Deja-vu: Open-Source Memory Layer for Coding Agents Synced Over SSH
Open-source memory for coding agents, synced over SSH
I built deja-vu, a zero-dependency binary that turns your existing coding agent logs into a powerful, searchable memory layer. It instantly retrieves past solutions from Claude Code, Codex, and opencode while automatically redacting secrets. You can sync this local memory across machines via SSH or shared folders, ensuring your agents never re-debug the same problems without ever sending data to the cloud.
Your agents already solved this. deja finds it.
- zby
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...
- arjie
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...
- BedVibe_Studios
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?
- catzapd
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 ?
- cbcoutinho
I have developed an open-source memory system for agents accessible over MCP, which makes it possible to access it via any coding agent locally (claude-code) or via mobile (Claude AI, Mistral AI, etc).
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: