The markdown approach has a real advantage people underestimate: you can read and edit the memory yourself. With vector DBs and embeddings the memory becomes opaque — you can't inspect or correct what the model "knows". Plain files keep the human in the loop.
The hard part is usually knowing what +not+ to write down. Every system I've seen eventually drowns in low-signal entries.
This assumes that the model's behavior and memories are faithful to their english/human language representation, and don't stray into (even subtle) "neuralese".
I've seen a lot of such systems come and go. One of my friends is working on probably the best (VC-funded) memory system right now.
The problem always is that when there are too many memories, the context gets overloaded and the AI starts ignoring the system prompt.
Definitely not a solved problem, and there need to be benchmarks to evaluate these solutions. Benchmarks themselves can be easily gamed and not universally applicable.
The armchair ML engineer in me says our current context management approach is the issue. With a proper memory management system wired up to it’s own LLM-driven orchestrator, memories should be pulled in and pushed out between prompts, and ideally, in the middle of a “thinking” cycle. You can enhance this to be performant using vector databases and such but the core principle remains the same and is oft repeated by parents across the world: “Clean up your toys before you pull a new one out!”
Also since I thought for another 30 seconds, the “too many memories!” Problem imo is the same problem as context management and compaction and requires the same approach: more AI telling AI what AI should be thinking about. De-rank “memories” in the context manager as irrelevant and don’t pass them to the outer context. If a memory is de-ranked often and not used enough it gets purged.
What is the memory system you are referring to? I've been trying Memori with OpenClaw. Haven't had a ton of time to really kick the tires on it, so the jury's still out.
The markdown approach has a real advantage people underestimate: you can read and edit the memory yourself. With vector DBs and embeddings the memory becomes opaque — you can't inspect or correct what the model "knows". Plain files keep the human in the loop.
The hard part is usually knowing what +not+ to write down. Every system I've seen eventually drowns in low-signal entries.
This assumes that the model's behavior and memories are faithful to their english/human language representation, and don't stray into (even subtle) "neuralese".
Is there anything (besides plumbing) that prevents both? i.e. when the file is edited, all the representations are updated
I've seen a lot of such systems come and go. One of my friends is working on probably the best (VC-funded) memory system right now.
The problem always is that when there are too many memories, the context gets overloaded and the AI starts ignoring the system prompt.
Definitely not a solved problem, and there need to be benchmarks to evaluate these solutions. Benchmarks themselves can be easily gamed and not universally applicable.
The armchair ML engineer in me says our current context management approach is the issue. With a proper memory management system wired up to it’s own LLM-driven orchestrator, memories should be pulled in and pushed out between prompts, and ideally, in the middle of a “thinking” cycle. You can enhance this to be performant using vector databases and such but the core principle remains the same and is oft repeated by parents across the world: “Clean up your toys before you pull a new one out!”
Also since I thought for another 30 seconds, the “too many memories!” Problem imo is the same problem as context management and compaction and requires the same approach: more AI telling AI what AI should be thinking about. De-rank “memories” in the context manager as irrelevant and don’t pass them to the outer context. If a memory is de-ranked often and not used enough it gets purged.
Mid thinking cycle seems dangerous as it will probably kill caching.
The mid thinking cycle would require significant architecture change to current state of art and imo is a key blocker to AGI
What is the memory system you are referring to? I've been trying Memori with OpenClaw. Haven't had a ton of time to really kick the tires on it, so the jury's still out.
I love how you approached this with markdown !
I guess the markdown approach really has a advantage over others.
PS : Something I built on markdown : https://voiden.md/
I really like the simplicity of this! What's retrieval performance and speed like?