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A multi-agent compatible workflow based on Andrej Karpathy's ideas, transforming raw materials into an LLM-maintained Markdown knowledge base that supports knowledge compounding and audit trails.
Section 01
LLM Wiki is a multi-agent compatible workflow based on Andrej Karpathy's ideas. It transforms raw materials into an LLM-maintained Markdown knowledge base, supporting knowledge compounding and audit trails. Complementary to traditional RAG, it is suitable for scenarios like in-depth research and long-term projects. Core principles include Markdown-first and traceable sources.
Section 02
Traditional RAG workflows retrieve document fragments temporarily for each query, with no persistent structure, so models have to re-understand the same content repeatedly. The core idea of LLM Wiki comes from Karpathy's gist document: let LLMs incrementally build and maintain a persistent, interlinked Markdown wiki to achieve knowledge compounding, instead of starting from scratch every time.
Section 03
LLM Wiki defines a clear workflow:
Section 04
Agent-Agnostic Design: Convert the core Markdown workflow to native formats of various agents (e.g., OpenClaw, Claude Code, Codex) via adapters to avoid tool lock-in. The knowledge base remains in Markdown format (human-readable, version-controllable, migratable). Core Principles: Markdown-first (plain text, Git-friendly), traceable sources (pages link to raw materials), knowledge accumulation (temporary outputs converted to persistent knowledge), auditability (track evolution via index logs).
Section 05
Typical usage flow:
raw/ directorywiki/wiki/index.md and append to wiki/log.mdSection 06
LLM Wiki does not replace RAG; instead, it complements it:
Section 07
Applicable Scenarios: Academic research (literature reviews, research question tracking), investment decisions (industry insights, argument formation), product development (competitor analysis, user research), personal learning (cross-domain lifelong learning notes). Project Status: Current version is 0.3.0 draft, providing multi-agent starter packs, shared templates, verification scripts, and demo vaults; the roadmap includes richer OpenClaw integration, structured metadata, automatic reports, CLI tools, release automation, etc.
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