NanoResearch

End-to-End Autonomous AI Research Engine — From Idea to Full Paper, Fully Automated

Quick Start · Showcase · Pipeline · Claude Code · Feishu Bot

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🔬 NanoResearch actually runs computational experiments — it doesn't just generate code, it submits jobs to GPU clusters, collects real results, generates figures, and outputs a complete LaTeX paper backed by real experimental data. Every number, table, and chart in the paper comes from actual experiment outputs, not LLM fabrication.

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📖 Table of Contents

• 📊 Real-World Output Showcase
• ⚡ CLI Demo
• 📢 News
• ✨ Key Features
• 🆚 Why NanoResearch
• 🎯 Use Cases
• 🖼️ Showcase
• CLI: Plain vs TUI
• 🔬 Pipeline
• 📦 Quick Start
• 🧬 Evo Self-Evolving Pipeline
• 🧩 Claude Code Mode
• ⚙️ Configuration
• 💻 CLI Reference
• 🍪 Examples & Demos
• 📂 Output Structure
• 💬 Feishu Bot
• 🏗️ Project Structure
• ❓ FAQ
• 🎯 Roadmap
• 📋 Requirements
• 🙏 Acknowledgements
• 🤝 Contributing
• 📝 Citation
• 📄 License

🔝 Back to top

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📊 Real-World Output Showcase

Figures below are generated by NanoResearch from real experiment runs. All curves, tables, and visualizations are derived from actual training logs and execution outputs — not LLM fabrication.

Experimental Results

Main Results & Comparison

Ablation & Visualization

Every figure is produced by the pipeline from genuine experiment artifacts; all data traces back to executed runs.

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⚡ CLI Demo

The command-line interface (CLI) offers both a full-screen TUI and classic streaming logs. Below is a TUI theme and interface demo (color themes and layout). For other entry points (e.g. Claude Code, Feishu bot), see the corresponding sections in this README.

🖥️ CLI / TUI
Download / play CLI demo video

_{TUI layout, color theme switching, and improved on-screen structure}

🔝 Back to top

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📢 News

• [2026-03] 📄 NanoResearch documentation and showcase overhaul — local image references and real-world output display.
• [2026-03] 🚀 NanoResearch v1.0 officially released — the first end-to-end autonomous AI research engine, covering literature search through LaTeX paper output.
• [2026-03] 🔬 Core capabilities: real GPU/SLURM execution, evidence-driven writing, 9-stage resumable pipeline, per-stage multi-model routing.

📜 Release History

Date	Milestone
2026-03	Feishu bot, Claude Code integration, NeurIPS/ICML/arXiv templates
2026-03	SLURM cluster auto-submission, checkpoint & resume, multi-model routing
2026-02	Core pipeline and Agent architecture

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✨ Key Features

🔬 Real Experiments	🧠 Multi-Model Routing	🔄 Checkpoint & Resume	📝 Evidence-Grounded Writing
Auto-submit GPU/SLURM jobs and collect real metrics	Configure different models for each pipeline stage	Resume from any failed stage without restart	Paper data is bound to real experiment results
📊 Auto Figure Gen	🏗️ Multi-Format Templates	🤖 Multi-Interface Control	💰 Cost Efficient
Code-based charts + AI architecture diagrams	NeurIPS / ICML / arXiv one-click switch	CLI / Claude Code / Feishu Bot	As low as $0.5/paper with DeepSeek

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🆚 Why NanoResearch

Feature	Traditional AI Writing Tools	NanoResearch
Literature Search	Partial	✅ OpenAlex + Semantic Scholar
Experiment Design	❌	✅ Auto-generated blueprints
Code Generation	Partial	✅ Complete runnable code
GPU Experiment Execution	❌	✅ Local / SLURM auto-training
Results Analysis	❌	✅ Parse real training logs
Paper Figures	❌	✅ From real data
Paper Writing	Outline/draft	✅ Full LaTeX paper
Checkpoint & Resume	❌	✅ Any stage recoverable
Multi-Model Collaboration	Single model	✅ Per-stage routing

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🎯 Use Cases

• Research Prototyping — Quickly turn a research idea into a full experiment + paper workspace
• Autonomous Experiments — Let the system generate code, submit GPU training, and analyze results
• Benchmark Generation — Batch-run multiple topics with reproducible experiment results
• Paper Draft Assistance — Produce LaTeX drafts grounded in real experimental data
• Research Audit Trail — Complete workspaces, intermediate artifacts, and logs for full traceability

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🖼️ Showcase

Break free from the manual research grind

No more debugging failed experiments, wrangling data by hand, or writing papers from scratch —

NanoResearch automates the full research workflow so you can focus on real innovation.

Overview

NanoResearch is a unified research pipeline that automates the full paper-production workflow:

• starts from a research topic
• searches and synthesizes relevant literature
• proposes an experiment blueprint
• generates runnable code and scripts
• executes locally or on SLURM
• analyzes real outputs
• generates figures
• writes a LaTeX paper draft
• reviews and revises the result

It is designed around resumable workspaces, multi-model routing, and grounded writing so that downstream paper content is tied to actual experiment evidence instead of free-form draft generation.

Why NanoResearch

Most "AI paper writers" stop at outlines or prose. NanoResearch is built for a deeper loop:

• End-to-end pipeline: topic to exportable paper workspace
• Grounded writing: writing consumes structured experiment evidence, figures, and citations
• Checkpoint + resume: failed stages can be resumed from the last saved state
• Execution-aware: supports local execution and SLURM-backed workflows
• Multi-model by stage: route ideation, coding, writing, and review to different models
• Exportable outputs: clean paper/code/figure bundles for sharing or submission prep

Use cases

• Research prototyping — quickly turn a fresh idea into a full experiment-and-paper workspace
• Benchmark generation — create repeatable topic-to-paper runs across multiple tasks
• Autonomous experimentation — let the system generate code, execute runs, and analyze outputs
• Paper drafting from evidence — produce LaTeX drafts grounded in actual experiment artifacts
• Internal research tooling — use workspaces, manifests, and stage artifacts as an auditable research log

Showcase

Generated research workspace

A typical NanoResearch run produces a clean, inspectable workspace containing:

• literature and planning artifacts
• runnable experiment code
• generated figures
• LaTeX paper sources and bibliography
• a final exported bundle for sharing or submission prep

Example outputs

Framework Overview	Generated Paper Examples
Main Results (Real Experiment Data)	Ablation Study

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🔬 Pipeline

Research Topic
     ↓
 IDEATION → PLANNING → SETUP → CODING → EXECUTION → ANALYSIS → FIGURE_GEN → WRITING → REVIEW
     ↓
 Exported: paper.pdf / paper.tex / references.bib / figures / code / data

📋 Stage Details

Stage	What It Does
IDEATION	Search literature, identify gaps, propose hypotheses, collect must-cite candidates
PLANNING	Turn the idea into a concrete experiment blueprint (datasets, baselines, metrics, ablations)
SETUP	Prepare repositories, dependencies, models, and datasets
CODING	Generate a complete runnable experiment project (training scripts, data loading, model definition)
EXECUTION	Run experiments locally or on SLURM, with automatic retry and debugging
ANALYSIS	Parse training logs and metrics into structured evidence
FIGURE_GEN	Create architecture diagrams, result comparison charts, and ablation figures
WRITING	Write and compile the LaTeX paper from experiment evidence and citations
REVIEW	Multi-perspective review, issue detection, and revision

🚀 EXECUTION Stage Core Capabilities

The EXECUTION stage is NanoResearch's core differentiator:

• Auto SLURM Submission — Generate sbatch scripts, submit to cluster, monitor job status
• Local GPU Execution — Auto-detect available GPUs and manage training processes
• Auto Debug & Retry — Analyze error logs on failure, fix code, and re-execute automatically
• Real-Time Log Monitoring — Track training progress and metric changes
• Hybrid Execution — Automatically switch between local and cluster based on task complexity

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📦 Quick Start

Follow these steps to go from install to first run in about 5 minutes.

Step 1: Install

git clone https://github.com/OpenRaiser/NanoResearch.git
cd NanoResearch
pip install -e ".[dev]"

Step 2: Configure

[!TIP] Create ~/.nanoresearch/config.json. Replace base_url and api_key with your own OpenAI-compatible API endpoint.

View full configuration example

```json { "research": { "base_url": "https://your-openai-compatible-endpoint/v1/", "api_key": "your-api-key", "template_format": "neurips2025", "execution_profile": "local_quick", "writing_mode": "hybrid", "max_retries": 2, "auto_create_env": true, "aut