Luxas Research Colleague
Turns a research question into a cited, illustrated, compiled LaTeX manuscript through a file-backed multi-agent workflow.
What does this agent do, and when should you use it?
Luxas is an open-source multi-agent system for taking a topic in RESEARCH.md through literature work, experiments, and a compiled LaTeX PDF. Its brain coordinates detached Node sub-agent processes for search, reading, experiments, mathematics, illustration, typesetting, and adversarial review. Project state is externalized in files, logs, and notes; a resumed run reconstructs accounting from log.jsonl and recovers background work. The intended artifact is a cited report with self-generated figures and review notes, suited to literature surveys and small computational studies. It runs as a self-hosted CLI workflow in a local project directory, with shell, network, LaTeX, Python, and provider credentials rather than as a hosted chat application.
After luxas init creates a project, brain decomposes the research brief. search discovers papers through OpenAlex, arXiv, CrossRef, citation chains, web search, and an optional anti-detect browser; reader writes per-paper material to notes/literature.d/ for consolidation into notes/literature.md. experiment designs tools, then assigns scripts/<tool>.py to tool_impl and tests/test_<tool>.py to tool_review independently; pytest failures are sent back for revision before results are written to data/experiments/<EXP_ID>/runs/run_N/results.json and notes/experiments.md. math can call Wolfram Engine through wolframscript and falls back to sympy, while illustrator, illustrator_write, and typesetter generate or audit figures and rasterized PDF pages. The finish tool checks completed experiment commitments, background agents, report.pdf, a self-generated figure, typesetter clearance, and PI review status before allowing a clean completion.
- A researcher who needs a cited survey PDF from one well-scoped scientific question.
- A computational research group that wants experiment tooling designed, independently implemented and tested, then incorporated into a manuscript-style report.
- An individual running multi-hour unattended research jobs who needs file-backed logs, notes, and restart recovery.
- A research engineer who wants independent implementation and test authors for experiment scripts rather than one agent validating its own semantics.
- A technical user with local LaTeX, Python, tmux, and API credentials who wants to run research automation in a project directory.
What are this agent's strengths and limitations?
- Externalized project state, reverse-scanned log recovery, detached processes, and orphan recovery support long-running work that can be resumed after a crash.
- The experiment workflow separates tool_impl from tool_review and uses pytest as the validation ground truth.
- It joins literature discovery, experiment execution, figure work, LaTeX compilation, figure review, and page-layout review in one research-oriented delivery path.
- Aligned finish and reviewer gates require completed experiment records, artifact checks, and review conditions instead of treating a model's assertion of completion as sufficient.
- Adoption requires a local shell, filesystem workspace, Node.js, LaTeX, Poppler, Python with plotting packages, tmux, and API credentials; it is not a zero-install hosted service.
- Anthropic is the default provider. DeepSeek and Kimi alternatives have configuration and capability tradeoffs: the documented DeepSeek path is text-only, so vision work needs a separate vision profile.
- The system may autonomously run Python, shell commands, and pip install. The documentation advises against targeting directories containing credentials and against running as root.
- Documented run costs are anecdotal and variable: about $20–80 with the default Claude setup and $2–10 with the dual profile, depending on topic depth and review iterations.
How do you install or deploy this agent?
Install the documented prerequisites first. On macOS:
brew install --cask mactex
brew install poppler tmux [email protected]
pip3 install matplotlib numpy
Then install Luxas:
git clone https://github.com/Muuuun/luxas.git && cd luxas
npm install && npm link
Node.js 22+ and ANTHROPIC_API_KEY are required for the default path. pdflatex, bibtex, Poppler, Python, matplotlib, numpy, and tmux must be available. OPENAI_API_KEY is optional for the o3 math agent; DeepSeek, Kimi, Brave, Gemini, Wolfram Engine, browser-use, and provref are optional according to the features used.
How do you use this agent?
Set the default provider key, initialize a project, and start a run:
export ANTHROPIC_API_KEY="..."
luxas init ~/research/x --prompt "Survey LLM chain-of-thought reasoning"
luxas run ~/research/x --model opus
luxas status ~/research/x
Use "luxas figures ~/research/x" to rerun only the figure/typesetter loop and "luxas list" to list projects. The default uses each agent definition's Claude model; "luxas run ~/research/x --profile dual" selects deepseek-v4-pro for text and k2p5 for vision-required work.
How does this agent compare with similar options?
Luxas is positioned as a research-specific system rather than a general workflow framework. Against LangGraph, CrewAI, and AutoGPT, it emphasizes file-backed state, hook-enforced closure gates, and a compiled LaTeX PDF deliverable; against Sakana AI Scientist, its documentation emphasizes literature discovery and citations; against Claude Code, it uses multiple detached agent roles instead of one chat session. For general-purpose orchestration or interactive coding, the repository points readers toward LangGraph/pi-agent-core or Claude Code respectively.