[AI Mem0 Platform] Quickstart: Empower Your AI Applications with Long-term Memory and Personalization!

Introduction

Empower your AI applications with long-term memory and personalization capabilities.

Welcome to Mem0 Platform

Mem0 Platform is a managed service that revolutionizes the way AI applications handle memory. By providing a smart, self-improving memory layer for Large Language Models (LLMs), we enable developers to create personalized AI experiences that evolve with each user interaction.

Why Choose Mem0 Platform?

Enhanced User Experience: Deliver tailored interactions that make your AI applications truly stand out.
Simplified Development: Our API-first approach streamlines integration, allowing you to focus on building great features.
Scalable Solution: Designed to grow with your application, from prototypes to production-ready systems.

Key Features

Comprehensive Memory Management: Easily manage long-term, short-term, semantic, and episodic memories for individual users, agents, and sessions through our robust APIs.
Self-Improving Memory: Our adaptive system continuously learns from user interactions, refining its understanding over time.
Cross-Platform Consistency: Ensure a unified user experience across various AI platforms and applications.
Centralized Memory Control: Store, update, and delete memories effortlessly, taking away the hassle of memory management.

Common Use Cases

Personalized Learning Assistants
Customer Support AI Agents
Health Assistants
Virtual Companions
Productivity Tools
Gaming AI

Getting Started

Ready to supercharge your AI application with Mem0? Follow these steps:

Sign Up: Create your Mem0 account on our platform.
API Key: Generate your API key in the dashboard.
Installation: Install our Python SDK using pip: pip install mem0ai
Quick Implementation: Check out our Quickstart Guide to start using Mem0 quickly.

Quickstart

Get started with Mem0 Platform in minutes

Installation

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pip install mem0ai

API Key Setup

Log in to Mem0 Platform
Copy your API Key from the dashboard

Instantiate Client

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from mem0 import MemoryClient
client = MemoryClient(api_key="your-api-key")

Memory Operations

Mem0 provides a simple and customizable interface for performing CRUD operations on memory.

Create Memories

You can create long-term and short-term memories for your users, AI agents, etc. Here are some examples:

Long-term memory for a user

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messages = [
    {"role": "user", "content": "Hi, I'm Learn AI from Scratch. I'm very interested in AI."},
    {"role": "assistant", "content": "Hello Learn AI from Scratch! I've noted that you're very interested in AI. I'll keep this in mind for any learning-related recommendations or discussions."}
]
client.add(messages, user_id="learn-ai-from-scratch")

Short-term memory for a user session

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messages = [
    {"role": "user", "content": "I plan to read a book next month."},
    {"role": "assistant", "content": "That's exciting, Learn AI from Scratch! Reading a book next month sounds wonderful. Would you like some recommendations for AI-related books?"},
    {"role": "user", "content": "Yes, please!"},
    {"role": "assistant", "content": "Great! I'll remember that you're interested in AI-related books. I'll prepare a list for you in our next interaction."}
]
client.add(messages, user_id="learn-ai-from-scratch", session_id="read-a-book")

Long-term memory for an agent

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messages = [
    {"role": "system", "content": "You are a book recommendation assistant. Remember user preferences and provide tailored recommendations."},
    {"role": "assistant", "content": "Understood. I'll maintain personalized book recommendation preferences for each user based on their interests and past interactions."}
]
client.add(messages, agent_id="book-recommendation-assistant")

You can monitor memory operations on the platform:

Search Relevant Memories

You can also get related memories for a given natural language question using our search method.

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query = "What do you know about me?"
client.search(query, user_id="learn-ai-from-scratch")

Get All Memories

Fetch all memories for a user, agent, or session using the getAll() method.

Get all memories of an AI agent

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client.get_all(agent_id="book-recommendation-assistant")

Get all memories of a user

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user_memories = client.get_all(user_id="learn-ai-from-scratch")

Get short-term memories for a session

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short_term_memories = client.get_all(user_id="learn-ai-from-scratch", session_id="next-month-book-recommendation")

Get specific memory

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memory = client.get(memory_id="0e2bded6-4d55-11ef-b589-00163e064f1a")

Memory History

Get history of how a memory has changed over time

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# Add some message to create history
messages = [{"role": "user", "content": "I've recently been reading books related to AI"}]
client.add(messages, user_id="learn-ai-from-scratch")

# Add second message to update history
messages.append({'role': 'user', 'content': 'I have switched to reading novels now.'})
client.add(messages, user_id="learn-ai-from-scratch")

# Get history of how memory changed over time
memory_id = "<memory-id-here>"
history = client.history(memory_id)

Delete Memory

Delete specific memory:

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client.delete(memory_id)

Delete all memories of a user:

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client.delete_all(user_id="learn-ai-from-scratch")

Fun fact: You can also delete the memory using the add() method by passing a natural language command:

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client.add("Delete all of my book preferences", user_id="learn-ai-from-scratch")