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Requirements

  • macOS or Linux (ARM Linux, including Raspberry Pi 4/5, works for cloud-Ollama configurations)
  • Python 3.11+
  • Ollama installed locally for embeddings (ollama pull mxbai-embed-large)
  • A Zotero library (Better BibTeX optional)
  • An Obsidian vault (full absolute path required)
pip install lit-monitor        # or: uvx lit-monitor / pipx install lit-monitor
lit-monitor first-run

lit-monitor first-run walks you through interactive setup and then launches the web UI. The base install is cloud-free and runs entirely on local Ollama. Ollama itself is a separate prerequisite — see Requirements.

From source (development)

git clone https://github.com/max3925vats/lit-monitor.git
cd lit-monitor
./install.sh

The script installs uv if needed, creates a project-local .venv, resolves all dependencies, and seeds working configs from config/*.example.yaml.

Not in biopharma?

lit-monitor ranks against your library, whatever the field — and ships synthetic starter configs for ml-research, climate-science, and bioprocessing. See Starting from a non-biopharma field.

Manual install

To drive uv yourself instead of running install.sh:

uv venv && source .venv/bin/activate
uv sync                                  # web UI, graph, MCP, notifications all included
for f in config/*.example.yaml; do cp -n "$f" "${f%.example.yaml}.yaml"; done

Optional extras

Extra Adds Without it
--extra nlp BiobertNER for entity extraction (~3 GB; transformers + torch) The LLM fallback handles entities
--extra litellm Multi-provider LLM routing (Anthropic, OpenAI, Vertex AI, etc.) Ollama only
--extra dev Contributor tooling (ruff, pytest, mypy) —

Install one or more with pip (square-bracket extras):

pip install "lit-monitor[nlp]"           # BioBERT entity extraction
pip install "lit-monitor[litellm]"       # multi-provider cloud LLM routing
pip install "lit-monitor[nlp,litellm]"   # both at once

Or, from a source checkout driving uv yourself:

uv sync --extra nlp --extra litellm

Next steps