Read this in 한국어.
LLM Wiki Newsroom is an open-source framework that turns a folder of documents into a cross-linked, human-readable markdown wiki, maintained by an AI agent organized as a five-role newsroom. Drop articles, notes, and PDFs into a folder, run one command, and the agent — powered by Claude Code — reads them, extracts entities, concepts, and relationships, and organizes everything into interlinked pages. It’s a persistent, structured 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.
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See the output before installing
The example corpus shipped in the repo — the debate over what “open source” means for AI — is published as a browsable GitHub Wiki, so you can read the generated pages without cloning. The interactive knowledge graph below runs locally after you clone.
The interactive 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 on a larger private deployment (~2,300 nodes) to convey how it scales; this repo ships a deliberately small 15-node example corpus you browse the exact same way.
What makes it different
It keeps the three-layer shape of Karpathy’s LLM Wiki — raw/ for untouched sources, wiki/ for the pages the agent maintains, and a schema layer holding the operating rules — plus the same three operations: ingest, query, and lint. There are plenty of takes on that idea now; after reading the popular implementations, three things here are genuinely rare:
- Authoring guidelines that evolve themselves. 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 rotating regression set shows it actually helped. So it isn’t only the wiki that improves over time, but the rules that build it. (Still experimental — the author is measuring whether it earns its keep rather than claiming it’s solved.)
- A full newsroom, not just “an agent.” The work is split across five roles — a reporter drafts source pages, a columnist writes the deep cross-source analysis, a desk editor re-reads it for bias, argument quality, and narrative flow, a copy editor runs deterministic checks, and an editor-in-chief routes work and gates publication. Not every seat is a model passing judgment: the desk holds the only independent LLM verdict, the copy editor is a Python script, and the rest is writing work or orchestration. The reviewer sees only the draft and the rubric, never the writer’s reasoning — the real lever is context isolation, not instance count.
- Memex-style associative discovery. Saved reading trails and “unexpected connection” surfacing, inspired by Vannevar Bush’s Memex (1945), that the other implementations don’t carry.
Nothing reaches the wiki until it clears both gates:
| Gate | Who | What it checks | How |
|---|---|---|---|
| 1 | copy editor | links, citations, structure | deterministic — tools/lint.py, a Python script, not a model |
| 2 | desk | bias, argument quality, narrative flow | qualitative — a fresh-context review against an editorial rubric |
Machine-checkable things are checked by machine; only what needs judgment costs a model call.
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 rubric, and the Memex discovery are the bet.
How it compares to RAG
| RAG | LLM Wiki Newsroom | |
|---|---|---|
| Knowledge state | re-extracted per query | organized once, continuously updated |
| Retrieval unit | source chunk | structured wiki page |
| Cross-reference | none | wikilinks + backlink index |
| Contradiction handling | may surface at query time | flagged at ingest time + tracked |
| Accumulation effect | none | new sources enrich existing pages |
| Exploration | keyword search | graph traversal + associative trails |
To be precise, this doesn’t do away with retrieval. Karpathy framed the wiki as a compile step for knowledge, not as a replacement for search, and the optional local search used here is itself a BM25 + vector hybrid. What changes is what gets retrieved: a few already-structured, cross-referenced pages instead of raw chunks reassembled from scratch on every query.
Highlights
- Persistent, plain-markdown knowledge base — your “second brain” as version-controlled
.mdfiles, not a vendor silo. Doubles as an Obsidian vault. - Cascading updates — ingesting one document refreshes ~10–15 related existing pages automatically.
- Contradiction tracking — conflicting claims between sources are flagged at ingest time, not 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 — the Python tools (graph, lint, search) run entirely on your machine with no API keys; the agent itself runs on Claude Code.
When the split earns its cost
A separate reviewer costs more tokens and more wall-clock than letting one agent grade its own draft. That trade isn’t always worth it:
- One agent is enough when the output is quick and disposable, and a mistake costs nothing to throw away — a scratch summary, a one-off answer. Self-critique in the same context will do.
- Separate the writer from the reviewer when the output is published, accumulates, and gets built on later — where a plausible-but-wrong page quietly hardens into the thing everything else cites.
A wiki is the second case by construction: today’s page is tomorrow’s input, so an error doesn’t stay one error. That’s the bet here, and why this spends the extra tokens.
Where it stands
- New. The repository went public on 2026-06-26. Treat it as the idea plus a small reproducible example, not a battle-tested product.
- The shipped corpus is deliberately small — 15 pages on the open-source-AI debate. The graph screenshot above comes from a larger private instance (~2,300 nodes), shown for scale; that one you can’t verify from the repo.
- The differentiators are hypotheses. Writer–reviewer separation and the self-evolving loop are argued from design, not from published A/B numbers. They’re being measured, not claimed as settled.
- “No API keys” covers the tooling, not the agent. Build, lint, and search are local Python; the reading and writing happen through your own Claude Code access.
- Korean mode localizes prose, not schema.
WIKI_LANG=kotranslates body text and field values; the frontmatter keys and section headers the tools parse stay English, and the shipped example corpus is English throughout.
Quick start
git clone https://github.com/alfadur7/llm-wiki-newsroom.git
cd llm-wiki-newsroom
Or click “Use this template” to scaffold your own wiki repo. Then ingest your own sources with /wiki-ingest. Full setup, all nine slash commands, and the architecture are in the README.
Learn more
- Full README — install, commands, architecture, feature reference
- FAQ — common questions answered
- The Knowledge Factory — the four-loop production system behind the newsroom, from the concept down to the implementation
- Browsable example wiki — the shipped corpus, no clone needed
- Karpathy’s original LLM Wiki gist — the design inspiration
MIT-licensed. A structured, local-first take on the LLM Wiki pattern — built and maintained in the open.