Cool idea. Why is this beneficial over just using markdown files and allowing agents to grep for whatever they need? I've tried various MCP things in the past and I've found they tend to slow down the agent and waste tokens more than they end up helping, but a better memory system is 100% needed for agents.
Please excuse my noob-ish, naïve question, but to what extent is the business of getting the LLM to actually consult memory a model-dependent thing? Do you have to introduce the tool and guide models with different language for different model families?
Looking at your tool descriptions (as wit the ones on the original post) I wonder if this something perhaps only current frontier models will do, but the systems themselves seem like they'd be even more useful for open weights models with shorter working contexts.
Since you built something on OKF, how would you contrast it with knowledge graph implementations? How do you manage the ontology of what to keep knowledge about? Any cases where traversal would have helped?
I have seen the value of recording past sessions but I am more skeptical of the value in recording facts, which may soon become stale, about a constantly changing code base. Got benchmarks?
Well, do you see the value of writing notes for yourself occasionally, even though they might soon become stale in your constantly changing environment? Yes, right?
Same principle. It's a good idea to have a schedule to clean them up periodically - an idea you can also put into a note.
Cool idea. Why is this beneficial over just using markdown files and allowing agents to grep for whatever they need? I've tried various MCP things in the past and I've found they tend to slow down the agent and waste tokens more than they end up helping, but a better memory system is 100% needed for agents.
Looks very interesting. Can you explain for noobs why using Google's OKF format and not plain MD files?
OKF is basically md files with front-matter for meta data
Nice to see more OKF-based approaches. My entry in this field is https://rcarmo.github.io/projects/memento/, which I’ve been running for a few months now.
Please excuse my noob-ish, naïve question, but to what extent is the business of getting the LLM to actually consult memory a model-dependent thing? Do you have to introduce the tool and guide models with different language for different model families?
Looking at your tool descriptions (as wit the ones on the original post) I wonder if this something perhaps only current frontier models will do, but the systems themselves seem like they'd be even more useful for open weights models with shorter working contexts.
Since you built something on OKF, how would you contrast it with knowledge graph implementations? How do you manage the ontology of what to keep knowledge about? Any cases where traversal would have helped?
I have seen the value of recording past sessions but I am more skeptical of the value in recording facts, which may soon become stale, about a constantly changing code base. Got benchmarks?
Well, do you see the value of writing notes for yourself occasionally, even though they might soon become stale in your constantly changing environment? Yes, right?
Same principle. It's a good idea to have a schedule to clean them up periodically - an idea you can also put into a note.
did you test if that actually outperforms local claude code memory by any metric?