LLM Orchestrator - Release Notes

🚀 Major Features & Architectural Overhaul

Unified Installer & App: Completely refactored the PyInstaller build pipeline. The application is now distributed as a single llm-orchestrator-setup.exe monolithic executable, dropping the confusing two-executable system.
Smart Entrypoint: The executable now dynamically inspects its execution name. If run as "setup" or "install", it launches the Tkinter UI to configure your environment (.env) and installs itself to your local AppData. If run as orchestrator.exe, it launches the main LLM backend.
Fixed Dynamic Imports: Resolved a critical ModuleNotFoundError: No module named 'tools.git.core' crash in production builds by explicitly linking dynamic tool submodules into PyInstaller's hiddenimports.

Codebase Indexing: The agent now features persistent "Retrieval Memory". Every time the agent reads, writes, edits, or searches a file, it automatically extracts AST symbols (functions, classes), docstrings, and imports, updating its global map of your workspace.
smart_context_search Integration: Searches now utilize a 3-pronged approach for instantaneous codebase navigation: BM25 keyword scoring, AST symbol lookup, and Semantic Vector embeddings (powered by sentence-transformers).
Session Persistence: Vector indices and symbol maps are saved locally to .orchestrator_memory/, meaning the agent remembers your project layout across system restarts.
Stats Command: Added the /memory slash command so you can actively monitor the size and health of your codebase index.

"Thinking..." Animation: Added a sleek, persistent loading animation to the terminal UI during complex LLM reasoning and API waits, providing better feedback during long execution sequences.
Context Handling Constraints Lifted: Removed overly restrictive context compaction rules, allowing for significantly longer and more detailed agent interaction sequences before truncation occurs.

API Circuit Breaker (ServerHealthTracker): Implemented a protective health tracker that prevents the orchestrator from hammering the LLM endpoint during persistent "500 Internal Server Error" failures.
Schema Compliance: Fixed strict role injection errors by preventing unauthorized system role insertions mid-conversation, ensuring complete compliance with the OpenAI/Gemini chat schemas.
Model Configuration Matching: Fixed the environment variable resolution chain so that LLM_MODEL specified in .env accurately cascades through the installer into config.json, permanently resolving "Model not found" errors upon startup.
File Writing Hardening: Strengthened file-writing strategies to prevent workspace clutter and ensure atomic file operations when the agent operates autonomously.