We already support the feature of inclusion and exclusion of memories where the developer can control what things to remember vs not remember for their AI app/agent. For example, you can specify something like this:

- Inclusion prompt: User's travel preferences and food choices - Exclusion prompt: Credit card details, passport number, SSN etc.

Although we definitely think that there is scope to make it better and we are actively working on it. Please let us know if you have feedback/suggestions. Thanks!

This is why in my system I have more specific, falsifiable metrics: freshness, confidence, etc. which come together to create a fitness score at the surface-level, while still exposing individual metrics in the API.

Mem0 currently handles outdated or irrelevant memories by:

1. Automatically deprioritizing older memories when new, contradictory information is added. 2. Adjusting memory relevance based on changing contexts.

We're working on improving this system to give developers more control. Future plans include:

1. Time-based decay of unused memories 2. Customizable relevance scoring 3. Manual removal options for obsolete information

These improvements aim to create a more flexible "forgetting" mechanism, allowing AI applications to maintain up-to-date and relevant knowledge bases over time.

We're open to user feedback on how to best implement these features in practical applications.

Thanks yding! Definitely agree with the feedback here. We have seen similar things when talking to developers where they want:

- Control over what to remember/forget - Ability to set how detailed memories should be (some want more detailed vs less detailed) - Different structure of the memories based on the use case

Thanks for the feedback! Yes, we are definitely planning to add support for other graph datastores including Memgraph and others.

Thanks for your question!

Vector databases are typically used for storing embeddings and are great for tasks like similarity search. However, they are generally read-only and don't natively support the concept of time or state transitions. Let's take an example of tracking state of a tasks from your todo list in a vector database:

You might store the task's states like:

Task 1 in backlog Task 1 in progress Task 1 in canceled

But there's no concept of "latest state" or memory of how the task evolved over time. You'd have to store multiple versions and manually track changes.

With a memory-enabled system like Mem0, you could track: Task 1 (current state: in progress) with a memory of previous states (backlog, canceled, etc). This gives your AI app more stateful understanding of the world, allowing it to update and reflect the current context automatically.

Traditional databases, on the other hand, are designed for structured, relational data with fixed schemas, like customer information in a table. These are great for handling transactional data but aren't optimal for cases where the data is unstructured.

As mentioned in the post, we use a hybrid datastore approach that handles these cases effectively and that's where the graph aspect comes into picture.

Hey, yes. It remembers whatever is important from the previous chats.

Hey, Deshraj from Mem0 team. Right now you cannot change the “user” you are chatting as yet but we can definitely make it happen. Will ship this update later today. :)

Hey, that would be great. Lets chat

Thanks for the question. Here's how Mem0 differs from ChatGPT memory:

1. LLM Compatibility: Mem0 works with various AI providers (OpenAI, Anthropic, Groq, etc.), while ChatGPT memory is tied to OpenAI's models only.

2. Target Audience: Mem0 is built for developers creating AI applications, whereas ChatGPT memory is for ChatGPT users.

3. Quality and Performance: Our evaluations show Mem0 outperforms ChatGPT memory in several areas:

    - Consistency: Mem0 updates memories more reliably across multiple instances.

    - Reliability: ChatGPT memory can be inconsistent with the same prompts, while Mem0 aims for more consistent performance.

    - Speed: Mem0 typically creates memories in about 2 seconds, compared to ChatGPT's 30-40 seconds to reflect new memories.

4. Flexibility: Mem0 offers more customization options for developers, allowing better integration into various AI applications.

These differences make Mem0 a better choice for developers building AI apps that need efficient memory capabilities.

thanks

Thanks for your question! Currently, we process data in the US and are not yet fully GDPR-compliant, but we're actively working on it. We also plan to offer a Europe-based data processing option soon. Your feedback on this is welcome!

Thanks for your question!

Claude Prompt Caching and Mem0's memory system have several key differences:

1. Purpose and duration: Claude's cache is designed for short-term memory, clearing every 5 minutes. In contrast, Mem0 is built for long-term information storage, retaining data indefinitely unless instructed otherwise. 2. Flexibility and control: Mem0 offers more flexibility, allowing developers to update, delete, or modify stored information as needed. Claude's cache is more static - new information creates additional entries rather than updating existing ones. 3. Content management: Claude has minimum length requirements for caching (1024 characters for Sonnet, 2048 for Haiku). Mem0 can handle information of any length, from short facts to longer contexts. 4. Customization: Developers have greater control over Mem0's memory management, including options for prioritizing or deprioritizing information based on relevance or time. Claude's caching system offers less direct control. 5. Information retrieval: Mem0 is designed for more precise and targeted information retrieval, while Claude's cache works with broader contextual blocks.

These differences reflect the distinct purposes of each system. Claude's cache aims to maintain recent context in ongoing conversations, while Mem0 is built to serve as a flexible, long-term knowledge base for AI applications.