NanoResearch is OpenRaiser's MIT-licensed nine-stage engine that takes a research idea to a complete LaTeX paper by running the experiments itself: the pipeline submits generated code to local or SLURM GPU clusters, parses the training logs that come back, and writes every table and figure from those outputs. **It is the idea-to-paper column that binds the manuscript to actual runs rather than sandbox simulations, and its resumable pipeline plus per-stage model routing make it the practical choice for anyone whose experiments need a GPU cluster, with the usual discount that the evidence is self-published.** ## What it is A Python CLI (github.com/OpenRaiser/NanoResearch, MIT, Python 3.10+, v1.0 released March 2026) with a bilingual Chinese-and-English README. The nine stages run from literature search (OpenAlex and Semantic Scholar) through experiment blueprints, code generation, GPU job submission, log parsing, figure generation, and LaTeX writing, with checkpoint-and-resume from any failed stage and a different model configurable per stage. Execution targets are a local GPU or a SLURM cluster with auto-submission; a Claude Code mode and a Feishu bot add chat and messaging surfaces to the CLI, and an Evo self-evolving pipeline is listed as a feature section in both READMEs. The README's core claim: every number, table, and chart in the output paper comes from the pipeline's own experiment outputs, not LLM fabrication. ## Status Active: 1,340 stars, 92 forks, created 2026-03-17, last pushed 2026-10-09, as of 2026-10-11. The release history is thin (a single assets release in March 2026) despite the README's v1.0 milestone, and a search for the name returns no Hacker News threads as of 2026-10-11. Star History Chart ## Strengths - The evidence model is the differentiator: code goes to a GPU cluster, logs come back, and the paper binds to those logs, attacking the fabricated-results failure the citation checkers cannot see. - Checkpoint-and-resume per stage plus per-stage model routing is engineering for the long, flaky runs actual experiments produce. - Venue templates (NeurIPS, ICML, arXiv) and the claimed cost floor (as low as $0.5 of API credit per paper on DeepSeek) make it the cheapest member here per paper. - Three surfaces (CLI, Claude Code mode, Feishu bot) suit both individual researchers and lab groups. ## Cautions - The core claim is the README's: this run found no independent evaluation, no benchmark integration, and no third-party account of a NanoResearch paper's data integrity. - A single March 2026 release asset against a v1.0 milestone suggests versioning lives in the README rather than in tagged releases. - SLURM auto-submission runs generated code on shared infrastructure, a surface the README does not discuss sandboxing. - The project publishes no paper of its own, so its design claims rest on documentation alone. ## Pricing Free and open source under MIT; there is no paid tier, so pricing does not apply. Costs are your own GPU time (local or cluster) and LLM API calls; the README claims as low as $0.5 per paper with DeepSeek. ## Compared to - [AutoResearchClaw](../autoresearchclaw/index.md): the 23-stage rival with citation verification and human-oversight modes; NanoResearch is the GPU-cluster executor with no published citation-check layer. - [Sakana AI Scientist](../sakana-ai-scientist/index.md): the ancestor; Sakana's experiments ran in managed sandboxes, NanoResearch's go to schedulers you administer. - [Agent Laboratory](../agent-laboratory/index.md): the human-assist ancestor with published quality scores; NanoResearch is fully autonomous with published costs instead. ## Bottom line **Recommended for researchers with GPU cluster access who want idea-to-paper runs where the paper's numbers come from jobs the pipeline itself submitted.** Not for anyone needing verified data integrity: the binding-to-logs claim is self-published, and the citation layer other pipelines ship is absent here. ## Changes - 2026-10-11 - Created. ## See also - [AutoResearchClaw](../autoresearchclaw/index.md) - the stage-count rival with the citation-verification layer NanoResearch lacks - [Sakana AI Scientist](../sakana-ai-scientist/index.md) - the sub-family ancestor - [Agent Laboratory](../agent-laboratory/index.md) - the measurement-focused ancestor - [Automated Research Feature Matrix](../automated-research-feature-matrix/index.md) - the category comparison this note joins ## References - https://github.com/OpenRaiser/NanoResearch - repository, MIT license, 1,340 stars, last push 2026-10-09 (as of 2026-10-11) - https://raw.githubusercontent.com/OpenRaiser/NanoResearch/main/README.md - the Chinese main README: the actually-runs-experiments claim, nine stages, SLURM, Evo pipeline, Claude Code mode, Feishu bot (fetched 200, 2026-10-11) - https://raw.githubusercontent.com/OpenRaiser/NanoResearch/main/README_en.md - the English README: v1.0 milestones, OpenAlex and Semantic Scholar search, checkpoint-and-resume, per-stage routing, the $0.5-per-paper claim (fetched 200, 2026-10-11) - https://api.github.com/repos/OpenRaiser/NanoResearch/releases?per_page=5 - the single assets release of 2026-03-19 (fetched 200, 2026-10-11) - https://hn.algolia.com/api/v1/search?query=NanoResearch - the search returning no threads (fetched 200, 2026-10-11)