LLMs TXT

About

Designed to overcome the limitations of large `llms.txt` documentation files that often lead to token limits and timeouts when used with large language models. This tool acts as a lean server, efficiently parsing `llms.txt` content (supporting both YAML frontmatter and Markdown), embedding sections for semantic search, and retrieving only the most relevant parts. It prioritizes surgical access, ensuring responses are concise and focused, delivering only the necessary context to avoid overwhelming LLMs.

Key Features

• Intelligent source resolution, prioritizing `llms-full.txt` when available
• Instant startup with lazy loading and background indexing
• Byte-capped responses to protect LLM context windows
• 0 GitHub stars
• Search-first approach; no full-document dumps
• Human-readable IDs for direct section access (e.g., `https://ai-sdk.dev/llms.txt#rag-agent`)

Use Cases

• Searching specific documentation for relevant sections while adhering to byte limits (e.g., "Search Next.js docs for middleware routing. Give only the most relevant sections and keep it under 60 KB.")
• Refreshing indexed documentation sources to get the latest versions before performing new searches (e.g., "Refresh the Drizzle docs so I get the latest version, then search for migrations.")
• Retrieving exact section content from large documentation files using natural language queries (e.g., "From Drizzle ORM docs, show how to define relations. Retrieve the exact section content.")