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by Oshayr · MCP Server · ★ 51
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llm-wiki An autonomous knowledge base that grows as you work. LLM Wiki is a Claude Code plugin that captures research, ideas, and decisions into an interlinked wiki with semantic search, automatic research, and a Wikipedia-style web UI. Knowledge compounds over time — the more you use it, the smarter it gets. Inspired by Andrej Karpathy's LLM Wiki pattern: raw sources are immutable, the LLM maintains the wiki layer, and a schema governs behavior. Core Features Knowledge Management Automatic capture — saves research, ideas, decisions, and findings to the wiki as you work Smart retrieval with research-on-miss — checks wiki first, automatically researches and ingests if not found Full-text search — TF-IDF keyword search with content-aware scoring and snippet extraction Block references & transclusion — links and embeds Backlink panel with automatic unlinked mention detection Frontmatter query language — Dataview-like
| Stars | 51 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.5603299830593/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-26 |
| Created | 2026-04-09 |
| Platforms | browser, claude-code, mcp, python |
| Est. Tokens | ~17k |
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LLM-Wiki is Autonomous knowledge base plugin for Claude Code - captures reserch, ideas, and decisions into an interlinked wiki with reserch-on-miss, semantic search, and a Wikipedia-style web UI. Knowledge compou. It is categorized as a MCP Server with 51 GitHub stars.
LLM-Wiki is primarily written in Python. It covers topics such as ai-tools, autonomous-agent, claude-code.
You can find installation instructions and usage details in the LLM-Wiki GitHub repository at github.com/Oshayr/LLM-Wiki. The project has 51 stars and 1 forks, indicating an active community.
LLM-Wiki is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LLM-Wiki on Agent Skills Hub include Kompl, llm-wiki-plugin, mindbase. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.