We're exploring related ideas in embodied AI rather than LLM agents. MH-FLOCKE uses Izhikevich spiking neurons with R-STDP to control quadruped locomotion — the memory is in the synaptic weights, not in a vector store.
The brain persists across sessions: stop the robot, restart it, synaptic weights reload and it continues from where it left off. Decay happens naturally through R-STDP — synapses that don't contribute to reward weaken over time. No explicit forgetting mechanism needed.
Currently running on a Unitree Go2 (MuJoCo) and a 100€ Freenove robot dog (Raspberry Pi 4, real hardware). Same architecture, different bodies.
The biggest issue I have with these systems is, I don't want a blanket memory. I want everything to be embedded in skills and progressively discovered when they are required.
I've been playing around with doing that with a cron job for a "dream" sequence.
I really want to get them out of main context asap, and where they belong, into skills.
Blanket memory doesn't scale, totally agree. I built something similar in Atmita (https://atmita.com). Agents see short summaries of each other instead of full memory dumps, and automation run logs live in their own layer.
I've long held the belief that if you want to simulate human behaviour, you need human-like memory storage, because so much of our behaviour is influenced by how our memories work. Even something as stupid as walking into between rooms and forgetting why you went there, is a behaviour that would otherwise have to be simulated directly but can be indirectly simulated by the memory of why an Agent is moving from room to room having a chance of disappearing.
Now, as for how useful this will be for something that isn't trying to directly simulate a human and is trying to be "superintelligent", I'm not entirely sure, but I am excited that someone is exploring it.
The memory system I am working on is specifically targeted at simulating human memory and retrieval patterns, including memory consolidation during sleep cycles. I would love to discuss the topic more with you - I'll look into getting access to your papers.
Cool project. I like the neuroscience analogy with decay and consolidation.
I've been working on a related problem from the other direction: Claude Code and Codex already persist full session transcripts, but there's no good way to search across them. So I built ccrider (https://github.com/neilberkman/ccrider). It indexes existing sessions into SQLite FTS5 and exposes an MCP server so agents can query their own conversation history without a separate memory layer. Basically treating it as a retrieval problem rather than a storage problem.
no open code plugin? This seems like something that should just run in the background. It's well documented that it should just be a skill agents can use when they get into various fruitless states.
The "biological" memory strength shouldn't just be a time thing, and even then, the time of the AI agent should only be conformed to the AI's lifetime and not the actual clock. Look up https://stackoverflow.com/questions/3523442/difference-betwe... monotonic clock. If you want a decay, it shouldn't be related to an actual clock, but it's work time.
But memory is more about triggers than it is about anything else. So you should absolutely have memory triggers based on location. Something like a path hash. So whever an agent is working and remembering things it should be tightly compacted to that location; only where a "compaction" happens should these memories become more and more generalized to locations.
The types of memory that often are more prominent are like this, whether it's sports or GUIs, physical location triggers much more intrinsics than conscious memory. Focus on how to trigger recall based on project paths, filenames in the path, file path names, etc.
I think explicit post-training is going to be needed to make this kind of approach effective.
As this repo notes is "The secret to good memory isn't remembering more. It's knowing what to forget." But knowing what is likely to be important in the future implies a working model of the future and your place in it. It's a fully AGI complete problem: "Given my current state and goals, what am I going to find important conditioned on the likelihood of any particular future...". Anyone working with these agents knows they are hopelessly bad at modeling their own capabilities much less projecting that forward.
Nice work! I have been thinking along similar lines and designed a simulation of human memory using a tiered database design with hot/warm/cold storage, temporal data, and graph relationship nodes. Hot memory is processed by an LLM during "sleep cycles" or downtime as I have seen others mention in the comments.
You have some novel approaches here which I have learned a lot from! Your hypershpere physics approach is fascinating - it's a different approach than I took, but it accomplishes some tasks without an LLM. Your importance-based eviction system can significantly reduce the size of the ephemeral session state before it gets processed to persistent memory by the LLM, and your half-life knowledge decay mechanism is more elegant than the temporal approach I took.
If I am finally allowed to post a show hn, I'll post a few details, but our projects mostly solve different things and are complimentary. I can certainly use some things in Hippo to improve my system, maybe there is something that would interest you in mine -- Memforge (https://github.com/salishforge/memforge) if you're interested.
hmm the repo doesnt mention this at all but this name and problem domain brings up HippoRAG https://arxiv.org/abs/2405.14831 <- any relation? seems odd to miss out this exactly similarly named paper with related techniques.
Aren't tools like claude already store context by project in file system? Also any reason use "capture" instead of "export" (an obvious opposite of import)?
How does it select what to forget? Let's say I land a PR that introduces a sharp change, migrating from one thing to another. An exponential decay won't catch this. Biological learning makes sense when things we observe similar things repeatedly in order to learn patterns. I am skeptical that it applies to learning the commits of one code base.
Are there any natrual ways of swapping from clock time to agent "active time"? For some agents that are running intermittently I might want to keep those memories longer (in clock time).
Thank you again for all the feedback. I have made a lot of significant changes to the repo. Make sure you update it to the latest version and lots of options on how you can best utilise it.
29 comments
The brain persists across sessions: stop the robot, restart it, synaptic weights reload and it continues from where it left off. Decay happens naturally through R-STDP — synapses that don't contribute to reward weaken over time. No explicit forgetting mechanism needed.
Currently running on a Unitree Go2 (MuJoCo) and a 100€ Freenove robot dog (Raspberry Pi 4, real hardware). Same architecture, different bodies.
github.com/MarcHesse/mhflocke
I've been playing around with doing that with a cron job for a "dream" sequence.
I really want to get them out of main context asap, and where they belong, into skills.
https://github.com/notque/claude-code-toolkit
What you want is a context mipmap.
Then there was the Claude article describing using filesystem hierarchy to organize markdown knowledge, which apparently beats RAG.
I've long held the belief that if you want to simulate human behaviour, you need human-like memory storage, because so much of our behaviour is influenced by how our memories work. Even something as stupid as walking into between rooms and forgetting why you went there, is a behaviour that would otherwise have to be simulated directly but can be indirectly simulated by the memory of why an Agent is moving from room to room having a chance of disappearing.
Now, as for how useful this will be for something that isn't trying to directly simulate a human and is trying to be "superintelligent", I'm not entirely sure, but I am excited that someone is exploring it.
https://ieeexplore.ieee.org/abstract/document/5952114 https://ieeexplore.ieee.org/abstract/document/5548405 https://ieeexplore.ieee.org/abstract/document/5953964
I never did get many citations for these, maybe I just wasn't very good at "marketing" my papers.
I've been working on a related problem from the other direction: Claude Code and Codex already persist full session transcripts, but there's no good way to search across them. So I built ccrider (https://github.com/neilberkman/ccrider). It indexes existing sessions into SQLite FTS5 and exposes an MCP server so agents can query their own conversation history without a separate memory layer. Basically treating it as a retrieval problem rather than a storage problem.
The "biological" memory strength shouldn't just be a time thing, and even then, the time of the AI agent should only be conformed to the AI's lifetime and not the actual clock. Look up https://stackoverflow.com/questions/3523442/difference-betwe... monotonic clock. If you want a decay, it shouldn't be related to an actual clock, but it's work time.
But memory is more about triggers than it is about anything else. So you should absolutely have memory triggers based on location. Something like a path hash. So whever an agent is working and remembering things it should be tightly compacted to that location; only where a "compaction" happens should these memories become more and more generalized to locations.
The types of memory that often are more prominent are like this, whether it's sports or GUIs, physical location triggers much more intrinsics than conscious memory. Focus on how to trigger recall based on project paths, filenames in the path, file path names, etc.
As this repo notes is "The secret to good memory isn't remembering more. It's knowing what to forget." But knowing what is likely to be important in the future implies a working model of the future and your place in it. It's a fully AGI complete problem: "Given my current state and goals, what am I going to find important conditioned on the likelihood of any particular future...". Anyone working with these agents knows they are hopelessly bad at modeling their own capabilities much less projecting that forward.
You have some novel approaches here which I have learned a lot from! Your hypershpere physics approach is fascinating - it's a different approach than I took, but it accomplishes some tasks without an LLM. Your importance-based eviction system can significantly reduce the size of the ephemeral session state before it gets processed to persistent memory by the LLM, and your half-life knowledge decay mechanism is more elegant than the temporal approach I took.
If I am finally allowed to post a show hn, I'll post a few details, but our projects mostly solve different things and are complimentary. I can certainly use some things in Hippo to improve my system, maybe there is something that would interest you in mine -- Memforge (https://github.com/salishforge/memforge) if you're interested.
we're building swarm-like agent memory agents share memories across rooms and nodes. Reading Steiner + Time Leap Capsules (yeah, Steins;Gate easter eggs lol).
your consolidation and decay mechanics are close to what we want. might integrate similar approach.
Here's a post I wrote about how we can start to potentially mimic mechanisms
https://n0tls.com/2026-03-14-musings.html
Would love to compare notes, I'm also looking at linguistic phenomena through an LLM lens
https://n0tls.com/2026-03-19-more-musings.html
Hoping to wrap up some of the kaggle eval work and move back to researching more neuropsych.