karpathy-loop

name	karpathy-loop
description	Executes autonomous Loop Engineering cycles (Autoresearch) for continuous code and metric optimization.
version	2.0.0

Karpathy Loop (Autoresearch)

Implements Loop Engineering — the practice of delegating experimentation loops to AI agents that iteratively modify code, run experiments, evaluate metrics, and decide to keep or revert changes. Based on Andrej Karpathy's Autoresearch method.

Core Architecture

Three-file contract:

File	Role	Agent Editable?
prepare.py	Static experiment setup: data, tokenizer, metric definition	No (immutable baseline)
train.py (or target)	Model/code under optimization	Yes (agent's playground)
program.md	Human-written instructions, constraints, and acceptance criteria	No (human edits only)

Each iteration: agent reads program.md → forms hypothesis → edits target code → runs timed experiment (default 5 min) → evaluates metric → commits if improved, reverts if regressed.

Memory Caching Strategy

Two-layer cache inspired by the AI Engineering Guidebook:

Layer 1 — Cold Cache (CAG / KV Cache) Stable, rarely-changing context cached directly in KV memory:

• program.md instructions
• Baseline metrics and experiment contract
• Permission policies and constraints
• Shared system prompts

Avoids recomputing the same static information on every iteration. Uses Paged Attention to prevent GPU memory fragmentation.

Layer 2 — Hot Cache (Prompt Cache) Dynamic per-iteration state via OpenAI/Anthropic prompt caching:

• Agent hypotheses generated this session
• Runtime logs and partial results
• Frequently changing context

Cold + hot separation keeps cache size bounded while maximizing reuse. See /getCacheStatus endpoint for current cache utilization.

Endpoints

POST /runLoop

Start an autonomous optimization loop.

{
  "program_file": "./program.md",
  "target_file": "./train.py",
  "evaluation_script": "./prepare.py",
  "metric_name": "val_bpb",
  "max_iterations": 100,
  "timeout_seconds": 300,
  "cache_strategy": "prompt_cache"
}

Response:

{
  "status": "success",
  "data": {
    "loop_id": "loop_a1b2c3d4",
    "status": "running",
    "started_at": "2026-07-09T20:00:00Z"
  }
}

GET /getResults

Retrieve loop progress and history.

{
  "loop_id": "loop_a1b2c3d4"
}

Response:

{
  "loop_id": "loop_a1b2c3d4",
  "status": "running",
  "current_iteration": 42,
  "best_metric": {
    "name": "val_bpb",
    "value": 1.1023,
    "improvement_pct": 12.4
  },
  "total_improvements": 5,
  "iterations": [
    {
      "number": 38,
      "hypothesis": "Increase depth from 8 to 12",
      "metric_value": 1.1023,
      "accepted": true,
      "duration_seconds": 298
    }
  ],
  "cache_hits": 38,
  "cache_savings_ms": 15200
}

POST /runExperiment

Run a single isolated experiment without commit.

{
  "target_file": "./train.py",
  "dry_run": false,
  "use_cache": true
}

Response:

{
  "execution_time_seconds": 298,
  "metric": { "name": "val_bpb", "value": 1.2345 },
  "logs": "Epoch 1 loss: 2.1... val_bpb final: 1.2345",
  "cached": true
}

GET /getCacheStatus

Inspect cache state across layers.

Response:

{
  "prompt_cache": { "active_entries": 3, "hits": 38, "misses": 4, "savings_ms": 15200 },
  "kv_cache": { "active_entries": 2, "memory_usage_mb": 128, "fragmentation_pct": 3.2 },
  "cold_storage": { "cached_files": ["program.md", "baseline.json"], "size_bytes": 24576 }
}

POST /revertLast

Revert the last accepted change.

{ "loop_id": "loop_a1b2c3d4" }

Response:

{
  "status": "success",
  "reverted_iteration": 38,
  "previous_metric": { "name": "val_bpb", "value": 1.1500 }
}

Permissions

Permission	Purpose
gpu_access	Run ML experiments (PyTorch) within strict time windows
llm_api_access	Agent generates hypotheses and code edits via LLM APIs
file_storage_read_write	Read/write target files, logs, and progress artifacts
network_access	Download datasets, sync results, call LLM APIs
git_operations	Commit accepted changes, revert regressions, track history

Testing

1. lemon-cli plugin audit karpathy-loop — verify all 5 permissions requested
2. Create mock train.py that prints val_bpb: 1.50 → /runExperiment → verify metric extraction
3. Write program.md targeting metric reduction → /runLoop with max_iterations: 3 → verify autonomous cycle
4. /getCacheStatus pre/post loop — verify prompt cache hits increase with shared prefixes
5. /getResults during loop + /revertLast — verify iteration tracking and git revert