AI/ML●●Solid
Open KB: Open LLM Knowledge Base
Compiled wiki beats query-time RAG with vectorless PageIndex retrieval for long PDFs.
Big BrainNiche Gem
mingtianzhang
621mo ago
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GitHub Repository
Memoriki - LLM Wiki + MemPalace. Personal knowledge base with real memory.
106 stars
Memoriki – LLM Wiki+MemPalace for persistent personal knowledge bases
by Aianback·Apr 9, 2026·5 points·0 comments
AI Analysis
●●SolidBig BrainNiche Gem
LLM compiles knowledge into wiki pages instead of RAG retrieving raw chunks.
Strengths
- •Three-layer architecture separates wiki structure from semantic search and entity graphs.
- •Temporal knowledge graph tracks how facts change over time with date validity.
- •Works with any MCP-compatible agent like Claude Code and Cursor.
Weaknesses
- •Described as a template requiring users to assemble MemPalace and configure everything.
- •Personal knowledge base market is crowded with Obsidian, Logseq, and Notion AI.
Category
Target Audience
Researchers, founders, and students building second brains
Similar To
Obsidian · Logseq · Mem.ai
Post Description
Memoriki is a template for building personal knowledge bases where the LLM does all the maintenance work.
It combines Karpathy's LLM Wiki pattern (structured markdown wiki maintained by an LLM) with MemPalace (an MCP server that adds semantic search and a temporal knowledge graph).
Three layers: - Wiki pages with [[wiki-links]] and YAML frontmatter - the LLM creates and maintains these - Semantic search via embeddings (ChromaDB) - find things by meaning, not keywords - Knowledge graph with typed relationships and date validity - "what changed since last month?"
It's not RAG. RAG re-derives answers from raw chunks every time. Here the LLM compiles knowledge into wiki pages once, keeps them current as new sources arrive, and the graph tracks how everything connects.
Works with any MCP-compatible agent (Claude Code, OpenAI Codex, Cursor, Gemini CLI).
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