How to Build an Autonomous Research Agent with Claude Code and ARIS
A hands-on guide to setting up ARIS with Claude Code for autonomous ML research. Covers installation, configuration, and overnight automation.
Why Autonomous Research Agents Matter
Research is the bottleneck in machine learning. Reading papers, tracking experiments, and summarizing findings consumes more time than building models. ARIS (Auto-claude-code-research-in-sleep) solves this by automating the entire loop.
What Is ARIS?
ARIS is a lightweight skill set for Claude Code that turns a simple markdown repository into a fully autonomous research system. It fetches papers, reviews methods, generates experiment code, and writes daily summary reports. All while you sleep.
What You Need
- Claude Code installed locally
- An Anthropic API key
- A Git repository for ARIS to work in
- Python 3.10 or higher
Step-by-Step Setup
1. Install Claude Code
Follow the official Anthropic guide to install Claude Code on your machine.
2. Clone the ARIS Repository
git clone https://github.com/richardyc/aris.git cd aris
3. Configure Your Environment
Create a .env file with your Anthropic API key and target research topics.
4. Run ARIS
Start the autonomous loop with a single command. ARIS will begin scanning sources, analyzing papers, and generating reports.
What Happens Overnight
ARIS follows a four-stage loop: Discover, Analyze, Experiment, Report. Each cycle produces markdown files you can review in the morning.
Customization
Modify the prompts in the prompts/ directory to change research focus, output format, or source preferences.
Limitations and Costs
ARIS requires API credits for long-running loops. Start with narrow topics and short cycles to control costs.
Bottom Line
ARIS is a practical tool for anyone drowning in ML literature. It does not replace critical thinking, but it removes the mechanical burden of staying current.