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. Claude 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 → Claude reads it → wiki pages are created/updated → graph is rebuilt

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

Three layers:

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

Quick Start — Any Coding Agent (no API key needed)

Works with Claude Code, Codex, OpenCode, Gemini CLI, and any agent that reads a config file from the repo root.

git clone https://github.com/SamurAIGPT/GPT-Agent.git
cd GPT-Agent

claude          # Claude Code
codex           # OpenAI Codex
opencode        # OpenCode
gemini          # Gemini CLI

Each agent reads its config file and follows the same workflows. Then talk to it:

# Claude Code slash commands:
/wiki-ingest raw/articles/my-article.md
/wiki-query what are the main themes across all sources?
/wiki-lint
/wiki-graph

# Any agent (plain English):
"Ingest this paper: raw/papers/my-paper.md"
"What does the wiki say about X?"
"Check for contradictions"
"Build the knowledge graph"

Quick Start — Standalone Python (requires API key)

pip install -r requirements.txt
export ANTHROPIC_API_KEY=your_key_here

python tools/ingest.py raw/articles/my-article.md
python tools/query.py "What are the main themes?"
python tools/query.py "How does X relate to Y?" --save
python tools/build_graph.py --open
python tools/lint.py --save

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

Commands

Claude Code (primary — no API key)

Or describe what you want in plain English — Claude Code follows CLAUDE.md and does the right thing.

Standalone Python (optional — requires ANTHROPIC_API_KEY)

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

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