Turn scattered notes, docs and transcripts into a queryable Markdown wiki — an LLM knowledge-base compiler with MCP access, no embeddings, self-hosted.
Turn scattered notes, documents, and transcripts into a searchable, queryable personal Wiki — powered by LLM-driven knowledge compilation.
KaaS started as an internal tool. Our knowledge lived scattered across documents, meetings, and email — and every time someone changed roles or left, the context they'd built up walked out with them. New people spent weeks piecing it back together.
A distillation pipeline fixed that. It compiles each person's scattered material into a wiki tied to their role rather than their identity — so when someone moves on, the raw data goes but the distilled judgment stays for whoever fills the seat next.
The payoff is the same either way: the organization stops re-answering the same questions. That's what convinced us it was worth open-sourcing.
Unlike typical RAG systems that chunk and embed raw text, KaaS compiles your content through a 4-phase LLM pipeline:
The result is human-readable Markdown articles — not a black-box vector store. You can read, edit, and git-manage your knowledge base.
Already living in a coding agent (Claude Code, Codex, openclaw, …)? Skip Docker. Copy this and paste it to your agent — it will install kb-ai, ask what to distill, build the wiki, and wire up MCP so you can query it in any later session:
Prefer a web UI, or want the full backend? Use the Docker path below.
KaaS calls LLMs through any OpenAI-compatible API (OpenAI, DeepSeek, Ollama, vLLM, Azure OpenAI, etc.). Pick either method below:
From the project README.
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