redsl 1.2.24

ReDSL — Refactor + DSL + Self-Learning. LLM-powered autonomous code refactoring.

ReDSL

Refactor + DSL + Self-Learning — autonomous code refactoring with LLM, memory, and DSL.

ReDSL is a code refactoring system that combines static analysis, DSL rules, and LLM intelligence to automatically improve Python code quality.

Current project state

Based on the 2026-04-09 code2llm analysis:

• Files: 114
• Functions: 781
• Classes: 112
• Lines of code: 19,151
• Average complexity: CC̄ = 4.1
• Critical hotspots: 3
• Duplications / cycles: 0 / 0
• Test suite: 468 collected tests
• Next refactor: split format_cycle_report_markdown(), format_batch_report_markdown(), and LLMLayer.call()

Features

• 🔍 Static Analysis - Integration with popular linters and metrics tools
• 🧠 LLM with Reflection - Generate refactoring proposals with self-reflection loop
• ⚡ Hybrid Engine - Direct refactorings for simple changes, LLM for complex ones
• 📊 DSL Engine - Define refactoring rules in readable YAML format
• 💾 Memory System - Learn from refactoring history
• 🚀 Scalability - Process multiple projects simultaneously

Installation

pip install redsl

Quick Start

Basic CLI Usage

# Refactor a single project (dry run)
redsl refactor ./my-project --max-actions 5 --dry-run

# Refactor without dry run (apply changes)
redsl refactor ./my-project --max-actions 10

# Get output in YAML format (for integration)
redsl refactor ./my-project --format yaml

# Get output in JSON format (for APIs)
redsl refactor ./my-project --format json

Batch Processing

# Process semcod projects with LLM
redsl batch semcod /path/to/semcod --max-actions 10

# Hybrid refactoring (no LLM) for semcod projects
redsl batch hybrid /path/to/semcod --max-changes 30

# Batch processing with JSON output
redsl batch semcod /path/to/semcod --format json

Every refactor and batch run also writes a Markdown report next to the project or root folder:

• redsl_refactor_plan.md — --dry-run output
• redsl_refactor_report.md — executed refactor cycle
• redsl_batch_semcod_report.md — batch summary for batch semcod
• redsl_batch_hybrid_report.md — batch summary for batch hybrid

Python Code Quality Analysis

# Analyze code quality
redsl pyqual analyze ./my-project

# Analyze with custom config
redsl pyqual analyze ./my-project --config pyqual.yaml

# Get analysis in JSON format
redsl pyqual analyze ./my-project --format json

# Apply automatic fixes
redsl pyqual fix ./my-project

Debugging

# Check configuration
redsl debug config --show-env

# View DSL decisions for a project
redsl debug decisions ./my-project --limit 20

Advanced Usage

Using with CI/CD

# GitHub Actions example
- name: Run reDSL analysis
  run: |
    redsl refactor ./ --max-actions 5 --dry-run --format yaml > refactor-plan.yaml
    
- name: Upload refactoring plan
  uses: actions/upload-artifact@v3
  with:
    name: refactor-plan
    path: refactor-plan.yaml

Integration with Other Tools

# Use with jq for JSON processing
redsl refactor ./ --format json | jq '.refactoring_plan.decisions[] | select(.score > 1.0)'

# Pipe to file for review
redsl refactor ./ --format yaml > review-plan.yaml

# Extract only high-impact decisions
redsl refactor ./ --format yaml | yq '.refactoring_plan.decisions[] | select(.score > 1.5)'

Environment Configuration

Create .env file:

# LLM Configuration
OPENAI_API_KEY (set in your environment)
REFACTOR_LLM_MODEL=openai/gpt-4
REFACTOR_DRY_RUN=false

# Custom settings
REFACTOR_MAX_ACTIONS=20
REFACTOR_REFLECTION_ROUNDS=2

Available Refactoring Actions

Simple Actions (no LLM)

• REMOVE_UNUSED_IMPORTS - Remove unused imports
• FIX_MODULE_EXECUTION_BLOCK - Fix module execution blocks
• EXTRACT_CONSTANTS - Extract magic numbers to constants
• ADD_RETURN_TYPES - Add return type annotations

Implementation note: the deterministic AST helpers now live in redsl/refactors/ast_transformers.py, and redsl.refactors plus redsl.refactors.direct re-export them for backward compatibility.

Complex Actions (with LLM)

• EXTRACT_FUNCTIONS - Extract high-complexity functions
• SPLIT_MODULE - Split large modules
• REDUCE_COMPLEXITY - Reduce cyclomatic complexity

Fresh-project smoke test

To quickly verify that ReDSL runs in a brand-new project, create a tiny temporary project and run the CLI in dry-run mode:

mkdir -p /tmp/redsl-smoke
cat > /tmp/redsl-smoke/main.py <<'PY'
import os


def main() -> None:
    return None


main()
PY

python3 -m redsl analyze /tmp/redsl-smoke
python3 -m redsl refactor /tmp/redsl-smoke --dry-run --max-actions 5

REST API

Start the API server:

# Using uvicorn directly
uvicorn redsl.api:app --reload --host 0.0.0.0 --port 8000

# Using the CLI
redsl api --host 0.0.0.0 --port 8000

API Endpoints

Refactor a Project

curl -X POST "http://localhost:8000/refactor" \
  -H "Content-Type: application/json" \
  -d '{
    "project_path": "./my-project",
    "max_actions": 5,
    "dry_run": true,
    "format": "json"
  }'

Batch Processing

# Batch semcod processing
curl -X POST "http://localhost:8000/batch/semcod" \
  -H "Content-Type: application/json" \
  -d '{
    "semcod_root": "/path/to/semcod",
    "max_actions": 10,
    "format": "yaml"
  }'

# Hybrid batch processing
curl -X POST "http://localhost:8000/batch/hybrid" \
  -H "Content-Type: application/json" \
  -d '{
    "semcod_root": "/path/to/semcod",
    "max_changes": 30
  }'

Debug Endpoints

# Get configuration
curl "http://localhost:8000/debug/config?show_env=true"

# Get decisions for a project
curl "http://localhost:8000/debug/decisions?project_path=./my-project&limit=10"

Python Quality Analysis

# Analyze code quality
curl -X POST "http://localhost:8000/pyqual/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "project_path": "./my-project",
    "format": "json"
  }'

# Apply fixes
curl -X POST "http://localhost:8000/pyqual/fix" \
  -H "Content-Type: application/json" \
  -d '{
    "project_path": "./my-project"
  }'

Interactive API Documentation

Once the server is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Architecture

┌─────────────────────────────────────────────────────┐
│                  ORCHESTRATOR                       │
│   (loop: analyze → decide → refactor → reflect)    │
├─────────────┬──────────────┬────────────────────────┤
│  ANALYZER   │  DSL ENGINE  │   REFACTOR ENGINE      │
│  ─ toon.yaml│  ─ rules     │   ─ patch generation   │
│  ─ linters  │  ─ scoring   │   ─ validation         │
│  ─ metrics  │  ─ planning  │   ─ application        │
├─────────────┴──────────────┴────────────────────────┤
│            HYBRID REFACTOR ENGINES                  │
│  ─ DirectRefactorEngine (no LLM)                   │
│  ─ LLM RefactorEngine (with reflection)            │
├─────────────────────────────────────────────────────┤
│                  LLM LAYER (LiteLLM)                │
│   ─ code generation  ─ reflection  ─ self-critique  │
├─────────────────────────────────────────────────────┤
│                 MEMORY SYSTEM                       │
│   ─ episodic (refactoring history)                 │
│   ─ semantic (patterns, rules)                     │
│   ─ procedural (strategies, plans)                 │
└─────────────────────────────────────────────────────┘

Configuration

Environment variables:

• OPENAI_API_KEY or OPENROUTER_API_KEY — API key
• REFACTOR_LLM_MODEL — LLM model (e.g., openrouter/openai/gpt-5.4-mini)
• REFACTOR_DRY_RUN — test mode (true/false)

Examples

Directory	Description
examples/01-basic-analysis/	Project analysis from toon.yaml files
examples/02-custom-rules/	Define custom DSL rules
examples/03-full-pipeline/	Full cycle: analyze → decide → refactor → reflect
examples/04-memory-learning/	Memory system: episodic, semantic, procedural

License

Apache License 2.0