# OpenRaiser/NanoResearch

🦞+🔬 NanoResearch: The Autonomous AI Research Assistant

Repository: https://github.com/OpenRaiser/NanoResearch
Canonical: https://ross.abutalabs.com/products/nanoresearch
Language: Python
License: MIT
License Family: permissive
Topics: agent-skills, agents, ai, ai-agents, ai-scientist, artificial-intelligence, autonomous-agents, autonomous-research, autoresearch, claude-code, claude-skills, nanobot, openclaw
Last push: 2026-08-25T09:28:09+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 43, longevity 12
- inputs: {"age_days": 169, "days_push": 8, "days_rel": 167, "gap_med": null, "n_releases_24m": 1}
- flags: young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1358, forks 96 (observed 2026-08-28T04:04:29.580703+00:00)

## What it is
NanoResearch is an autonomous AI research assistant that runs an end-to-end pipeline from a research idea to a complete LaTeX paper. It executes real computational experiments on local or SLURM GPU clusters, collects actual results, generates figures, and produces papers grounded in real data rather than LLM-fabricated numbers.

## Use cases
- automatically write a research paper from an idea
- run ML experiments on a GPU cluster autonomously
- generate LaTeX papers with real experimental results
- automate the AI research pipeline end to end
- use Claude Code skills for autonomous research
- self-evolving research agent that improves its own experiments

## When to choose
- you want fully automated idea-to-paper research workflows with real executed experiments
- you have GPU/SLURM cluster access and want experiments submitted and results collected automatically
- you prefer results grounded in actual runs instead of LLM-hallucinated numbers
- you use Claude Code or agent-skill ecosystems and want research automation integrated

## When to avoid
- you only need text generation or paper writing without running experiments
- you lack GPU or cluster resources, since the pipeline depends on executing training jobs
- you need a polished GUI product rather than a Python CLI workflow
- your research domain is not amenable to computational experiments

## Facets
- artifact type: cli-tool
- maturity: active
- function: agent-framework, llm-inference, llm-training, workflow-automation, cli, data-visualization, developer-tools
- domain: artificial-intelligence, machine-learning, large-language-models
- platform: python, cli
- tags: autonomous-research, ai-scientist, claude-code, agent-skills, latex-paper-generation, slurm, experiment-automation, nanobot, ai-agents, automation, research, linux, docker, gpu

## Member repositories
- OpenRaiser/NanoResearch (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:29.580703+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:41:44.884134+00:00, confidence not recorded.
  - readme: https://github.com/OpenRaiser/NanoResearch (fetched 2026-08-28T04:04:29.580703+00:00, sha 7b9e4894c5dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.