LLM Wiki Newsroom

A multi-agent AI knowledge base run by a five-role "newsroom" — open-source, local-first, no API keys, no vendor lock-in. Drop articles, documents, and PDFs into the raw/ folder, type a single command, and the newsroom — powered by an agent like Claude Code — reads them, extracts entities, concepts, and relationships, and organizes everything into a fully cross-referenced wiki, a structured and persistent alternative to RAG. Unlike most takes on the idea, the agent that writes a page is never the one that reviews it, and the authoring guidelines evolve themselves over time. Every new document you add also enriches the existing pages. This repo ships with a small example corpus — the debate over what "open source" means for AI — under wiki/, but the framework is domain-agnostic.

Most knowledge tools leave the finding to you. This project makes the AI read and understand your collected documents first, then organizes them into a wiki — with cross-references between pages, automatic flagging of conflicting claims, and per-topic synthesis built in from the start, so later retrieval is fast.

See the output before installing — the example corpus shipped in this repo is published as a browsable GitHub Wiki (no clone needed). It's a rendered static snapshot of the wiki/ folder; the interactive graph below runs locally.

The interactive knowledge graph (graph/graph.html) — every page a node, every wikilink an edge, color-coded by auto-detected cluster, with a live physics layout and filter/search built in. Shown here on a larger private deployment (~2,300 nodes) to convey how it scales; this repo ships a deliberately small 15-node example corpus you can browse the exact same way. (Interface shown in the optional Korean WIKI_LANG=ko mode.)

What makes this different

There are plenty of takes on Karpathy's LLM Wiki idea now. After reading the popular implementations, two things here are genuinely rare:

1. Authoring guidelines that evolve themselves — something I haven't found in the other implementations. When the same review failure keeps recurring, the system drafts a fix to its own writing rules and keeps it only if a blind A/B against a regression set shows it actually helped — an idea borrowed from Self-Harness and Microsoft SkillOpt. So it isn't only the wiki that improves over time, but the rules that build it. (This loop is still experimental — I'm measuring whether it earns its keep rather than claiming it's solved.)

2. A full newsroom, not just "an agent." Plenty of tools wrap one agent around your notes, and a few add a verifier. Here the work is split across five roles — a reporter drafts source pages and entity/concept stubs, a columnist writes the deep cross-source analysis, a desk editor reviews it with fresh eyes, a copy editor runs the deterministic checks, and an editor-in-chief gates publication — and the agent that writes a page is never the one that reviews it. That review is held to an editorial rubric drawn from real craft (journalism, consulting, encyclopedic forms), so a different person or session reproduces the same bar, and a two-sided gate means the deterministic lint and the qualitative review must both pass.

It also leans on Memex-style associative discovery — saved reading trails and "unexpected connection" surfacing — that the other implementations don't carry.

The rest — the knowledge graph, contradiction tracking, cascading updates, plain-markdown/Obsidian output — many LLM-wiki tools have in some form. The self-evolving guidelines, the five-role newsroom with its editorial rubric, and the Memex discovery are the bet.

Highlights

• Persistent, plain-markdown knowledge base — your "second brain" as version-controlled .md files, not a vendor silo. Doubles as an Obsidian vault for personal knowledge management (PKM).
• Cascading updates — ingesting one document refreshes ~10–15 related existing pages automatically.
• Contradiction tracking — conflicting claims between sources are flagged at ingest time, not at query time.
• Interactive knowledge graph — every page a node, every link an edge, auto-clustered and browsable.
• Associative discovery (Memex) — follow connected concepts to surface unexpected relationships.
• Local-first, no API keys — the Python tools (graph, lint, search) run entirely on your machine.

See Key Features below for how each one works.

Installation

git clone https://github.com/alfadur7/llm-wiki-newsroom.git
cd llm-wiki-newsroom

Or click "Use this template" to create your own wiki repo from this scaffold. To start from a clean slate, delete the example pages under wiki/ (keep the folders and graph/cluster_labels.json), then ingest your own sources with /wiki-ingest.

This project assumes an environment where the AI agent reads and edits files and invokes tools on its own. Support levels by agent:

Claude Code-only features include cascading updates that refresh related existing pages whenever a new document is added, a backlink index across all pages, automatic contradiction tracking, and associative discovery that follows connected concepts. AGENTS.md and GEMINI.md carry only the basic workflow from the original SamurAIGPT project. The Python tools used to build the wiki run locally with no external API keys.

Slash Commands (Claude Code)

In Claude Code, typing a /-prefixed command like /wiki-ingest runs the matching workflow. The table below gives each command's role in one line; the Key Features section explains how they work. Argument notation follows <required>, [optional], and a | b | c (pick one); invoking a command with no required argument prints usage and stops.

Core workflow

News & sharing

Associative discovery

"Associative discovery" is inspired by the Memex proposed by Vannevar Bush in 1945 — an exploration style that follows connected concepts to surface unexpected relationships without a fixed search term.

You don't have to memorize the slash commands — you can request the same tasks in natural language, and Claude Code figures out the intent and maps it to the right command.

"Ingest this file: raw/NewsScrap/article.md"
"What's the relationship between open weights and the OSI definition?"
"Check the wiki for contradictions and auto-fix them"
"Start from Meta and find unexpected connections"

Architecture

Three-layer structure

This follows the LLM Wiki pattern proposed by Andrej Karpathy. The source documents (Layer 1), the agent-processed wiki (Layer 2), and the agent's operating rules (Layer 3) are kept separate so that humans and AI don't trespass into each other's territory.

The Layer 2 wiki is further formalized into four sub-layers. Knowing which slash command produces or updates each sub-layer makes the workflow intuitive to follow.