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RAGIX 0.60.0 – Contractive Reasoning & Agentic Profiles

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@ovitrac ovitrac released this 14 Dec 17:42
· 60 commits to main since this release
v0.60.0
24f70b4

RAGIX v0.60.0 Release Notes

Release Date: 2025-12-14
Author: Olivier Vitrac, PhD, HDR | olivier.vitrac@adservio.fr | Adservio

Overview

Version 0.60.0 enhances RAGIX's position as a sovereign swiss knife for industrial-scale code auditing. This release focuses on:

  1. MCP Server Enhancement — 5 new tools for comprehensive system introspection
  2. Parallel KOAS Execution — Faster audits through dependency-aware parallelization
  3. French i18n Fixes — Proper UTF-8 diacritics in audit reports
  4. System Introspection — GPU, CPU, memory, and model management

Key Features

1. MCP Server v0.8.0 (22 Tools Total)

Five new tools expand RAGIX's MCP capabilities:

Tool Purpose
ragix_ast_scan Extract AST symbols from source code
ragix_ast_metrics Compute code quality metrics
ragix_models_list List available Ollama models
ragix_model_info Get detailed model information
ragix_system_info Comprehensive system introspection

Example: System Info

result = ragix_system_info()
# Returns: platform, cpu, memory, gpu (CUDA), disk, ollama status

Example: AST Scan

result = ragix_ast_scan("/path/to/project", language="auto")
# Returns: symbols, summary (classes, methods, functions)

2. Parallel KOAS Execution

The koas_run tool now supports parallel kernel execution:

# Sequential (default)
koas_run(workspace, stage=0)  # ~5-10s for 60K LOC

# Parallel (new)
koas_run(workspace, stage=0, parallel=True, workers=4)  # ~3.4s for 60K LOC

Performance gains:

  • Stage 1 (Data Collection): 2-3x faster
  • Stage 2 (Analysis): ~2x faster with batching
  • Stage 3 (Reporting): Unchanged (sequential for consistency)

3. French i18n Corrections

Fixed 50+ translation strings for proper UTF-8 diacritics in French reports:

Before After
Methodologie Méthodologie
Synthese Executive Synthèse Exécutive
Complexite Complexité
Indice de Maintenabilite Indice de Maintenabilité
Évaluation des Risques Évaluation des Risques

Files updated:

  • ragix_kernels/audit/report/i18n.py
  • ragix_kernels/audit/report/templates.py
  • ragix_kernels/audit/section_drift.py

4. Claude Code Slash Commands

New and updated commands:

Command Description
/koas-audit Full audit with --parallel option
/ragix-system System introspection for deployment
/ragix-models Model management and selection

Technical Details

New MCP Tool Signatures

# AST Analysis
def ragix_ast_scan(
    path: str,
    language: str = "auto",
    include_private: bool = False,
) -> Dict[str, Any]: ...

def ragix_ast_metrics(
    path: str,
    language: str = "auto",
) -> Dict[str, Any]: ...

# Model Management
def ragix_models_list() -> Dict[str, Any]: ...
def ragix_model_info(model: str) -> Dict[str, Any]: ...

# System Info
def ragix_system_info() -> Dict[str, Any]: ...

KOAS Run Enhanced Signature

def koas_run(
    workspace: str,
    stage: int = 0,
    kernels: str = "",
    parallel: bool = False,  # NEW
    workers: int = 4,        # NEW
) -> Dict[str, Any]:
    # Returns: status, stages_run, results, report_path,
    #          execution_mode, workers, duration_seconds

Test Coverage

New test file: tests/test_mcp_server.py

18 tests covering:

  • AST scan single file and directory
  • Private symbol filtering
  • Non-existent path handling
  • Metrics computation
  • Model listing (mocked Ollama)
  • Model info retrieval
  • System info structure
  • KOAS parallel parameter validation
  • Tool availability checks (core, KOAS, v0.8.0)
pytest tests/test_mcp_server.py -v
# Result: 18 passed in 4.34s

Performance Benchmarks

IOWIZME Audit (Java, 60K LOC)

Metric Value
Total Files 806
Total Classes 582
Total Methods 2,704
Lines of Code 60,157
Audit Time (parallel) 3.4 seconds
Maintainability Index 100/100 (Grade A)
Technical Debt 0.0 days

Throughput Estimates

Project Size Time (parallel) Hourly Rate
10K LOC ~1s 3,600/hour
60K LOC ~3-4s 900-1,200/hour
200K LOC ~10-15s 240-360/hour
1M LOC ~60s 60/hour

Installation

No new dependencies required. Existing installations update automatically:

pip install --upgrade ragix
# Or from source:
pip install -e .

Migration Notes

From v0.59.0

  • No breaking changes
  • All existing MCP tools continue to work
  • KOAS workspaces are fully compatible
  • New tools available immediately after upgrade

For MCP Clients

Update tool invocations to use new features:

// Old
{"tool": "koas_run", "workspace": "/path"}

// New (with parallel)
{"tool": "koas_run", "workspace": "/path", "parallel": true, "workers": 4}

Known Issues

None reported.

What's Next (v0.61.0)

Potential areas for future improvement:

  • GPU acceleration for AST parsing (CUDA)
  • Streaming audit progress via MCP
  • Multi-repository batch auditing
  • Real-time audit dashboard

Video Demonstration

Watch the RAGIX demo showcasing key features:
https://www.youtube.com/watch?v=vDHI70ZPnDE

Industrial Applications

RAGIX v0.60.0 is ready for:

  • Enterprise Code Audits: Thousands of files, millions of classes
  • MCO (Maintenance) Assessments: Technical debt quantification
  • Continuous Quality Monitoring: Regular codebase reviews
  • Documentation Gap Analysis: Code-spec drift detection

With proper GPU and CPU resources, organizations can audit:

  • 3-20 codebases per hour (depending on size)
  • Hundreds of projects per day
  • Full deterministic, statistical, and AI-powered analysis

Contributors

  • Olivier Vitrac — Architecture, implementation, testing

Generated by KOAS (Kernel-Orchestrated Audit System)
RAGIX — Retrieval-Augmented Generative Interactive eXecution Agent

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