Mem0 provides a memory layer for LLM applications, enabling them to remember and learn from user interactions over time.

The Memory layer for your AI apps
Title: Mem0: The Memory layer for your AI apps | Y Combinator

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### The Memory layer for your AI apps

Mem0 provides a memory layer for LLM applications, enabling them to remember and learn from user interactions over time.



Mem0

Founded:2023

Team Size:4

Location:San Francisco

### Active Founders

### Taranjeet Singh, Founder

Taranjeet Singh is the co-founder & CEO of Mem0. He joined Khatabook (YC S18) as first growth engineer & quickly transitioned to Senior PM. He began his software engineering career at Paytm (India's Paypal) & witnessed its meteoric rise to become a household name. He built an AI-powered tutoring app that was featured at Google I/O. He co-created EvalAI, an open source Kaggle alternative with Deshraj & grew it to 1.6K GitHub stars. He also created the first GPT app store, scaling it to 1M+ users.



### Deshraj Yadav, Founder

Deshraj Yadav is the co-founder and CTO of Mem0. He is broadly interested in the field of Artificial Intelligence and Machine Learning Infrastructure. He led the AI Platform at Tesla Autopilot, which enabled large-scale training, model evaluation, monitoring, and observability for Tesla's full self-driving development. Prior to that, Deshraj created EvalAI, an open source ML platform, as his master's thesis at Georgia Tech. He has published papers at top AI conferences like CVPR, ECCV, AAAI etc.



### Company Launches

[### Mem0 - Open Source Memory Layer for AI Apps](

**TL;DR**

[Mem0]( is an open-source memory layer for AI applications. It solves the problem of stateless LLMs by efficiently storing and retrieving user interactions, enabling personalized AI experiences that improve over time. Our hybrid datastore architecture combines graph, vector, and key-value stores to make AI apps personalized and cost-efficient. Watch our explainer video [here](

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Hey everyone! We're Taranjeet and Deshraj, and we built [Mem0]( to solve a big problem we faced with LLMs while building Embedchain (an open-source RAG framework with 2M+ downloads). LLMs don’t have memory, so they forget everything after each session. This leads to inefficient and repetitive interactions, making it hard to create personalized AI experiences. Think about having to repeat your preferences over and over again, and how frustrating that gets! Mem0 changes that.

❌ **The Problem**

LLMs are stateless—they don’t remember anything between sessions. Every time you interact with them, you have to provide the same context, which gets repetitive and wastes computational resources. This makes AI apps less useful and personalized over time.

✨ **Our Solution**



Mem0 adds a **memory layer** to AI applications, making them stateful which allows them to store and recall user interactions, preferences, and relevant context. This way, AI apps evolve with every interaction, delivering more personalized and relevant responses without needing large context blocks in each prompt.

To make this possible, we needed to create a system that could efficiently manage and retrieve all the relevant information AI apps collect over time. That’s where Mem0’s **hybrid datastore architecture** comes in, making AI smarter and more efficient as it learns.

⚙️ **How it works**

[Mem0]( employs a hybrid datastore architecture that combines graph, vector, and key-value stores to store and manage memories effectively. Here’s the breakdown:

*   **Adding memories:** When you use Mem0 with your AI App, it automatically detects and stores the important parts of your messages or interactions.
*   **Organizing information:** Mem0 categorizes memories in three ways:
    *   **Key-value stores** for quick access to structured data (facts, preferences).
    *   **Graph stores** for understanding relationships (like people, places, objects).
    *   **Vector stores** for capturing the overall meaning and context of conversations, allowing AI apps to find similar memories later.
*   **Retrieving memories:** When an input query is received, Mem0 searches for and retrieves relevant memories using a combination of graph traversal techniques, vector similarity, and key-value lookups. It prioritizes the most important, relevant, and recent information, ensuring that the AI has the right context, no matter how much memory is stored.

Watch this video for a demo of our playground in action [here](

🙏 **Our Asks**

*   Try out [Mem0]( We guarantee that your users will have personalized interactions after adding Mem0 in your AI apps. Moreover, you will save on your LLM costs.
*   If you are looking for a memory provider for your AI app and want to schedule a demo, please feel free to block some time on [my calendar]( or email me at [taranjeet@mem0.ai](mailto:taranjeet@mem0.ai)
*   Checkout our platform and open-source offering and give feedback:
    *   Platform: [
    *   GitHub: [

### Selected answers from Mem0's original YC application for the S24 Batch

#### What is your company going to make? Please describe your product and what it does or will do.

We are building self-improving memory for LLM apps that enables seamless personalization for end-users. Our product offers APIs that allow developers to store and manage individual user preferences in a centralized layer.

This smart memory continuously learns from user interactions, ensuring preferences are consistently applied no matter which LLM the user engages with. It provides a personalized experience across different AI platforms and applications.

By offering our smart memory as a service, we empower developers to integrate advanced personalization capabilities into their products, significantly reducing complexity.
