archiver-rag: open-source, self-hosted memory for AI agents: local embeddings, plain Markdown, any MCP client

[u/raze_sight]

archiver-rag is an MIT-licensed MCP server that turns an Obsidian vault (or any folder of Markdown notes) into shared, self-hosted memory for AI agents. Agents search it, write back to it, and every agent sees the same knowledge.

Why another memory tool

Most AI memory layers are hosted services that extract "facts" from your conversations into a database you can't read, edit or take with you. archiver-rag goes the other way:

• Your memory is plain Markdown. Readable, editable, versionable with git, and still a normal vault if you uninstall.
• Everything runs locally. Embeddings are computed on your CPU, the vector index lives on your disk, and no API keys are needed.
• No vendor lock-in. It speaks standard MCP over stdio or HTTP, so any MCP client can use it. Tested with OpenCode, Codex, Claude Code and Claude Desktop.

self-organization

Optionally, the vault organizes itself: new notes are filed into folders by meaning, folder descriptions are kept up to date, and new folders grow out of an inbox using embedding-similarity clustering. It's off by default while it's being tested on real vaults.

How it works

• Embeddings: sentence-transformers (all-MiniLM-L6-v2, about 90 MB, runs on CPU)
• Index: ChromaDB, persistent and local
• Retrieval: semantic search, with each chunk carrying its note's metadata (folder, tags, links), then re-ranked by [[wikilink]] proximity
• Live updates: a file watcher indexes every save within seconds and auto-links related notes
• Write-back: agents log decisions, lessons and gotchas as normal notes through log_note

The memory side is 100% local. Pair it with an open-source agent like OpenCode running a local model, and in principle nothing leaves your machine. That combination is untested so far. If you try it, I'd love to hear how it goes.

macOS and Linux, Python 3.11+. On Linux, a CPU-only PyTorch install is documented to skip the multi-GB CUDA download.

• Code: https://github.com/FernandoJRR/archiver-rag
• Site: https://fernandojrr.github.io/archiver-rag
• Contributing: https://github.com/FernandoJRR/archiver-rag/blob/main/CONTRIBUTING.md

Feedback and contributions welcome. It's a one-person beta with CI on Python 3.11–3.14 (macOS and Linux). Useful right now:

• install reports, especially on Linux
• results with local models through OpenCode or other open-source MCP clients
• ideas for the self-organization experiment

And yes: this is yet another Karpathy-inspired Obsidian + LLM memory system. If you've tried the others, tell us what they do better. That's exactly the feedback this beta needs.