| name | neurico |
| version | 0.2.0 |
| description | Autonomous research framework that orchestrates AI agents (Claude Code, Codex, Gemini) to design, execute, analyze, and document scientific experiments. Takes a structured research idea (YAML with title, domain, hypothesis) and produces code, results, plots, LaTeX papers, and GitHub repositories.
|
| tags | ["autonomous-research","ai-scientist","experiment-automation","research-agent","paper-writing","literature-review","hypothesis-testing","scientific-computing","multi-agent","machine-learning","docker","latex"] |
NeuriCo
Autonomous AI research framework. Idea in, paper out.
Quick Reference
| |
|---|
| What it does | Takes a research idea (YAML) and autonomously runs the full research lifecycle: literature review, experiment design, code execution, analysis, paper writing, GitHub push |
| Input | YAML file with 3 required fields: title, domain, hypothesis |
| Output | Code (src/), results & plots (results/), LaTeX paper (paper_draft/), GitHub repo |
| Providers | Claude Code, Codex, Gemini (OAuth login, not API keys) |
| Install | git clone https://github.com/ChicagoHAI/neurico && cd neurico && ./neurico setup |
| Source | github.com/ChicagoHAI/neurico — Chicago Human+AI Lab (ChicagoHAI), University of Chicago |
| License | Apache 2.0 |
Requirements
Minimal (one of)
| Option | What you need |
|---|
| Docker (recommended) | git + docker |
| Native | git + python>=3.10 + uv |
Resource
Access to at least one AI coding CLI (OAuth login required):
Recommended
| What | Why |
|---|
GitHub token (classic, repo scope) | Auto-creates repos and pushes results. Create here |
Optional API Keys
| Key | Purpose |
|---|
OPENAI_API_KEY | LLM-based repo naming, IdeaHub fetching, paper-finder |
S2_API_KEY | Semantic Scholar literature search via paper-finder |
OPENROUTER_KEY | Multi-model access during experiments |
COHERE_API_KEY | Improves paper-finder ranking (~7% boost) |
HF_TOKEN | Hugging Face private models/datasets |
WANDB_API_KEY | Weights & Biases experiment tracking |
Setup Tiers
- Basic: CLI login +
GITHUB_TOKEN -- full NeuriCo functionality
- Enhanced: +
OPENAI_API_KEY -- LLM repo naming + IdeaHub support
- Full: +
S2_API_KEY (+ optional COHERE_API_KEY) -- paper-finder literature search
Installation
Docker (recommended)
The Docker image is a pre-configured environment with Python, Node.js, AI coding CLIs (Claude Code, Codex, Gemini), and a full LaTeX installation for paper compilation -- so you don't have to install any of these yourself. All experiments run inside this container; nothing is installed on your host system beyond the cloned repo. The image is built from the open-source Dockerfile and hosted on GitHub Container Registry.
git clone https://github.com/ChicagoHAI/neurico && cd neurico
./neurico setup
Or step by step:
git clone https://github.com/ChicagoHAI/neurico && cd neurico
docker pull ghcr.io/chicagohai/neurico:latest
docker tag ghcr.io/chicagohai/neurico:latest chicagohai/neurico:latest
./neurico config
claude
Native
git clone https://github.com/ChicagoHAI/neurico && cd neurico
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
cp .env.example .env
claude
Invocation
Fastest: Fetch from IdeaHub and run
./neurico fetch <ideahub_url> --submit --run --provider claude
Browse ideas at IdeaHub, copy the URL, and run the command above. NeuriCo fetches the idea, creates a GitHub repo, runs experiments, writes a paper, and pushes everything.
From a YAML file
./neurico submit path/to/idea.yaml
./neurico run <idea_id> --provider claude
Run options
| Option | Description |
|---|
--provider claude|gemini|codex | AI provider (default: claude) |
--no-github | Run locally without GitHub integration |
--write-paper | Generate LaTeX paper after experiments (default: on) |
--paper-style neurips|icml|acl|ams | Paper format (default: neurips) |
--private | Create private GitHub repository |
Input Format
Only 3 fields required:
idea:
title: "Do LLMs understand causality?"
domain: artificial_intelligence
hypothesis: "LLMs can distinguish causal from correlational relationships"
Optional fields: background (papers, datasets, code references), methodology (approach, steps, baselines, metrics), constraints (compute, time, memory, budget), expected_outputs, evaluation_criteria.
Full schema: ideas/schema.yaml
Output Format
workspace/<repo-name>/
src/ # Python experiment code
results/ # Metrics, plots, models
paper_draft/ # LaTeX paper (with --write-paper)
logs/ # Execution logs
artifacts/ # Models, checkpoints
.neurico/ # Original idea spec
Results are automatically pushed to the GitHub repo created during submission.
Supported Domains
| Domain | Examples |
|---|
| Artificial Intelligence | LLM evaluation, prompt engineering, AI agents |
| Machine Learning | Training, evaluation, hyperparameter tuning |
| Data Science | EDA, statistical analysis, visualization |
| NLP | Language model experiments, text analysis |
| Computer Vision | Image processing, object detection |
| Reinforcement Learning | Agent training, policy evaluation |
| Systems | Performance benchmarking, optimization |
| Theory | Algorithmic analysis, proof verification |
| Scientific Computing | Simulations, numerical methods |
Configuration
./neurico config
./neurico setup
./neurico shell
./neurico help
Environment variables go in .env (copy from .env.example). See README for details.
Security
- No secrets are uploaded. API keys and tokens stay local in your
.env file and are never committed, pushed, or sent anywhere beyond the APIs they authenticate with. Sensitive environment variables are explicitly filtered out from all subprocess calls and sanitized from logs.
- Experiments run inside Docker. The container is isolated from your host system. The only host directories mounted are your config, templates, and workspace output folder.
- Open source. The entire codebase, including the Dockerfile and install script, is publicly auditable on GitHub.
- Built by ChicagoHAI — the Human+AI Lab at the University of Chicago.
