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Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…
It’s good that a lot of people are trying a lot of things when it comes to agentic memory. Sadly none of it represents a complete solution at this time. But we need the experimentation.
I still dont understand what "agentic memory" is . agents can already call sql / rag and grep through files or whatever. why is "agentic memory" a special thing.
I am on linggen.dev , that is the one make daily agent work easier.
It occurs to me: we have latent embedding giving 'general knowledge' to an LLM. What if we use a 'blank' LLM as well as an agent and train that blank LLM on personal context to query that as memory?
Can an LLM be trained to understand language without remembering anything else from its training data? I thought the intrinsic knowledge and the ability to understand language were tied together.
- Store session turns in an sqlite-vec
- Provide the agent with an mcp to search the vec-db
- Let the agent write notes in md files along with an index / frontmatter
Along with the commit history, the vec-db gives the agent long-term memory. The notes allow the user to correct accumulation of false lessons.
Simpler but better.
---
title: Carbon Fibre Woks
created: '2026-03-01T09:00:00Z'
updated: '2026-08-22T14:30:00Z'
uuid: 6aa615f0-486f-48a7-a210-ba4f5ff18c8b
summary: Thermal properties of carbon fibre cookware
---
Carbon fibre woks conduct heat evenly, but...
Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…
It keeps documentation from going out of date by embedding re-runnable verification checks directly inside markdown files (that the agents use, not humans). I use it with a handoff workflow to force Claude/GPT to re-verify facts before handing off documentation to the next session.
Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…
That being said, in coding over a longer time horizon, having the agent continually re-derive decisions/laws/facts/etc from your code is wasteful of tokens and time, and if your code doesn’t consistently apply them you can’t know the agent will make the correct choices.
You need memory of these important facts to avoid this expense or potential incorrectness. Memory does not itself scale though, without maintenance and pruning, and that has its own impacts on cost and correctness like the Chat memory.
“Damned if you do, damned if you don’t” at least until the agent can itself maintain its memory accurately - or some other non-human effort can achieve that.
Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…