io.github.toonight/mnemoscope

An open-source observability layer for LLM agent memory on Markdown vaults.
Predict context rot before it happens · audit every agent write with a signed journal · tier your knowledge the way the science says you should.

_{Demo · Workflow · Tools · Quickstart · Architecture · Science}

[!NOTE] The dominant 2025–2026 narrative on X — "Markdown trips up the LLM at scale" — is partially wrong. Markdown does not trip up the LLM. Long-context loading trips up the LLM (Chroma, Context Rot, July 2025). Mnemoscope is built on that distinction.

👀 See it in action

Real output, captured from the bundled examples/demo-vault — a 13-note synthetic vault built so every rot factor moves. Reproduce locally with mnemoscope-init examples/demo-vault (full transcript: SAMPLE-OUTPUT.md).

predict_rot — score, factors, top-risk notes

mnemoscope-verify — clean run vs. tamper detection

Full overview (click) — gauge, factors, top-risk notes, both verify states, tier counts, hash chain — all on one page

✨ What is Mnemoscope?

Mnemoscope is not another memory store. It is an instrument that sits between your LLM agent and your Markdown vault and gives you three things nobody else gives you in one tool:

• 🎯 Predict the rot risk of a corpus before injection, with a citation-backed score across 5 factors.
• 📝 Witness every read and write your agent performs, in an Ed25519-signed, hash-chained journal that detects field-level tampering, deletion, and reordering.
• 🧱 Tier the corpus into a working / episodic / semantic hierarchy, drawing on the 2025–2026 science instead of the GraphRAG hype.

It ships as an MCP server (Claude Code, Cursor, ChatGPT desktop, any

... View full README on GitHub