agr

name	agr
description	AGR: Artificial General Research — autonomous iterative optimization framework for Claude Code. Generalizes Karpathy's autoresearch to any measurable problem with variance-aware acceptance, correctness verification, and fresh-context-per-iteration (Ralph Loop). Use when setting up autoresearch, creating optimization loops, or autonomous research. Triggers on: autoresearch, AGR, auto research, optimization loop, auto optimize, artificial general research.
argument-hint	["metric-to-optimize"]
disable-model-invocation	true
allowed-tools	Bash, Read, Write, Edit, Glob, Grep, Agent

AGR: Artificial General Research

Autonomous iterative optimization for any measurable problem. Powered by Claude Code. Inspired by Karpathy's autoresearch.

Thank you Andrej Karpathy for everything you do for open source and the AI community. Your autoresearch showed that AI agents can run experiments overnight and wake up to a better system. AGR generalizes that vision beyond ML to any domain.

Also inspired by uditgoenka/autoresearch for the Guard/Metric separation and rework-on-failure patterns.

Requires: Claude Code v2.1.72+ (claude CLI in PATH)

What Is AGR?

AGR uses Claude Code's headless mode (claude -p) to run an autonomous optimization loop. Each iteration is a fresh Claude Code instance (no context degradation) that:

1. Reads state from files (what's been tried, what worked, what's next)
2. Picks ONE experiment to try
3. Implements the change
4. Measures Metric (the number to optimize) + checks Guard (must not break)
5. Keeps if improved, reworks if guard failed, discards if no improvement
6. Logs everything, updates strategy document
7. Dies — next iteration starts with fresh context

Works for any problem where you can: measure a number + verify correctness + modify code.

Quick Start

/agr speed                    # set up speed optimization
/agr accuracy                 # set up accuracy optimization
/agr "bundle size"            # set up bundle size optimization

What Makes AGR Different

vs Karpathy's autoresearch

Feature	Karpathy	AGR
Domain	ML training only	Any measurable problem
Context	One long session (degrades)	Fresh per iteration (Ralph Loop)
Correctness	Not verified	Checksums + guard command
Variance handling	Not addressed	Per-benchmark variance analysis
Failed ideas	Not tracked	Exhausted Approaches registry

vs uditgoenka/autoresearch

Feature	Udit	AGR
Variance analysis	Not explicit	Per-benchmark with artifact detection
Context management	Long session	Fresh per iteration (Ralph Loop)
Progress visualization	Not included	progress.png with breakdown chart
Supervisor pattern	Not included	Audit discards, adjust strategy between batches
Templates	Pure instructions	Generates benchmark.py, analysis.py, etc.

Unique to AGR

• Variance-aware acceptance: measurement noise in dominant benchmarks can mask real improvements. AGR checks each sub-benchmark independently and accepts if ANY improves >5% without others regressing.
• Artifact detection: if ALL experiments show the same "improvement", it's system noise, not optimization.
• Supervisor pattern: a parent agent or human monitors results.tsv, audits discards for hidden improvements, and adjusts strategy between batches.
• Multi-benchmark visualization: progress.png shows total metric + per-component breakdown.

Setup Wizard

When invoked, follow these steps:

Step 1: Understand the Project

Ask the user:

1. What to optimize? (speed, accuracy, size, cost, latency, score...)
2. How to measure it? (Metric — must output a parseable number)
3. How to verify correctness? (Guard — tests, checksums, golden outputs)
4. What can the agent modify? (files, directories)
5. What MUST NOT change? (benchmark, tests, data)
6. Build step needed? (compile, bundle, deploy)
7. Runtime path? (especially on Windows with multiple Pythons)

Step 2: Generate Files

See references/templates.md for complete templates.

{project}/
├── benchmark.py              ← Metric + Guard verification
├── baseline_checksums.json   ← Guard ground truth
├── program.md                ← Agent instructions (one iteration)
├── STRATEGY.md               ← Persistent brain
├── results.tsv               ← Experiment log (append-only)
├── analysis.py               ← Generates progress.png
├── run_agr.sh                ← Loop launcher (bash)
└── run_agr.ps1               ← Loop launcher (PowerShell)

Step 3: Baseline

1. Run benchmark.py --save to establish baseline + save checksums
2. Record baseline in results.tsv
3. Mandatory dry-run validation: confirm benchmark produces parseable number
4. Generate initial progress.png

Step 4: Launch

bash run_agr.sh --max 3   # start with 3 to validate

Core Principles

From Karpathy

• Single metric — one number, lower (or higher) is better
• Fixed benchmark — NEVER modified by the agent
• Git keep/discard — commit before running, reset if no improvement
• Log everything — failures prevent re-trying bad ideas
• Never stop — runs until human interrupts
• Simplicity criterion — complexity needs proportional improvement

From AGR

• Fresh context per iteration (Ralph Loop) — no quality degradation
• Metric + Guard separation — optimize speed without breaking tests
• Rework before discard — if guard fails but metric improved, fix implementation (2 attempts)
• Variance-aware acceptance — per-benchmark analysis beats total-only
• Stuck detection (>5 discards) — re-read everything, try opposites, combine successes
• Persistent strategy — STRATEGY.md with exhausted approaches registry
• Supervisor audit — review discards for hidden improvements

Decision Logic

1. GUARD FAILED + Metric improved?   → REWORK (max 2 attempts)
2. GUARD FAILED + Metric didn't?     → DISCARD
3. GUARD PASSED + Metric improved?   → KEEP
4. GUARD PASSED + benchmark >5% up?  → KEEP (noise-masked improvement)
5. GUARD PASSED + code simpler?      → KEEP (simplification win)
6. None of above?                    → DISCARD
7. Build crashed?                    → Fix (3 attempts) or CRASH
8. Benchmark timeout?                → CRASH

Claude Code Flags

claude -p "$(cat program.md)" \
    --dangerously-skip-permissions \
    --max-turns 100 \
    --effort high

Flag	Purpose	Recommendation
-p	Headless mode (fresh context)	Always use
--dangerously-skip-permissions	Full autonomy	Required
--max-turns 100	Safety limit	50 simple / 100 compiled
--effort high	Deep reasoning	Use for optimization
--max-budget-usd N	Cost cap	Optional
-w / --worktree	Parallel experiments	Advanced

See references/guide.md for complete documentation.

Compatibility

Designed for Claude Code (Anthropic CLI). Expanding to other AI coding agents (OpenCode, Cursor CLI, Aider) is pending — the core architecture is agent-agnostic, only loop scripts and CLI flags need adaptation.

Credits

• Andrej Karpathy — original autoresearch vision
• Udit Goenka — Guard/Metric separation, rework patterns
• Frank Bria — Ralph Loop pattern (fresh context per iteration)
• Built with Claude Code by Anthropic