/autoresearch — Autonomous Experiment Loop for Claude Code
한글 README
Inspired by karpathy/autoresearch (43.7k stars). Adapted for any project type — not just ML.
An autonomous experiment loop that modifies a single file, runs experiments, and keeps improvements. It never stops until you tell it to.
How It Works
LOOP FOREVER:
1. Analyze current project state
2. Generate an improvement idea
3. Modify the target file
4. Run the experiment (build / test / train)
5. Parse the metric from output
6. Improved? → keep. Same or worse? → discard (git reset).
7. Log to results.tsv + JSONL
8. Next idea → repeat
Supported Project Types
Auto-detected from project files — no manual configuration needed.
| Type | Detection | Default Target | Default Metric | Direction |
|---|
| ML | train.py + prepare.py | train.py | val_bpb | lower is better |
| Web (Node.js) | package.json | auto-detected main file | bundle size (KB) | lower is better |
| Flutter | pubspec.yaml | lib/main.dart | APK size (MB) | lower is better |
| Java/Kotlin | pom.xml / build.gradle | auto-detected main | build time (s) | lower is better |
| Custom | CLAUDE.md autoresearch config | user-defined | user-defined | user-defined |
Usage
/autoresearch # Start autonomous experiment loop
/autoresearch setup # Initialize environment only (create branch, results.tsv)
/autoresearch results # View experiment results
/autoresearch train.py # Use specific file as target
Custom Configuration
Override defaults by adding an autoresearch section to your project's CLAUDE.md:
## autoresearch
- target_file: src/model.py
- run_command: python train.py --epochs 5
- metric_name: accuracy
- metric_parse: grep "accuracy:" run.log | tail -1 | awk '{print $2}'
- metric_direction: higher_is_better
- time_budget: 600
- readonly_files: data/dataset.py, config.yaml
| Setting | Description | Default |
|---|
target_file | The single file to modify | Auto-detected |
run_command | Command to run each experiment | Based on project type |
metric_name | Name of the metric to track | Based on project type |
metric_parse | Shell command to extract metric value | Based on project type |
metric_direction | lower_is_better or higher_is_better | lower_is_better |
time_budget | Max seconds per experiment | 300 |
readonly_files | Comma-separated files that must not be modified | None |
How It Compares
| karpathy/autoresearch | /autoresearch (this) |
|---|
| Scope | ML model training only | Any project type (ML, Web, Flutter, Java, custom) |
| Setup | Manual Python environment | Auto-detect from project files |
| Configuration | Hardcoded in source | CLAUDE.md-based, fully customizable |
| Logging | TSV only | TSV + JSONL (includes prev, delta, memory_gb, timestamp) |
| Git integration | Manual | Auto-creates autoresearch/$TAG branch |
| Hardware | NVIDIA GPU required | No hardware requirements (runs in Claude Code) |
| Metric type | Fixed (val_bpb) | Any metric you can parse from stdout/log |
See the full comparison for a detailed analysis.
Logging
Every experiment is recorded in two formats:
results.tsv (human-readable)
commit metric value status description
a1b2c3d val_bpb 0.997900 keep baseline
b2c3d4e val_bpb 0.993200 keep increase LR to 0.04
c3d4e5f val_bpb 1.005000 discard switch to GeLU activation
d4e5f6g val_bpb 0.000000 crash double model width (OOM)
JSONL (machine-readable)
Stored at .claude/logs/autoresearch.jsonl with additional fields: prev, delta, memory_gb, tag, timestamp.
Querying Logs
# Recent 10 experiments
grep experiment_done .claude/logs/autoresearch.jsonl | tail -10 | jq .
# Successful improvements only
jq 'select(.details.status == "keep")' .claude/logs/autoresearch.jsonl
# Metric trend (TSV output)
grep experiment_done .claude/logs/autoresearch.jsonl | \
jq -r '[.local_time[:19], .details.status, .details.value] | @tsv'
Core Rules
- NEVER STOP — Runs until manually interrupted
- Single file only — Only modifies
target_file; all other files are read-only
- Keep or discard — Improved metric → keep. Same or worse →
git reset --hard HEAD~1
- Log everything — Every experiment is recorded, including crashes
- Simpler wins — Same metric improvement with less code → keep
- Deletion is best — Removing code while maintaining performance is the ideal outcome
Install
