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Changelog

All notable changes to DeepCode will be documented in this file.

[1.0.6-jm] - 2025-10-19

Added

  • Dynamic Model Limit Detection: New utils/model_limits.py module that automatically detects and adapts to any LLM model's token limits and pricing
  • Loop Detection System: utils/loop_detector.py prevents infinite loops by detecting repeated tool calls, timeouts, and progress stalls
  • Progress Tracking: 8-phase progress tracking (5% → 100%) with file-level progress indicators in both UI and terminal
  • Abort Mechanism: "Stop Processing" button in UI with global abort flag for clean process termination
  • Cache Cleanup Scripts: start_clean.bat and start_clean.ps1 to clear Python cache before starting
  • Enhanced Error Display: Real-time error messages in both UI and terminal with timestamps
  • File Progress Tracking: Shows files completed/total with estimated time remaining

Fixed

  • Critical: False Error Detection: Fixed overly aggressive error detection that was marking successful operations as failures, causing premature abort and empty file generation
  • Critical: Empty File Generation: Files now contain actual code instead of being empty (2-byte files)
  • Unique Folder Naming: Each project run now creates paper_{timestamp} folders instead of reusing pdf_output
  • PDF Save Location: PDFs now save to deepcode_lab/papers/ instead of system temp directory
  • Duplicate Folder Prevention: Added session state caching to prevent duplicate folder creation on UI reruns
  • Token Limit Compliance: Fixed max_tokens to respect model limits dynamically (e.g., gpt-4o-mini's 16,384 token limit)
  • Empty Plan Detection: System now fails early with clear error messages when initial plan is empty or invalid
  • Process Hanging: Fixed infinite loops and hanging on errors - process now exits cleanly
  • Token Cost Tracking: Restored accurate token usage and cost display (was showing $0.0000)
  • PDF to Markdown Conversion: Fixed automatic conversion and file location handling
  • Document Segmentation: Properly uses configured 50K character threshold from mcp_agent.config.yaml
  • Error Propagation: Abort mechanism now properly stops process after 10 consecutive real errors

Changed

  • Model-Aware Token Management: Token limits now adapt automatically based on configured model instead of hardcoded values
  • Cost Calculation: Dynamic pricing based on actual model rates (OpenAI, Anthropic)
  • Retry Logic: Token limits for retries now respect model maximum (87.5% → 95% → 98% of max)
  • Segmentation Workflow: Better integration with code implementation phase
  • Error Handling: Enhanced error propagation - errors no longer reported as "success"
  • UI Display: Shows project folder name after PDF conversion for better visibility
  • Terminal Logging: Added timestamps to all progress messages

Technical Improvements

  • Added document-segmentation server to code implementation workflow for better token management
  • Improved error handling in agent orchestration engine with proper cleanup
  • Enhanced subprocess handling on Windows (hide console windows, prevent hanging)
  • Better LibreOffice detection on Windows using direct path checking
  • Fixed input data format consistency (JSON with paper_path key)
  • Added comprehensive logging throughout the pipeline
  • Improved resource cleanup on errors and process termination

Documentation

  • Translated Chinese comments to English in core workflow files
  • Added inline documentation for new utility modules
  • Created startup scripts with clear usage instructions

Breaking Changes

  • None - all changes are backward compatible

Known Issues

  • Terminal may show trailing "Calling Tool..." line after completion (cosmetic display artifact - process completes successfully)
  • Some Chinese comments remain in non-critical files (cli, tools) - translation in progress
  • tiktoken package optional warning (doesn't affect functionality)

Success Metrics

  • ✅ Complete end-to-end workflow: DOCX upload → PDF conversion → Markdown → Segmentation → Planning → Code generation
  • ✅ Files generated with actual code content (15+ files with proper implementation)
  • ✅ Single folder per project run (no duplicates)
  • ✅ Dynamic token management working across different models
  • ✅ Accurate cost tracking per model
  • ✅ Clean process termination with proper error handling

[1.0.5] - Previous Release

See previous releases for earlier changes.