LLM Wiki Agent

A personal knowledge base that builds and maintains itself. Drop in source documents — articles, papers, notes — and the LLM reads them, extracts the knowledge, and integrates everything into a persistent, interlinked wiki. You never write the wiki. The LLM does.

Unlike RAG systems that re-derive knowledge from scratch on every query, LLM Wiki Agent compiles knowledge once and keeps it current. Cross-references are pre-built. Contradictions are flagged at ingest time. Every new source makes the wiki richer.

How It Works

You drop a source → LLM reads it → wiki pages are created/updated → graph is rebuilt

You ask a question → LLM reads relevant wiki pages → synthesizes answer with citations

Three layers:

• raw/ — your source documents (immutable, you own this)
• wiki/ — LLM-maintained markdown pages (Claude writes, you read)
• graph/ — auto-generated knowledge graph visualization

Quick Start

git clone https://github.com/SamurAIGPT/GPT-Agent.git
cd GPT-Agent
pip install -r requirements.txt
export ANTHROPIC_API_KEY=your_key_here

Add your first source:

# Drop a source document into raw/
cp my-article.md raw/articles/my-article.md

# Ingest it — LLM reads, extracts, and files knowledge into the wiki
python tools/ingest.py raw/articles/my-article.md

Query the wiki:

python tools/query.py "What are the main themes across all sources?"
python tools/query.py "How does X relate to Y?" --save   # save answer back to wiki

Build the knowledge graph:

python tools/build_graph.py --open   # opens graph.html in browser

Health-check the wiki:

python tools/lint.py --save   # checks for orphans, contradictions, gaps

Architecture

raw/                    ← your sources (never modified by LLM)
wiki/
  index.md              ← catalog of all pages (updated on every ingest)
  log.md                ← append-only operation log
  overview.md           ← living synthesis across all sources
  sources/              ← one page per source document
  entities/             ← people, companies, projects
  concepts/             ← ideas, frameworks, methods
  syntheses/            ← answers to queries, filed back as pages
graph/
  graph.json            ← node/edge data (SHA256-cached)
  graph.html            ← interactive vis.js visualization
tools/
  ingest.py             ← process a new source
  query.py              ← ask a question
  lint.py               ← health-check the wiki
  build_graph.py        ← rebuild the knowledge graph
CLAUDE.md               ← schema and workflow instructions for the LLM

Tools

Command	What it does
python tools/ingest.py <file>	Read a source, update wiki pages, append to log
python tools/query.py "<question>"	Search wiki, synthesize answer with citations
python tools/query.py "<question>" --save	Same, and file the answer back as a wiki page
python tools/lint.py	Check for orphans, broken links, contradictions, gaps
python tools/build_graph.py	Build graph.json + graph.html from wiki
python tools/build_graph.py --no-infer	Build graph without semantic inference (faster)
python tools/build_graph.py --open	Build and open in browser

The Graph

build_graph.py runs two passes:

1. Deterministic — parse all [[wikilinks]] in every page → explicit edges tagged EXTRACTED
2. Semantic — Claude infers implicit relationships not captured by wikilinks → edges tagged INFERRED (with confidence) or AMBIGUOUS

Community detection (Louvain) clusters nodes by topic. The output is a self-contained graph.html — open it in any browser. SHA256 caching means only changed pages are reprocessed.

CLAUDE.md

CLAUDE.md is the schema document — it tells the LLM how to maintain the wiki. It defines page formats, ingest/query/lint workflows, naming conventions, and log format. This is the key configuration file. Edit it to customize behavior for your domain.

What Makes This Different from RAG

RAG	LLM Wiki Agent
Re-derives knowledge every query	Compiles once, keeps current
Raw chunks as retrieval unit	Structured wiki pages
No cross-references	Cross-references pre-built
Contradictions surface at query time (maybe)	Flagged at ingest time
No accumulation	Every source makes the wiki richer

Use Cases

• Research — go deep on a topic over weeks; every paper/article updates the same wiki
• Reading — build a companion wiki as you read a book; by the end you have a rich reference
• Personal knowledge — file journal entries, health notes, goals; build a structured picture over time
• Business — feed in meeting transcripts, Slack threads, docs; LLM does the maintenance no one wants to do

Tips

• Use Obsidian to read/browse the wiki — follow links, check graph view
• Use Obsidian Web Clipper to clip web articles directly to raw/
• The wiki is a git repo — you get version history for free
• File good query answers back with --save — your explorations compound just like ingested sources

License

MIT License — see LICENSE for details.

Related

• graphify — graph-based knowledge extraction skill (inspiration for the graph layer)
• Vannevar Bush's Memex (1945) — the original vision this is related to in spirit