RT Avi Chawla (@_avichawla)The more your agent remembers, the less it knows.This sounds counterintuitive, but it is actually a direct result of how agent memory is built today.Agent memory inherits the cognitive shape of its store.- A vector DB gives it associative memory to recognize familiar patterns.- A graph gives it relational memory to understand how things connect.Most agents run on the first and skip the second.Here's an example that explains the failure it leads to:Say a study assistant stores thre ...

The more your agent remembers, the less it knows.This sounds counterintuitive, but it is actually a direct result of how agent memory is built today.Agent memory inherits the cognitive shape of its store.- A vector DB gives it associative memory to recognize familiar patterns.- A graph gives it relational memory to understand how things connect.Most agents run on the first and skip the second.Here's an example that explains the failure it leads to:Say a study assistant stores three facts about a student in ...

build agents that never forget.(100% open-source, self-evolving AI memory)most agents have no real memory. every conversation starts fresh with no recall of yesterday and no understanding of how information connects.and here's where most people go wrong when trying to fix this: they rely entirely on vector databases and call it a day.vector search is fast, but it treats your documents as isolated chunks with no understanding of how they connect. what your agent actually needs is memory that captures relatio ...

RT Avi Chawla (@_avichawla)RAG was never the end goal.Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.RAG (2020-2023):- Retrieve info once, generate response- No decision-making, just fetch and answer- Problem: Often retrieves irrelevant contextAgentic RAG:- Agent decides if retrieval is needed- Agent picks which source to query- Agent validates if results are useful- Problem: Still read-only, can't learn from interactionsAI Memory:- Read AND ...

RAG was never the end goal.Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.RAG (2020-2023):- Retrieve info once, generate response- No decision-making, just fetch and answer- Problem: Often retrieves irrelevant contextAgentic RAG:- Agent decides if retrieval is needed- Agent picks which source to query- Agent validates if results are useful- Problem: Still read-only, can't learn from interactionsAI Memory:- Read AND write to external knowledg ...