
llm_wiki
LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base, automatically. Instead of traditional RAG (retrieve-and-answer from scratch every time), the LLM incrementally builds and maintains a persistent wiki from your sources。
The Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Aug 2026
LLM Wiki builds a persistent, interlinked wiki out of your documents instead of re-answering the same questions from scratch. Most retrieval tools reason again on every query; this one reasons once, writes wiki pages with source traceability and cross-references, then maintains them as you add material. Feed it PDFs, Word files, or web clips. GPL-3.0, desktop app, macOS/Windows/Linux.
It needs an LLM provider configured before it does anything: OpenAI, Anthropic, Google, Ollama, or any OpenAI-compatible endpoint. Point it at Ollama and the whole thing runs local and costs nothing. Point it at a hosted API and ingest is where the money goes, because the two-step pass reasons over content before generating pages.
The build has grown well past a document indexer: a Rust backend agent with tool use, multimodal ingest that pulls images out of PDFs, a Deep Research mode running multi-query web searches, a Chrome clipper, and a knowledge graph view with community detection. Solo researchers and anyone sitting on a document pile get real value. Teams get less, since there is no shared instance.
The catch is that GPL-3.0 is a deliberate choice with consequences. Build a product on this and your product inherits the license. For personal use, irrelevant; for anything commercial, it is the first thing to check. The quieter catch: a wiki generated by an LLM is a wiki full of LLM claims, and traceability tells you where a sentence came from, not whether it is correct.
Free vs Self-Hosted vs Paid
fully freeWhat's Free
The application, all of it, under GPL-3.0. Two-step ingest, knowledge graph with community detection, multimodal PDF handling, Deep Research, the Rust agent backend, and the Chrome clipper. No account, no tier, no telemetry gate.
The Required Input
An LLM provider key. This does nothing until one is configured. Supported: OpenAI, Anthropic, Google, Ollama, and any OpenAI-compatible endpoint.
The Real Cost
Ingest tokens. The two-step chain-of-thought pass reasons over content before generating pages, so a document costs meaningfully more to ingest here than to embed in a plain vector store. Deep Research runs multiple web queries per question on top of that. A few hundred PDFs through a hosted API is real money.
The $0 Path
Configure Ollama and run a local model. Ingest quality drops relative to a frontier model, but the bill goes to zero and nothing leaves your machine. For a personal document library this is usually the right call.
The License Consideration
GPL-3.0 is copyleft. Personal and internal use is unaffected. Distributing a product built on this code means distributing your source under the same terms, which rules it out as a foundation for most commercial software.
Free and GPL-3.0 with no paid tier, but it does nothing without an LLM key. Run it against Ollama for a genuinely $0 setup, or budget real ingest costs on a hosted API.
What to do by team size
- Solo
- free; pair it with Ollama and the whole thing costs nothing
- Small team
- free; budget hosted-API ingest costs if you want frontier-model quality
- Medium team
- free, but there is no shared instance, so everyone maintains their own
- Large team
- free; the GPL-3.0 terms rule it out as a base for anything you ship
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A low score is not a verdict on quality. Young and niche tools start low by design. How we calculate scores
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License: GPL-3.0
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Commercial use: ✗ Restricted
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