llm-wiki-agent/README.md

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

git clone https://github.com/SamurAIGPT/llm-wiki-agent.git
cd llm-wiki-agent

Open it in your coding agent — no API key or Python setup needed:

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

Each agent reads its config file automatically (CLAUDE.md, AGENTS.md, or GEMINI.md) and follows the same workflows. Then just 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 works too:
"Ingest this paper: raw/papers/my-paper.md"
"What does the wiki say about X?"
"Check for contradictions"
"Build the knowledge graph"

Agent	Config file
Claude Code	CLAUDE.md + .claude/commands/
OpenAI Codex	AGENTS.md
OpenCode / Pear AI	AGENTS.md
Gemini CLI	GEMINI.md

Standalone use (without a coding agent): pip install -r requirements.txt, set ANTHROPIC_API_KEY, then use python tools/ingest.py, python tools/query.py, etc.

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)

Slash command	What it does
/wiki-ingest <file>	Read a source, update wiki pages, append to log
/wiki-query <question>	Search wiki, synthesize answer with citations
/wiki-lint	Check for orphans, broken links, contradictions, gaps
/wiki-graph	Build knowledge graph (graph.json + graph.html)

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)

Command	What it does
python tools/ingest.py <file>	Ingest a source
python tools/query.py "<question>"	Query the wiki
python tools/query.py "<question>" --save	Query and file answer back
python tools/lint.py	Lint the wiki
python tools/build_graph.py	Build graph
python tools/build_graph.py --no-infer	Build graph (skip 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

Going deep on a topic over weeks or months — reading papers, articles, reports.

# Each paper you read gets ingested:
/wiki-ingest raw/papers/attention-is-all-you-need.md
/wiki-ingest raw/papers/llama2.md
/wiki-ingest raw/papers/rag-survey.md

# Wiki builds up entity pages (e.g. "Meta AI", "Google Brain") and
# concept pages (e.g. "Attention Mechanism", "RLHF") automatically.

# Ask synthesis questions across everything you've read:
/wiki-query "What are the main approaches to reducing hallucination?"
/wiki-query "How has context window size evolved across models?"

# Check where your knowledge has gaps:
/wiki-lint
# → "No sources on mixture-of-experts — consider reading the Mixtral paper"

By the end of a research project you have a structured, interlinked reference that reflects everything you've read — not a folder of PDFs you'll never reopen.

---

Reading a Book

File each chapter as you go. Build out pages for characters, themes, plot threads.

# After each chapter:
/wiki-ingest raw/book/chapter-01-the-beginning.md
/wiki-ingest raw/book/chapter-02-the-conflict.md

# Wiki creates pages like:
# entities/ElonMusk.md, entities/Tesla.md
# concepts/FirstPrinciplesThinking.md

# Mid-book:
/wiki-query "How has the protagonist's motivation evolved?"
/wiki-query "What contradictions exist in the author's argument so far?"

# End of book — build the graph:
/wiki-graph
# Open graph.html → see every character/theme/event and how they connect

Think fan wikis like the Tolkien Gateway — thousands of interlinked pages. You can build something like that as you read, with the agent doing all the cross-referencing.

---

Personal Knowledge Base

Track goals, health, psychology, self-improvement — file journal entries, articles, podcast notes.

# File your journal entries:
/wiki-ingest raw/journal/2026-01-week1.md
/wiki-ingest raw/journal/2026-01-week2.md

# File articles and podcast notes that resonated:
/wiki-ingest raw/articles/huberman-sleep-protocol.md
/wiki-ingest raw/articles/atomic-habits-summary.md

# Ask introspective questions:
/wiki-query "What patterns show up in my journal entries about energy levels?"
/wiki-query "What habits have I tried and what was the outcome?"

# The wiki builds a structured picture of you over time —
# entities like "Sleep", "Exercise", "Deep Work" accumulate evidence
# from every source you've filed.

---

Business / Team Intelligence

Feed in meeting transcripts, Slack exports, project docs, customer calls.

# Onboard new context:
/wiki-ingest raw/meetings/q1-planning-transcript.md
/wiki-ingest raw/docs/product-roadmap-2026.md
/wiki-ingest raw/calls/customer-interview-acme.md

# Wiki creates pages for projects, people, decisions, recurring themes.

# Ask strategic questions:
/wiki-query "What feature requests have come up most across customer calls?"
/wiki-query "What decisions were made in Q1 planning and what was the rationale?"

# Lint catches things like:
# → "Project X mentioned in 5 pages but no dedicated page"
# → "Roadmap contradicts customer interview on priority of feature Y"

The wiki stays current because the agent does the maintenance no one on the team wants to do.

---

Competitive Analysis / Due Diligence

Track a company, market, or technology area over time.

# Feed in everything you find:
/wiki-ingest raw/competitors/openai-announcements.md
/wiki-ingest raw/competitors/anthropic-blog-posts.md
/wiki-ingest raw/market/ai-funding-report-q1.md

# Wiki builds entity pages per company, concept pages per technology.

# Ask comparison questions:
/wiki-query "How do OpenAI and Anthropic differ in their approach to safety?"
/wiki-query "Which companies have announced multimodal models in the last 6 months?"

# Save the answer back as a reusable synthesis:
/wiki-query "Competitive landscape summary as of today" --save

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