I think everyone's ended up building one of these for themselves. I did too[0]. In the end it's quite easy these days:
* I use the bge-en-base CPU embedding model
* I put storage behind a simple endpoint that has read,write,update,search semantics
* The endpoint just stores markdown in an S3 like structure (bucket-key-value; tree structure is inferred) and vector indexes
* The actual persistence is just SQLite
Most modern models are pretty good at handling this. Our home agents (voice and text) use this to store information and I also have skills for claude code and codex to do that as well. Overall, works quite well.
I still use MEMORY.md for the home agents to keep short-term memory, and use the KB for long-term memory. You do need a self-reflection/consolidation/dream step in order to periodically consolidate and groom the KB but that's also just another job.
The whole setup is pretty trivial and what anyone would come up with from scratch and it's surprising that it works but it does. I'm a pretty good documenter so it helps. In some future perhaps a vision model could see all the things I'm doing and remember, but for now I just tell it and it's pretty good about everything I want.
I wasn't but now, doing a lot of work inside a harness that is logging everything and can easily create docs from that, I'm finally persisting a lot of knowledge.
I like that this stays local instead of depending on a hosted service. One thing I've noticed while building LLM applications is that memory becomes much more valuable when it's easy to inspect and edit manually. Are you planning to support semantic search later, or is the goal to keep everything deterministic and text-based?
- Save everything to disk. Index it or store in vectorDB.
- Search the storage for similarity based on the new prompt
- include any finding with the new prompt as system/user prompt
(or if you find the exact answer skip the llm call)
Maybe somebody can enlighten me... this page for ctx says the program saves all "decisions, constraints, intent, rejected approaches, bug investigations, refactors, file paths, commands, patches, and notes from previous agents."
What am I missing if I instead just instruct the agent to create a handful of files called something like HISTORY.md and tell it to log summarized versions of all of these things inside the project repo whenever something is changed or evaluated? (Which is very close to what I currently do.)
Yes, ctx is the closest cousin here. I actually linked it in my top comment.
This causes a very similar itch, but we went a slightly different route regarding some implementation details
Secrets: The ctx README mentions saving the text as is and warns about the need to review the output data before publishing. deja explicitly removes known secret templates at the indexing stage (replacing them with tags like [redacted:aws-key]).
Syncing: It looks like ctx is getting ready for a cloud beta to be shared by the team. Actually, I just wanted something local and free that I had full control over, so deja does P2P syncing through your existing SSH settings.
Recall that deja has a session startup feature that automatically introduces a bit of context when a new agent is loaded, instead of relying solely on manual search.
Both applications solve the same annoying problem by simply optimizing for different workflows :)
My particular interest in deja was that I found a combination that I couldn't find on this list: zero dependencies, zero LLM calls, verbatim text search, proactive secret cleanup and P2P synchronization over SSH.
We are entering the era of personalized software. I have something similar but I'm not trying to promote or make money with it. I somewhat prefer it stay off the radar so I don't feel the need to cater to other people's feature requests and make it more complex than what I want.
Hi HN. I built deja after watching Claude Code and Codex debug the same problems more than once.
The annoying thing was that the answer usually already existed somewhere in my old sessions. My records were stored on the disk for months (~3.3 GB). It wasn't easy to find them manually and the new agent session had no idea what the other agent had already found out.
deja indexes the transcripts that Claude Code, Codex, and opencode already write. On my corpus, the initial index takes about 10 seconds and warm searches are 7-9 ms.
There are 3 ways to get the memory back: a normal CLI search, an MCP tool (agent can query it directly) and a SessionStart hook that automatically injects a bit of relevant project context.
The feature I built this for:
deja sync ssh <host>
It moves new memory between machines using the existing SSH setup. Secret data is deleted during indexing and checked again before exporting.
My setup is a laptop and a mac mini without an interface. The agent can work on the mini all night, and in the morning I extract its memory. Then the agent on my laptop will know what the mini tried, what broke, and what eventually worked.
I wanted deja to intentionally use a small approach: one binary code with zero dependency, no LLM calls, searchable verbatim text instead of generated summaries, and synchronization via your own SSH. It does not implement its own network transport; it just connects to the system ssh/scp.
You can install it via curl, brew, npx or go install, then run:
> My setup is a laptop and a mac mini without an interface. The agent can work on the mini all night, and in the morning I extract its memory. Then the agent on my laptop will know what the mini tried, what broke, and what eventually worked.
If it’s fully automated and then blindly injected into your laptop without any vetting, isn’t that a perfect vector to break that separation?
I did an automatic review of over 140 such systems.
There are comparisons and stuff: https://zby.github.io/commonplace/agent-memory-systems/
Deja-vu is here: https://zby.github.io/commonplace/agent-memory-systems/revie...
And the full list: https://zby.github.io/commonplace/agent-memory-systems/revie...
I think everyone's ended up building one of these for themselves. I did too[0]. In the end it's quite easy these days:
* I use the bge-en-base CPU embedding model
* I put storage behind a simple endpoint that has read,write,update,search semantics
* The endpoint just stores markdown in an S3 like structure (bucket-key-value; tree structure is inferred) and vector indexes
* The actual persistence is just SQLite
Most modern models are pretty good at handling this. Our home agents (voice and text) use this to store information and I also have skills for claude code and codex to do that as well. Overall, works quite well.
I still use MEMORY.md for the home agents to keep short-term memory, and use the KB for long-term memory. You do need a self-reflection/consolidation/dream step in order to periodically consolidate and groom the KB but that's also just another job.
The whole setup is pretty trivial and what anyone would come up with from scratch and it's surprising that it works but it does. I'm a pretty good documenter so it helps. In some future perhaps a vision model could see all the things I'm doing and remember, but for now I just tell it and it's pretty good about everything I want.
0: https://wiki.roshangeorge.dev/w/Blog/2026-07-01/The_Everythi...
> I'm a pretty good documenter so it helps.
I wasn't but now, doing a lot of work inside a harness that is logging everything and can easily create docs from that, I'm finally persisting a lot of knowledge.
I like that this stays local instead of depending on a hosted service. One thing I've noticed while building LLM applications is that memory becomes much more valuable when it's easy to inspect and edit manually. Are you planning to support semantic search later, or is the goal to keep everything deterministic and text-based?
I can't even get my agents to properly read the memories they have saved locally let alone remotely.
Does memory for AI agents basically mean -
- Save everything to disk. Index it or store in vectorDB. - Search the storage for similarity based on the new prompt - include any finding with the new prompt as system/user prompt (or if you find the exact answer skip the llm call)
?
Or is there more to it ?
Different implementation details have different way to rank or “forget” or supersede facts but that’s pretty much it
Still waiting for the agentic memory system for the local Mainframe emulator DB2 instance.
Similar to https://ctx.rs/ and others, I'm sure.
I'd lead with your differentiation. Is it the ssh?
Maybe somebody can enlighten me... this page for ctx says the program saves all "decisions, constraints, intent, rejected approaches, bug investigations, refactors, file paths, commands, patches, and notes from previous agents."
What am I missing if I instead just instruct the agent to create a handful of files called something like HISTORY.md and tell it to log summarized versions of all of these things inside the project repo whenever something is changed or evaluated? (Which is very close to what I currently do.)
It deterministically pulls the full session log so you don't need to tell an LLM to do it, plus it doesn't pollute your repo with such files.
> doesn't pollute your repo with such files.
An orphan branch solves that issue.
Yes, ctx is the closest cousin here. I actually linked it in my top comment.
This causes a very similar itch, but we went a slightly different route regarding some implementation details
Secrets: The ctx README mentions saving the text as is and warns about the need to review the output data before publishing. deja explicitly removes known secret templates at the indexing stage (replacing them with tags like [redacted:aws-key]).
Syncing: It looks like ctx is getting ready for a cloud beta to be shared by the team. Actually, I just wanted something local and free that I had full control over, so deja does P2P syncing through your existing SSH settings.
Recall that deja has a session startup feature that automatically introduces a bit of context when a new agent is loaded, instead of relying solely on manual search.
Both applications solve the same annoying problem by simply optimizing for different workflows :)
even more: https://hn.algolia.com/?dateRange=pastMonth&page=0&prefix=fa...
My particular interest in deja was that I found a combination that I couldn't find on this list: zero dependencies, zero LLM calls, verbatim text search, proactive secret cleanup and P2P synchronization over SSH.
We are entering the era of personalized software. I have something similar but I'm not trying to promote or make money with it. I somewhat prefer it stay off the radar so I don't feel the need to cater to other people's feature requests and make it more complex than what I want.
Hi HN. I built deja after watching Claude Code and Codex debug the same problems more than once.
The annoying thing was that the answer usually already existed somewhere in my old sessions. My records were stored on the disk for months (~3.3 GB). It wasn't easy to find them manually and the new agent session had no idea what the other agent had already found out.
deja indexes the transcripts that Claude Code, Codex, and opencode already write. On my corpus, the initial index takes about 10 seconds and warm searches are 7-9 ms.
There are 3 ways to get the memory back: a normal CLI search, an MCP tool (agent can query it directly) and a SessionStart hook that automatically injects a bit of relevant project context.
The feature I built this for:
deja sync ssh <host>
It moves new memory between machines using the existing SSH setup. Secret data is deleted during indexing and checked again before exporting.
My setup is a laptop and a mac mini without an interface. The agent can work on the mini all night, and in the morning I extract its memory. Then the agent on my laptop will know what the mini tried, what broke, and what eventually worked.
There are other projects (cass, ctx, claude-mem):
https://github.com/Dicklesworthstone/coding_agent_session_se...
https://github.com/ctxrs/ctx
https://github.com/thedotmack/claude-mem
I wanted deja to intentionally use a small approach: one binary code with zero dependency, no LLM calls, searchable verbatim text instead of generated summaries, and synchronization via your own SSH. It does not implement its own network transport; it just connects to the system ssh/scp.
You can install it via curl, brew, npx or go install, then run:
deja install --all
> My setup is a laptop and a mac mini without an interface. The agent can work on the mini all night, and in the morning I extract its memory. Then the agent on my laptop will know what the mini tried, what broke, and what eventually worked.
If it’s fully automated and then blindly injected into your laptop without any vetting, isn’t that a perfect vector to break that separation?
I just wonder how do you filter out signal from noise - does it self correct wrong memories?