NanoResearch is an autonomous AI research system that turns research ideas into executable experiments and LaTeX papers supported by results from real training runs. It is for researchers validating prototypes, running GPU experiments, generating benchmarks, analyzing logs, and preparing paper drafts. The catalogue add-ons support its research pipeline and agent workflows.
Borrowing it
Nothing to install: this file belongs to OpenRaiser/NanoResearch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/OpenRaiser/NanoResearch/main/AGENTS.mdgit clone --depth 1 https://github.com/OpenRaiser/NanoResearchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/openraiser/nanoresearch/agents-md)<a href="https://agentmods.dev/instructions/openraiser/nanoresearch/agents-md"><img src="https://agentmods.dev/badge/instructions/openraiser/nanoresearch/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/openraiser/nanoresearch/agents-md"><img src="https://agentmods.dev/badge/instructions/openraiser/nanoresearch/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00809 | $0.00809 |
| Opus 5.5 | $0.00324 | $0.00324 |
| Sonnet 5.5 | $0.00162 | $0.00162 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
NanoResearch AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NanoResearch — Codex Integration Mode
NanoResearch is an end-to-end autonomous AI research engine. In Codex integration mode, Codex should drive the existing pipeline rather than inventing a second workflow.
Core Goal
Given a research topic, NanoResearch should produce a resumable research workspace containing:
- literature artifacts
- planning artifacts
- runnable experiment code when needed
- execution or literature-analysis evidence
- figures
- a LaTeX paper draft
- review output and final exported assets
Pipeline
NanoResearch uses a 9-stage pipeline:
IDEATION -> PLANNING -> SETUP -> CODING -> EXECUTION -> ANALYSIS -> FIGURE_GEN -> WRITING -> REVIEW
Stage meanings:
ideation: literature search, gap finding, hypothesis or theme extractionplanning: experiment blueprint or survey blueprint generationsetup: environment and resource preparationcoding: runnable experiment generationexecution: local or SLURM-backed experiment executionanalysis: structured evidence extraction from outputsfigure_gen: figure generation for paper assetswriting: LaTeX paper draftingreview: critique, verification, and revision
Workspace Convention
Workspaces live under ~/.nanoresearch/workspace/research/.
A typical workspace contains:
{session_dir}/
├── manifest.json
├── papers/
├── plans/
├── experiment/
├── drafts/
├── figures/
├── output/
└── logs/
Reuse an existing workspace when the user asks to continue, inspect status, resume, or revise a prior run.
Paper Modes
Topic prefixes:
original: Topic->original_researchsurvey:short: Topic->survey_shortsurvey:standard: Topic->survey_standardsurvey:long: Topic->survey_long
Behavior:
- original research follows the full 9-stage pipeline
- survey modes skip experiment-heavy stages and use literature-grounded planning, writing, and review
- the prefix is parsed by the existing CLI and manifest logic; Codex should reuse that behavior
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 96 lines · 809 tokens per session scan A 977f1ee8565c
NanoResearch AGENTS.md is an instructions file published in the GitHub repository OpenRaiser/NanoResearch (1,332 stars, last pushed 28d ago), licensed MIT. It adds 809 tokens to every session, about $0.0032 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
NanoResearch AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| NanoResearch AGENTS.mdOpenRaiser/NanoResearch | Instructions file | 1.3k | 28d ago | MIT | A | 809 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |