NanoResearch
Reduces time spent on literature review and technical research by automating information gathering and synthesis, allowing engineers to focus on implementati…
End-to-end autonomous AI research engine that takes a research idea through a 9-stage pipeline to a finished LaTeX paper. It actually runs the computational experiments — generating code, executing it locally or on SLURM GPU clusters, collecting real results, and producing figures — so the paper's numbers come from runs, not LLM invention. Includes a Claude Code mode driven by /project:research slash commands.
- Ask Claude to search and summarize recent research papers on a specific technical topic quickly.
- Generate literature reviews by collecting relevant academic sources and organizing them by theme.
- Automate competitor analysis by researching published technical approaches and methodology comparisons.
Reduces time spent on literature review and technical research by automating information gathering and synthesis, allowing engineers to focus on implementation and architectural decisions.
Engineering teams evaluating emerging technologies or building POCs under tight deadlines.
https://github.com/OpenRaiser/NanoResearch
By OpenRaiser
How to Get It
git clone https://github.com/OpenRaiser/NanoResearch.git && cd NanoResearch && pip install -e ".[dev]"
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Search and summarize recent research papers on a specific technical topic quickly
Trust Signals Auto-scanned
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 1,379 GitHub stars; 7 contributors; last commit 84d ago; license MIT.
Things to check
- Documentation is primarily in Chinese (an English README is provided). Full pipeline runs require an OpenAI-compatible API endpoint and, for the experiment stages, GPU or SLURM cluster access — results depend on real compute, not just an API key.
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Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 1,379 GitHub stars; 7 contributors; last commit 84d ago; license MIT.