"role": "user", "content": "My 7-year-old daughter is allergic to peanuts"},
         {"role": "assistant", "content": "I'll remember to check for peanut-free options."}],
        [{"role": "user", "content": "I work remotely and need coffee shops with good wifi"},
         {"role": "assistant", "content": "I'll find remote-work-friendly coffee shops."}],
        [{"role": "user", "content": "We love hiking and outdoor activities on weekends"},
         {"role": "assistant", "content": "Great! I'll keep your outdoor activity preferences in mind."}],
    ]

    for conversation in conversations:
        mem0_client.add(conversation, user_id=user_id)
This gives the agent a baseline understanding of the user’s lifestyle and needs.

​
3. Retrieving User Context from Memory

When a user makes a new search query, we retrieve relevant memories to enhance the search query: 
def get_user_context(user_id, query):
    # For Platform API, user_id goes in filters
    filters = {"user_id": user_id}
    user_memories = mem0_client.search(query=query, filters=filters)

    if user_memories:
        context = "\n".join([f"- {memory['memory']}" for memory in user_memories])
        return context
    else:
        return "No previous user context available."
This context is injected into the search agent so results are personalized.

​
4. Creating the Personalized Search Agent

The agent uses Tavily search, but always augments search queries with user context: 
def create_personalized_search_agent(user_context):
    tavily_search = TavilySearch(
        max_results=10,
        search_depth="advanced",
        include_answer=True,
        topic="general"
    )

    tools = [tavily_search]

    prompt = ChatPromptTemplate.from_messages([
        ("system", f"""You are a personalized search assistant.

USER CONTEXT AND PREFERENCES:
{user_context}

YOUR ROLE:
1. Analyze the user's query and context.
2. Enhance the query with relevant personal memories.
3. Always use tavily_search for results.
4. Explain which memories influenced personalization.
"""),
        MessagesPlaceholder(variable_name="messages"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ])

    agent = create_openai_tools_agent(llm=llm, tools=tools, prompt=prompt)
    return AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
The workflow ties everything together: 
def conduct_personalized_search(user_id, query):
    user_context = get_user_context(user_id, query)
    agent_executor = create_personalized_search_agent(user_context)

    response = agent_executor.invoke({"messages": [HumanMessage(content=query)]})
    return {"agent_response": response['output']}

​
6. Store New Interactions

Every new query/response pair is stored for future personalization: 
def store_search_interaction(user_id, original_query, agent_response):
    interaction = [
        {"role": "user", "content": f"Searched for: {original_query}"},
        {"role": "assistant", "content": f"Results based on preferences: {agent_response}"}
    ]
    mem0_client.add(messages=interaction, user_id=user_id)

​
Full Example Run

if __name__ == "__main__":
    user_id = "john"
    setup_user_history(user_id)

    queries = [
        "good coffee shops nearby for working",
        "what can I make for my kid in lunch?"
    ]

    for q in queries:
        results = conduct_personalized_search(user_id, q)
        print(f"\nQuery: {q}")
        print(f"Personalized Response: {results['agent_response']}")

​
How It Works in Practice

Here’s how personalization plays out:
  • Context Gathering: User previously mentioned living in Los Angeles, being vegan, and having a 7-year-old daughter allergic to peanuts.
  • Enhanced Search Query:
    • Query: “good coffee shops nearby for working”
    • Enhanced Query: “good coffee shops in Los Angeles with strong WiFi, remote-work-friendly”
  • Personalized Results: The assistant only returns WiFi-friendly, work-friendly cafes near Los Angeles.
  • Memory Update: Interaction is saved for better future recommendations.

​
Conclusion

With Mem0 and Tavily, you can build a search assistant that doesn’t just fetch results but understands the person behind the query. Whether for shopping, travel, or daily life, this approach turns a generic search into a truly personalized experience. Full Code: Personalized Search GitHub

Deep Research with Mem0

Build comprehensive research agents that remember findings across sessions.

Tag and Organize Memories

Categorize search results and user preferences for better personalization.