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Quickstart Guide

Patchling everywhere: this library also ships for browser and Node as patchling — see it running live in nanoodle.com, a no-server visual AI workflow editor built on it.

Get Patchling running in under 2 minutes.

Step 1: Install

pip install patchling

Step 2: Configure

Get your API key from nano-gpt.com/api, then set it:

# Linux/macOS
export GPTDIFF_LLM_API_KEY='your-api-key'

# Windows
set GPTDIFF_LLM_API_KEY=your-api-key

Step 3: Your First Transformation

Navigate to any project and describe the change you want:

cd your-project
patchling "Add type hints to all functions" --apply

What happens: 1. Patchling scans your project files (respecting .gitignore) 2. Sends the code + your instruction to an AI model 3. Receives a unified diff with the requested changes 4. Applies the diff to your files

Expected output:

Reading project files...
Generating diff with gemini-3-pro-preview...
Applying changes...
✅ Successfully applied patch to 3 files

Tip: The default model (gemini-3-pro-preview) works great for most tasks. For very simple changes, use a faster model:

patchling "Fix typos in comments" --model gemini-2.0-flash --apply

Step 4: Review and Commit

Patchling modifies your files directly. Use Git to review and manage changes:

# See what changed
git diff

# Stage changes interactively (review each change)
git add -p

# Commit the changes you want
git commit -m "Add type hints"

# Or discard all changes if needed
git checkout .

Three Usage Modes

Command Behavior When to Use
patchling "prompt" Creates prompt.txt only Preview what will be sent to AI
patchling "prompt" --call Creates diff.patch Review diff before applying
patchling "prompt" --apply Applies changes directly Ready to modify files

Example: Preview before applying

# First, generate and review the diff
patchling "Refactor to async/await" --call
cat diff.patch  # Review the changes

# If it looks good, apply it
patchling-apply diff.patch

Step 5: Run Continuously (Agent Loops)

Patchling becomes even more powerful when you run it in a loop. Instead of making one change at a time, let it continuously improve your codebase:

while true; do
  patchling "Add missing test cases for edge conditions" --apply
  sleep 5
done

Each cycle finds the next improvement opportunity, applies it, and continues. Real example: One overnight test coverage run went from 18 to 127 test cases—7x improvement with zero manual effort.

Popular agent loop use cases:

Goal Prompt
Expand test coverage "Add missing test cases for edge conditions"
Reduce tech debt "Refactor functions with high complexity"
Fix security issues "Find and fix OWASP Top 10 vulnerabilities"
Sync documentation "Update docs to match implementation"

For detailed automation patterns, see the Agent Loops Guide.

Tips for Success

  • Start small: Test with a focused change before attempting large refactors
  • Review first: Use --call to preview changes before applying
  • Target specific files: For large codebases, specify directories to reduce context: bash patchling "Add logging" src/api/ src/utils/
  • Expect timing variance: Complex changes may take 30-60 seconds depending on the model
  • Always review: AI-generated code should be checked, especially for error handling and edge cases
  • Scale up with loops: Once you're comfortable, run Patchling overnight—one user went from 18 to 127 test cases in 8 hours

Next Steps