WikiMoth

Connects the dots. The same way, every time.

wikimoth.com · pip install wikimoth

Deterministic, token-minimal, auditable memory for Claude and agents. Point WikiMoth at a folder of [[wikilink]] notes (an Obsidian vault, or Claude's own memory folder) and it follows the authored links to the answer flat search can't reach, shows you the exact note-chain behind it, and feeds the reader ~99% fewer tokens than pasting the whole vault. Pure markdown, no GPU, no vector DB, no LLM in the retrieval loop.

pip install wikimoth
wikimoth demo         # instant multi-hop recall over a bundled demo vault (no setup)

Already have a [[wikilink]] vault (an Obsidian vault, a notes folder)? Point WikiMoth at it and get the full connect-the-dots view in one command, no capture, no waiting:

wikimoth serve --vault /path/to/your/vault      # browse + "what memory fed this answer"
wikimoth recall --vault /path/to/your/vault "a connect-the-dots question"

Want it to build memory from your Claude Code sessions? Install the capture hooks; each session you run and close is written into a [[wikilink]] vault:

wikimoth install      # capture: turn your Claude Code sessions into a [[wikilink]] vault
wikimoth serve        # once you have captured sessions, browse them

Why not just let Claude manage its own context?

We benchmarked exactly that. An agent that browses the notes folder and prunes its own context reaches the same answers, multi-hop included (12/12 in our run). It just pays for it: 4 to 6 model round-trips and roughly 10x the billed tokens per question, because it re-sends a growing transcript every step. WikiMoth retrieves the same note-chain in one deterministic pass, no model in the loop, and shows you the exact notes behind the answer.

_{Real run, Claude Sonnet 4.6, 12 multi-hop questions on a reproducible vault. The ~10x counts a reader on both sides; it is corpus-specific, not a universal law. Reproduce it with python scripts/run_agentic_benchmark.py. Full breakdown in Honest limits.}

Why WikiMoth

Most agent memory is either paste the whole notes folder into context (expensive, and the model gets lost in the middle) or LLM-summarised similarity search (lossy, and non-deterministic: the same question can return different memory next week). WikiMoth takes a different bet: your notes are the store (plain markdown), the graph is authored (your [[wikilinks]], no embeddings to train or drift), and retrieval is code, not a model, so it's reproducible and you can read exactl

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