Dhravya Shah - supermemory - Blog

Dhravya Shah

3 Ways To Build LLMs With Long-Term Memory

3 Ways To Build LLMs With Long-Term Memory

You’ve already met our guide on implementing short-term conversational memory using LangChain, which is great for managing context inside a single chat window. But life, therapy, and enterprise apps sprawl across days, weeks, and years. If our agents are doomed to goldfish-brain amnesia, users end up re-explaining everything from

How To Add Conversational Memory To LLMs Using LangChain

How To Add Conversational Memory To LLMs Using LangChain

Chatbots that don’t remember conversations are very frustrating to work with. Users treat AI like a human and expect it to remember. LangChain recently migrated to LangGraph, a new stateful framework for building multi-step, memory-aware LLM apps. So while the docs might still say “LangChain memory,” what you’re

The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products

The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products

Overview: Flow is a note-taking app built around a bold vision: to create a more personal, context-aware writing experience powered by AI. At the heart of this mission is memory. Flow uses Supermemory as a persistent memory layer, enabling users to ask questions about any of their notes, retrieve key

The UX and technicalities of awesome MCPs

The UX and technicalities of awesome MCPs

Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. Seems like people absolutely loved the concept

Architecting a memory engine inspired by the human brain

Engineering Featured

Architecting a memory engine inspired by the human brain

Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. Every leap in context window size is quickly outpaced by real-world demands: