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

Similar to https://ctx.rs/ and others, I'm sure.

I'd lead with your differentiation. Is it the ssh?

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?

Still waiting for the agentic memory system for the local Mainframe emulator DB2 instance.

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 ?