REMOROO

Why Remoroo?

Understanding the type of work Remoroo excels at: Refactoring, Optimization, and Self-Correction.

Philosophy

Software engineering is evolving from a human-in-the-loop process (where you type strictly what you think) to a human-on-the-loop process (where you guide agents to build what you need).

Remoroo is an offline-first autonomous engine designed for this new era. Unlike simple autocomplete tools, Remoroo agents take a high-level goal and a metric, then iterate autonomously in a sandbox until they produce a verified solution.

What is Remoroo Best For?

Remoroo shines when the task is objective and verifiable. If you can measure success, Remoroo can achieve it.

1. Large-Scale Refactoring

Updating dependencies, migrating legacy codebases, or standardizing patterns across hundreds of files is tedious for humans but trivial for agents.

[!NOTE] Example Task: Standardize Type Hints

Goal: "Add type hints to all untyped functions in remoroo_cp/auth."

Metric:

mypy remoroo_cp/auth --strict
# Must pass with zero errors

[!NOTE] Example Task: Modularization Refactor

Goal: "Refactor the monolithic etl_script.py into a modular package with separate strategy classes."

Metric:

pytest
# AND check imports work:
python -c "import remoroo_etl.strategies"

2. Optimization

Tuning hyperparameters, optimizing Dockerfiles for size, or improving Python script performance.

[!NOTE] Example Task: Performance Optimization

Goal: "Vectorize the data preprocessing loop in etl.py using NumPy."

Metric:

# Execution time < 200ms on test dataset
elapsed_time < 0.2

3. Self-Correction & Bug Fixing

Given a failing test, Remoroo can explore the codebase to identify the root cause and patch it.

[!NOTE] Example Task: Fix PyTorch Error

Goal: "Fix the RuntimeError: size mismatch in the PyTorch training loop."

Metric:

python train_model.py
# Must complete 1 epoch without crash

The Workflow

The core loop is simple:

  1. Define a Goal: Tell the agent what you want.
  2. Set a Metric: Tell the agent how to measure success.
  3. Run: The agent iterates, tests, and refines until the metric is met.

Ready to start? Install the CLI to run your first experiment.