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 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 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.
The session history is another database like any other database, can be used or invoked by agent like one.
The primary storage mechanism is .md files stored as Notes in Nextcloud; however, since Nextcloud supports a rich ecosystem of apps such as documents, rss feeds, calendar, etc, your knowledge base can grow with you. All content can optionally be indexed and available via semantic search - powered by a Qdrant vectordb.
The biggest cost drivers of a system like this is the memory required to host the vectordb - I'm really curious how others are optimizing their knowledge base. Thanks OP for doing to work in summarizing these tools!
If you're interested either the MCP server or Nextcloud App frontend, please check out:
Adding semantic search usually means sending hundreds of megabytes of embedded models and working with non-deterministic results which completely violates the promise of zero dependency within 7-9 ms. In fact, I encounter some uncertainty when expanding substrings (e.g. when requesting the code, the open source code will be displayed).
I won't completely abandon semantic search if there is a really easy and fast way to do it locally, but at the moment deterministic search is the priority. And I completely agree with your first comment, the verbatim approach provides just such a verification opportunity.
- 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 ?
Go-to stack for this in my case is to start off with a MEMORY.md, and then keep a long-running agent session going to observe human-operated tmux sessions, surface/respond to occurrences where there's something significant learned (typically the human operator redirecting away from an agent/LLM decision), then start having it integrate into and read out of DuckDB. The latter becomes dense, easily searchable data for agent skills and commands and, yes, memory and such.
First, the SessionStart hook aggressively introduces context immediately after loading, completely bypassing the agent's requirements.
Secondly, I'm forcibly changing the behavior by using a fragment of the system query in CLAUDE.md or AGENTS.md. I'm literally just copypaste this block from the README:
Before debugging or re-implementing anything, run deja "<query>" (or the MCP revocation tool) — the agent's past sessions in Claude Code, Codex, and opencode will be indexed locally. Specify what you are reusing.I'd lead with your differentiation. Is it the ssh?
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 :)
> 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]).
How is this not security theater? You've presumably already sent the api key to an LLM if it's in your session file. You need to rotate it.
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.)
An orphan branch solves that issue.
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/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
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?
However, when syncing, only data records are moved not instructions. Nothing is done and known secrets are deleted at the indexing stage even before they leave the source computer. In addition, the context here is your own computers and your terminal's output data, if an attacker can control your agent's transcripts, the source computer has already been completely compromised.
Nevertheless, you make a perfectly reasonable case for strict isolation. I just opened the issue to add an opt-out checkbox where imported sessions are only displayed when manually searched using the CLI/MCP, not when automatically implemented: https://github.com/vshulcz/deja-vu/issues/39
GitHub: https://github.com/open-latch/latch
I felt the same annoyance as OP mentioned, but focused on agents violating project judgement and direction. Obviously we are optimizing for some different things than deja: deja uses no-LLMs and exact verbatim transcript retrieval.
Latch uses LLMs, ranking, etc, to emphasize strong judgment, correction, and enforcement over dynamic project flow.