mnexa 0.0.1

A disciplined wiki maintainer for a personal markdown knowledge base.

Mnexa

A disciplined wiki maintainer for a personal markdown knowledge base. You drop documents into raw/; an LLM reads them and maintains a structured wiki of source / entity / concept pages with cross-references, an index, and a log. You curate; the LLM does the bookkeeping.

Implementation of the pattern in Andrej Karpathy's LLM Wiki gist — read that first; it's the design spec.

Why

Most LLM document tools are RAG: retrieve chunks at query time, generate from the chunks, throw the synthesis away. Mnexa treats the wiki as a persistent, compounding artifact — every ingest updates entity and concept pages once, every query runs against accumulated synthesis instead of re-deriving from raw sources. Open the wiki in Obsidian, Logseq, VS Code, or any markdown editor. The LLM is the maintainer; you are the curator.

Install

Requires Python 3.12+ and uv. Get a Gemini API key at https://aistudio.google.com/apikey.

git clone https://github.com/jiashuoz/mnexa
cd mnexa
uv sync
cp .env.example .env   # then paste your GOOGLE_API_KEY

Use

# Create a new vault
uv run mnexa init ~/my-vault

# Drop a source into raw/, then ingest
cp some-paper.pdf ~/my-vault/raw/
cd ~/my-vault
uv run --project /path/to/mnexa mnexa ingest raw/some-paper.pdf

# Ask the wiki a question
uv run --project /path/to/mnexa mnexa query "what does this paper claim?"

# Audit the wiki
uv run --project /path/to/mnexa mnexa lint

Vault layout

my-vault/
├── .git/
├── .gitignore                  # ignores .mnexa/ and .env
├── .mnexa/                     # Mnexa local state (lint reports)
├── CLAUDE.md                   # the schema — edit §6 to customize
├── raw/                        # immutable source documents
└── wiki/
    ├── index.md                # categorized table of contents
    ├── log.md                  # append-only activity log
    ├── sources/                # one page per ingested document
    ├── entities/               # people, orgs, products, places
    └── concepts/               # ideas, techniques, recurring topics

Every successful ingest is a git commit. Free undo, free history, free diff.

How it works

Ingest is a two-stage pipeline:

1. Analyze — LLM reads the source plus the schema, index, and obviously-related existing pages. Produces a structured analysis (entities, concepts, claims, contradictions). Internal scratch.
2. Generate — LLM emits FILE blocks for the new/updated wiki pages. Mnexa parses, validates paths and frontmatter, substring-verifies that every ⟦"..."⟧ source-quote marker appears verbatim in the source, then atomically writes and commits.

The substring verifier is the anti-hallucination floor. If the LLM invents a biographical detail not present in the source, the marker check fails and the ingest aborts with no on-disk changes.

Query is a single LLM call against index.md + the top-N pages by keyword overlap, streamed to stdout with inline [[wikilink]] citations. Logged to log.md.

Lint runs deterministic checks first (broken links, frontmatter, index/wiki sync, orphans, ungrounded pages, slug style), then one LLM call for semantic checks (contradictions, stale claims, missing pages, slug typos). Output: .mnexa/lint/<timestamp>.md.

LLM

Provider-agnostic via a small LLMClient protocol. v0 ships Google Gemini (default gemini-3-flash-preview). Set MNEXA_MODEL to any gemini-* model; set MNEXA_PROVIDER to override the auto-inference. Adding Anthropic or OpenAI is ~80 lines plus an extras entry — not shipped because no one needs it yet.

Status

mnexa init	✅
mnexa ingest	✅ (.md, .txt, .pdf, .docx)
mnexa query	✅
mnexa lint	✅
mnexa lint --fix	not yet (v0.1)
save query answer as wiki page	not yet (v0.1)
Anthropic / OpenAI providers	not yet

Develop

uv sync --all-extras
uv run pytest         # 45 tests
uv run ruff check .
uv run pyright        # strict

Prompts live as files in src/mnexa/prompts/ and load via importlib.resources. Edit them, rerun, iterate.

Design notes

• Pure markdown is the canonical store. No SQLite, no vector index, no FTS5. Karpathy's gist argues index.md is enough at moderate scale; we believe it until measurements say otherwise.
• Two-stage ingest is borrowed from nashsu/llm_wiki; the deterministic-then-LLM lint tier is borrowed from SamurAIGPT/llm-wiki-agent. The substring-grounding verifier is novel — neither reference project does it.
• Atomic-ish writes via stage-then-rename + git checkout HEAD -- rollback on failure. The git commit is the durability barrier.
• Gemini context caching is a no-op at our schema size (~3k tokens, below the threshold). The protocol still expresses intent so other providers can honor it.