deshraj
Joined 10 years ago
Karma
47
Submissions
29
Member since
Nov 29
Recent submissions
Comment · 1 year ago
Congratulations, Emir and Waleed! This is exactly the kind of OSS tooling I’ve been waiting for. I’ve spent countless hours wrestling with multi-step agent workflows hidden inside monolithic prompts, and every iteration felt like shooting in the dark. Having a drag-and-drop, executable graph with built-in branching, loops, and observability is a game-changer.
Comment · 2 years ago
Ah I see. We do support running Mem0 locally with Ollama. You can checkout our docs here: https://docs.mem0.ai/examples/mem0-with-ollama
Comment · 2 years ago
100% agree. I have seen similar issues related to both quality and performance of ChatGPT Memory feature.
Shameless plug: We have been working on this problem at Mem0 to solve the long-term memory problem with LLMs. GitHub: https://github.com/mem0ai/mem0
Comment · 2 years ago
Yes, you can run Mem0 locally since we have open sourced it but would need some more work to have a server up and running to be able to interact with Claude.
GitHub: https://github.com/mem0ai/mem0
Comment · 2 years ago
It only support Chrome for now. I built this in few hours quickly to solve my problem. Happy to accept contributions to the repository if someone builds it.
Comment · 2 years ago
Yes, it won't scale well. I have used postgres exactly the way you specified in my past job and it didn't scale well after a certain point.
Comment · 2 years ago
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.
These differences make Mem0 a better choice for developers building AI apps that need efficient memory capabilities.
Comment · 2 years ago
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!
Comment · 2 years ago
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
Comment · 2 years ago
Thanks for the feedback! Yes, we are definitely planning to add support for other graph datastores including Memgraph and others.
Comment · 2 years ago
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. :)
Before founding Mem0, I worked at Tesla as a Senior Autopilot Engineer, where I led the development of the AI Platform. This platform was essential for large-scale training and model evaluation for the Autopilot team. My role involved monitoring job performance, troubleshooting training clusters, automating machine learning workflows, and developing a centralized platform for engineers and researchers to access metrics, logs, and data.
My experience also includes creating various tools and infrastructure to improve machine learning processes, including 3D labeling software, model evaluation tools, and active learning-based label enhancement software.
Academically, I hold a Master's degree in Computer Science with a focus on Machine Learning from Georgia Tech. My research has concentrated on enhancing the accuracy of Artificial Intelligence and understanding human intentions and behaviors. I've been honored with the Snap Research Scholarship and gained experience as an intern at Snap Research.
Additionally, I spearhead EvalAI, an open-source platform dedicated to evaluating and comparing ML and AI algorithms at scale.