LLMind Python CLI
LLMind Python CLI is the first implementation of the LLM-Ready File Specification (LRFS). It is a command-line tool (pipx install 'llmind-cli[all]', llmind <command>) that reads and writes the full LRFS payload format, produces HMAC-SHA256 and ed25519 signatures per the signing scheme, and embeds structured metadata layers directly into PDF, JPEG, PNG, and audio (MP3/WAV/M4A) files as XMP metadata — following the conformance-level behaviors defined in /spec/conformance/.
Location
The CLI lives in the llmind-cli/ directory on the feature/llmind-cli branch of the LLMind repository. See pyproject.toml for the package definition (name: llmind-cli, entry point: llmind = llmind.cli:main).
Install
Via PyPI (end users) — pipx is the recommended installer because it sandboxes the CLI in its own virtualenv:
pipx install 'llmind-cli[all]'Available extras let you choose which vision/transcription provider gets bundled: [anthropic], [openai], [gemini], [whisper-local], or [all]. Requires Python 3.11+. PDF rendering also needs poppler-utils on the system (brew install poppler on macOS, apt install poppler-utils on Debian/Ubuntu).
For contributors, clone the LLMind repository and run pip install -e '.[all,dev]' from the llmind-cli/ directory.
What it does
LLMind takes a standard PDF, image, or audio file and enriches it with a structured metadata layer containing extracted text, a natural-language content description, and document/audio structure — all embedded as XMP metadata inside the file’s binary with no external database or sidecar file. Each enrichment layer is cryptographically signed with HMAC-SHA256 (default) or ed25519 (optional) and bound to the file via SHA-256 checksum.
Conformance
- L1 (reader): claimed — parses any LRFS payload, validates signatures, extracts layers.
- L2 (writer): claimed — writes new layers with valid RDF canonicalization and HMAC-SHA256 signatures.
- L3 (full): claimed — implements the full version-chain algorithm (immutable layer history) per /spec/conformance/.
Conformance self-verification pending — the implementation has not yet been formally re-run against the LRFS test vectors and test_vectors_passing is 0 until that check is performed. The conformance_level reflects the implementation’s design intent, not a verified pass count. When verification is run and passes, this entry will be updated to status: reference and test_vectors_passing: 10.