altRAG: Replace Vector DB RAG with 2KB Pointer Files for AI Coding Agents

What altRAG Does

altRAG addresses the problem of AI coding agents struggling with large 200KB skill files by replacing vector database retrieval-augmented generation (RAG) with a simpler pointer-based approach. The tool creates a lightweight skeleton file that maps document sections to their exact locations, eliminating the need for embeddings, chunking, or databases.

How It Works

altRAG scans your Markdown or YAML skill files and builds a TSV skeleton file (.skt extension) that maps every section to its exact line number and byte offset. This skeleton file is approximately 2KB in size.

When your AI agent needs information, it reads the skeleton file first, finds the specific section it requires, and then reads only those lines from the original document. This approach is particularly effective for structured documentation where you already know where information is located.

Key Features from Source

• Creates 2KB skeleton files instead of using vector databases
• Works with Markdown and YAML skill files
• Generates TSV format skeleton files (.skt extension)
• Maps sections to exact line numbers and byte offsets
• Zero dependencies
• Requires Python 3.10+
• MIT licensed

Installation and Setup

Installation is straightforward:

pip install altrag
altrag setup

Compatibility

The tool works with various AI coding agents including Claude Code, Cursor, Copilot, Windsurf, Cline, and Codex — essentially any system that can read files.

Plan Mode Benefits

Plan mode benefits significantly from this approach. According to the source, it allows agents to construct skill trees while utilizing early, bloat-free context to create "almost surgical" plans.

Use Case

This approach is specifically designed for structured documentation where developers already know where information is located, making vector database RAG overkill. It's particularly useful when AI agents need to reference specific sections of documentation without loading entire files into context.