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Open-source memory for coding agents, synced over SSH

GitHub - vshulcz/deja-vu: Memory layer for coding agents: search, MCP recall, auto-context, secret redaction, stats, share and sync over the session logs Claude Code, Codex and opencode already write. One zero-dep binary.

github.com

July 15, 2026

6 min read

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50/100

Summary

Deja-vu is a zero-dependency binary that creates a memory layer for coding agents, allowing users to search through extensive conversation histories generated by Claude Code, Codex, and opencode. It features a rapid search capability and a recall tool that enables agents to reference past solutions efficiently.

Key Takeaways

  • Deja is a zero-dependency binary that creates a memory layer for coding agents, allowing users to search through gigabytes of historical data from previous conversations and design decisions.
  • The tool features include agent recall, auto-recall, secret redaction, and session sharing, enabling efficient retrieval and management of coding information.
  • Deja supports synchronization of memory between machines through export/import commands, ensuring that records are append-only and idempotent.
  • Installation can be done via various package managers or directly through a shell command, making it easy to integrate with existing coding agents like Claude Code and Codex.
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Community Sentiment

Positive

Positives

  • The local storage approach is a game changer — it gives users full control over their memory, making it easy to inspect and edit.
  • Deja's ability to index past agent sessions in seconds has commenters buzzing about efficiency in retrieving previously solved problems.
  • Using a dictionary for memory retrieval is smart; it keeps things deterministic and focused on the exact details needed, unlike the chaos of semantic search.

Concerns

  • Concerns are raised about blindly injecting memories across devices — that could easily introduce errors and compromise separation.
  • Some users worry that filtering out signal from noise in memory systems is a significant challenge that hasn't been adequately addressed.

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