mem0
@mem0ai
Memory Layer for your AI agents. Open source:
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Thanks for the deep dive into Mem0's architecture by @_alejandroao! 🧠
I took Mem0 apart to understand how AI agent memory really works.
0:00 Introduction
0:55 What is agent memory
4:26 Persistent memory stores
8:06 LLM-based ingestion
15:30 Semantic + BM25 retrieval
19:19 Entity boosting
25:38 Local open models
A practical architecture deep dive 🧵
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Context Corner 2.0 is here! 🍽️🤖
Loved the first conversation on context, memory, and agents? We’re back for round two, with even more time around the table and an incredible lunch to match!
If you're a developer building, experimenting, or stuck on the frontlines of AI memory and agents, come hungry and ready to chat.
📍 San Francisco, CA
🗓️ Friday, Aug 14 | 12:00 PM - 1:30 PM PDT
Here's the registration link:
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Mem0 plugin is now listed in Cursor Marketplace: add it once, and every chat can recall decisions, fixes, and conventions from across your whole codebase history. Not just this session.
The plug-in is here:
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We evaluated Nemotron-3-Embed from @NVIDIAAI for our @mem0ai memory retrieval pipeline
Tested on longmemeval: retrieval@10 improved from 78.71 to 80.38 over qwen-3-600m
A few things made it a good fit for how we operate: open weights, NVFP4 support on Blackwell for efficient inference, and strong throughput, which matters for a system where writes happen constantly.
A breakdown on how Mem0 uses embeddings and Nemotron-3-embed evaluation below👇
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Earlier this month, we hosted a mem0rable game night: Mario Kart tournament at the Mem0 office with SF community leaders & growth teams!
Vibes all night!
At mem0, we love creating more memories!
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