Developers
Give your AI agent, MCP server, or RAG pipeline files it can actually read — without a vector DB.
LLMind — the file enrichment engine
The file is the index.
Make any file LLM-ready.
LLMind is a file enrichment engine that embeds a signed, tamper-evident semantic layer inside the file itself — so any AI tool can read it natively, no vector database required.
Install in 10 seconds
pipx install 'llmind-cli[all]'
llmind enrich myfile.pdfWho is this for?
Teams
Portable, signed semantic metadata across your DAM, your publishing pipeline, and any AI tool downstream.
Individuals
Enrich your files once. ChatGPT, Claude, NotebookLM, Cursor all read the same layer — no re-upload, no re-parse.
What LLMind is NOT
- Not a parser or IDP tool — not a replacement for LlamaParse, Docling, Reducto, Textract, Unstructured.io, Mistral OCR, or Azure/Google Document AI.
- Not a RAG framework — not a LlamaIndex/LangChain alternative.
- Not a vector database — not a Pinecone/Weaviate/Qdrant/Chroma alternative.
- Not enterprise search — not a Glean/Dropbox Dash/Notion AI alternative.
LLMind is a portable, signed semantic layer that sits inside a file's XMP metadata, so any AI tool — from a Claude agent to an MCP server to a custom RAG pipeline — reads the file like it's a Markdown document, without re-parsing or re-OCR'ing each time.
Open spec
The LLM-Ready File Specification (LRFS) defines
the XMP namespace, signing scheme, and layer format. Open, versioned,
stable at
https://llmind.org/ns/1.0/.
Explore
What's new
- Changelog — sprint ships, spec merges, and infrastructure updates.
- Benchmarks — file-size overhead, signing throughput, read-time vs. re-parse.
- Blog — announcements and technical writing on file enrichment.
- LRFS v1.0 — the LLM-Ready File Specification, payload format, and signing scheme.