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RAGIX v0.11.1

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@ovitrac ovitrac released this 28 Nov 17:11
· 92 commits to main since this release
v0.11.0
0096fd5

RAGIX Release v0.11.1

Build: 202511281400
Date: 2025-11-28

1. Overview

Version 0.20.0 marks a major leap forward for RAGIX, evolving it from a development assistant into a production-ready, multi-agent orchestration platform. This release introduces a powerful AST-based code analysis engine, a comprehensive Web UI, and a full suite of documentation, solidifying its position as a sovereign, local-first tool for professional software engineering.

2. 🚀 Major Features & Capabilities

This release is packed with new, deeply integrated capabilities that provide a powerful and cohesive experience.

2.1. Advanced AST Code Analysis

RAGIX now parses your entire codebase (Python & Java) into an Abstract Syntax Tree (AST), enabling deep, structural analysis and visualization.

  • Key Features: Dependency graph generation, cycle detection, code metrics (cyclomatic complexity, technical debt), and an interactive query language.
  • Example (ragix-ast CLI): Find all classes that extend BaseService and are decorated with @Transactional. 
    ragix-ast search ./src "type:class extends:BaseService @Transactional"

2.2. Multi-Agent Orchestration

Define, execute, and stream complex workflows with a dependency-aware graph executor.

  • Key Features: Pre-built templates for common tasks (bug_fix, code_review, feature_addition), parallel execution, and a clear separation of Planner, Worker, and Verifier agents.
  • Example (Python): 
    from ragix_core import get_template_manager

manager = get_template_manager()
graph = manager.instantiate("bug_fix", {
    "bug_description": "TypeError in handler.py",
})
# ... then run with GraphExecutor

2.3. Hybrid Search Engine

Combine the best of keyword-based (BM25) and semantic (Vector) search for highly accurate code retrieval.

  • Key Features: Multiple fusion strategies (RRF, Weighted, Interleave), clear source attribution for results, and a simple API.

2.4. Flexible LLM Backends

Choose the right LLM for the job, with a clear distinction between sovereign and cloud-based models.

  • Sovereign (Default): 🟢 Ollama. 100% local, private, and free. Recommended for sensitive codebases.
  • Cloud-Based: 🔴 Claude & OpenAI. Higher reasoning quality at the cost of sending data to third-party APIs.

2.5. Production-Grade Infrastructure

  • Monitoring: Health checks for system components (/api/health).
  • Resilience: Built-in patterns like CircuitBreaker and retry_async for robust tool execution.
  • Caching: In-memory and disk-based caching for LLM responses and tool calls to improve performance.

3. 💻 Developer Experience & Usability

3.1. Comprehensive Documentation Suite

A full suite of documentation has been created to make RAGIX accessible and easy to learn.

Document Location Description
CLI Guide docs/CLI_GUIDE.md A complete reference for all ragix-* commands.
AST Guide docs/AST_GUIDE.md A deep dive into code analysis with ragix-ast.
API Reference docs/API_REFERENCE.md REST API documentation for the ragix-web server.
Architecture docs/ARCHITECTURE.md An overview of the system architecture.
Playbook Guide docs/PLAYBOOK_GUIDE.md How to write ragix-batch automation playbooks.

3.2. New Web UI

A new, comprehensive Web UI (ragix-web) provides a graphical interface for:

  • Interactive agent chat.
  • Live AST visualizations (dependency graphs, DSM matrices, radial explorers).
  • Session management and log viewing.

3.3. Project Reorganization

  • The project root has been cleaned significantly.
  • Old development and planning documents are now in docs/archive/.
  • Internal technical notes are consolidated in docs/developer/.
  • Every project folder now contains a README.md.

4. 🐞 Bug Fixes

  • Web UI: Fixed a critical AttributeError on the /api/agents/config endpoint that caused a 500 Internal Server Error.
  • Numerous other minor stability improvements.
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