Aura Research: Local tool compiles documents into AI-navigable wiki with persistent memory

Aura Research is an open-source tool that compiles raw documents into an AI-navigable wiki with persistent memory. The tool runs 100% locally with no data leaving your machine.

How it works

The workflow consists of four main commands:

pip install aura-research
research init my-project
# copy docs into raw/
research ingest raw/
research compile
research query "your question"

You drop a folder of raw documents (PDFs, papers, notes, code, supporting 60+ formats) and the LLM compiles them into a structured markdown wiki with backlinked articles, concept pages, and a master index. It then compresses everything into a .aura archive optimized for RAG retrieval, which the developer claims is approximately 97% smaller than raw source data.

Key design decisions

• No embeddings, no vector databases. Uses SimHash + Bloom Filters instead with zero RAM overhead
• Built-in 3-tier Memory OS (facts / episodic / scratch pad) so the LLM doesn't forget important context across sessions
• The wiki is just .md files, browsable in Obsidian, VS Code, or any markdown editor
• Works with any LLM provider (OpenAI, Anthropic, Gemini) or as an agent-native tool inside Claude Code/Gemini CLI where no API key is needed
• Everything runs locally with no data leaving your machine

The "no embeddings" approach

The developer deliberately avoided the standard RAG pipeline (chunk → embed → vector search). Instead, the LLM compiles knowledge into a well-structured wiki with an index. When you query, it reads the index, finds the 2-3 relevant articles, and only loads those. The approach assumes that if knowledge is properly organized, the LLM is smart enough to navigate a good file structure without needing a separate embedding model.

The tool is available on GitHub at https://github.com/Rtalabs-ai/aura-research and can be installed via PyPI with pip install aura-research.