Introducing Mem0

We're excited to introduce Mem0—the memory layer to create personalized AI applications. Mem0 gives any LLM or agent application the ability to store user preferences, traits, action histories, life events, and more in a smart and self-improving long-term memory layer.

Mem0 is available today, both as open-source software and as a managed service for lower latency, simplified development, and scalable deployment.

The Need for Memory

The advent of Large Language Models (LLMs) opens up a new frontier in computing by enabling humans to interact with machines in natural language. Over the past two years, we've seen a flurry of activity as companies and startups look to leverage this innovation to reimagine all aspects of human life.

A problem that pops up while building these applications is that large language models are stateless. They don't account for individual preferences. Two separate users asking a question to an LLM will receive similar impersonal answers, irrespective of their distinct unique circumstances. To solve their problems, users have to have back-and-forth conversations with the LLM to guide it in a direction of their preferences.

Even if a user does have a conversation where they state their preferences, this context is lost the next time they have a fresh conversation with the LLM. In other words, LLMs lack a long-term memory layer.

The preferred way for developers to provide LLMs with external context is retrieval-augmented-generation, or RAG. RAG involves using search algorithms to query external data, and then incorporating the pre-processed information into the LLM. Developers can also use RAG for storing and surfacing user preferences.

The problem with RAG for long-term memory is that it is a general-purpose tool, not specialized for the memory use case. RAG is static while memories are dynamic. User preferences constantly evolve and change over time. A memory layer would be in constant flux, with memories being added, updated, deleted with every interaction. RAG by itself is ill-suited for this.

What is needed is a solution that specializes in providing memory for LLM applications.

Enter Mem0

Mem0 is the memory layer for creating personalized LLM applications. It empowers AI with the ability to remember and learn from interactions, much like a human would.

At its core, Mem0 uses specialized models and advanced algorithms to detect, store, and surface user memories from conversations with LLMs. This process happens seamlessly in the background:

1. As users interact with an AI, Mem0 identifies important information to remember.
2. It smartly updates these memories over time, resolving contradictions and helping create AI that evolves alongside users.
3. When needed, Mem0 employs semantic search enhanced with a scoring layer. This evaluates memories based on relevance, importance, and recency, ensuring only the most relevant context is surfaced.

The result? AI applications that truly understand and adapt to individual users, providing more meaningful and efficient interactions.

Mem0 is open-source, with over 21,000 stars on GitHub. We also provide a managed solution, the Mem0 platform, with:

• Low Latency: Sub-50ms response times for seamless, real-time AI interactions.
• Easy Integration: Add memory capabilities with just four lines of code.
• High Scalability: Supports everything from prototypes to production-ready systems.

Who Needs Mem0?

Mem0 can enhance any AI application that interacts with users over time—use it for any use case where personalization matters. Here are some examples:

• Customer Support: Retain customer interaction history and product information, enabling agents to provide faster, more accurate support without asking repetitive questions.
• Education: Store student learning progress and subject proficiencies, allowing tutors to deliver tailored lessons and targeted assistance without redundant assessments.
• Sales: Maintain customer preferences and purchase history, empowering sales representatives to offer relevant recommendations and personalized service without repeated inquiries.
• Healthcare: Preserve patient medical histories and treatment plans, helping healthcare providers offer more informed care and personalized advice without requesting known information.
• Personal Assistant: Record user preferences, routines, and important dates, enabling assistants to manage tasks and schedules more efficiently without constant instruction.

Now Live

Mem0 is now live.

You can experience it in our interactive playground.

To learn more, you can view our documentation. Join our community on Discord to connect with other developers. Get started with our platform to experience Mem0 firsthand. For those interested in contributing, our open-source project has over 21,000 stars on GitHub.

Let's make AI personalized and more human-like.