The get_available_models method in ModelProviderRegistry was only checking
for providers with SUPPORTED_MODELS attribute, which OpenRouter doesn't have.
This caused auto mode to fail with "No models available" error when only
OpenRouter API key was configured.
Added special handling for OpenRouter provider to check its _registry
for available models, ensuring auto mode works correctly with OpenRouter.
Added comprehensive tests to verify:
- Auto mode works with only OpenRouter configured
- Model restrictions are respected
- Graceful handling when no providers are available
- No crashes when OpenRouter lacks _registry attribute
Improved testgen; encourages follow-ups with less work in between and less token generation to avoid surpassing the 25K barrier
Improved coderevew tool to request a focused code review instead where a single-pass code review is too large or complex
OPENROUTER_ALLOWED_MODELS environment variable support to further limit the models to allow from within Claude. This will put a limit on top of even the ones listed in custom_models.json
- Added o3-pro model configuration to custom_models.json with 200K context
- Updated OpenAI provider to support o3-pro with fixed temperature constraint
- Extended simulator tests to include o3-pro validation scenarios
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Further fixes to tests
Pass O3 simulation test when keys are not set, along with a notice
Updated docs on testing, simulation tests / contributing
Support for OpenAI o4-mini and o4-mini-high
- Added autouse fixture to mock provider availability in tests
- Updated test expectations to match new auto mode behavior
- Fixed mock provider capabilities to return proper values
- Updated claude continuation tests to set default model
- All 256 tests now passing
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Encourage Claude to pick the best model for the job automatically in auto mode
Lots of new tests to ensure automatic model picking works reliably based on user preference or when a matching model is not found or ambiguous
Improved error reporting when bogus model is requested and is not configured or available
* Support for Custom URLs and custom models, including locally hosted models such as ollama
* Support for native + openrouter + local models (i.e. dozens of models) means you can start delegating sub-tasks to particular models or work to local models such as localizations or other boring work etc.
* Several tests added
* precommit to also include untracked (new) files
* Logfile auto rollover
* Improved logging
Simulation tests to confirm threading and history traversal
Chain of communication and branching validation tests from live simulation
Temperature enforcement per model
## Major Features Added
### 🎯 Dynamic Configuration System
- **Environment-aware model selection**: DEFAULT_MODEL with 'pro'/'flash' shortcuts
- **Configurable thinking modes**: DEFAULT_THINKING_MODE_THINKDEEP for extended reasoning
- **All tool schemas now dynamic**: Show actual current defaults instead of hardcoded values
- **Enhanced setup workflow**: Copy from .env.example with smart customization
### 🔧 Model & Thinking Configuration
- **Smart model resolution**: Support both shortcuts ('pro', 'flash') and full model names
- **Thinking mode optimization**: Only apply thinking budget to models that support it
- **Flash model compatibility**: Works without thinking config, still beneficial via system prompts
- **Dynamic schema descriptions**: Tool parameters show current environment values
### 🚀 Enhanced Developer Experience
- **Fail-fast Docker setup**: GEMINI_API_KEY required upfront in docker-compose
- **Comprehensive startup logging**: Shows current model and thinking mode defaults
- **Enhanced get_version tool**: Reports all dynamic configuration values
- **Better .env documentation**: Clear token consumption details and model options
### 🧪 Comprehensive Testing
- **Live model validation**: New simulator test validates Pro vs Flash thinking behavior
- **Dynamic configuration tests**: Verify environment variable overrides work correctly
- **Complete test coverage**: All 139 unit tests pass, including new model config tests
### 📋 Configuration Files Updated
- **docker-compose.yml**: Fail-fast API key validation, thinking mode support
- **setup-docker.sh**: Copy from .env.example instead of manual creation
- **.env.example**: Detailed documentation with token consumption per thinking mode
- **.gitignore**: Added test-setup/ for cleanup
### 🛠 Technical Improvements
- **Removed setup.py**: Fully Docker-based deployment (no longer needed)
- **REDIS_URL smart defaults**: Auto-configured for Docker, still configurable for dev
- **All tools updated**: Consistent dynamic model parameter descriptions
- **Enhanced error handling**: Better model resolution and validation
## Breaking Changes
- Removed setup.py (Docker-only deployment)
- Model parameter descriptions now show actual defaults (dynamic)
## Migration Guide
- Update .env files using new .env.example format
- Use 'pro'/'flash' shortcuts or full model names
- Set DEFAULT_THINKING_MODE_THINKDEEP for custom thinking depth
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This major refactoring addresses critical bugs in conversation history management
and significantly improves token efficiency through intelligent file embedding:
**Key Improvements:**
• Fixed conversation history duplication bug by centralizing reconstruction in server.py
• Added intelligent file filtering to prevent re-embedding files already in conversation history
• Centralized file processing logic in BaseTool._prepare_file_content_for_prompt()
• Enhanced log monitoring with better categorization and file embedding visibility
• Updated comprehensive test suite to verify new architecture and edge cases
**Architecture Changes:**
• Removed duplicate conversation history reconstruction from tools/base.py
• Conversation history now handled exclusively by server.py:reconstruct_thread_context
• All tools now use centralized file processing with automatic deduplication
• Improved token efficiency by embedding unique files only once per conversation
**Performance Benefits:**
• Reduced token usage through smart file filtering
• Eliminated redundant file embeddings in continued conversations
• Better observability with detailed debug logging for file operations
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses several critical issues and improvements:
🔧 Critical Fixes:
- Fixed conversation history not being included when using continuation_id in AI-to-AI conversations
- Fixed test mock targeting issues preventing proper conversation memory validation
- Fixed Docker debug logging functionality with Gemini tools
🐛 Bug Fixes:
- Docker compose configuration for proper container command execution
- Test mock import targeting from utils.conversation_memory.* to tools.base.*
- Version bump to 3.1.0 reflecting significant improvements
🚀 Improvements:
- Enhanced Docker environment configuration with comprehensive logging setup
- Added cross-tool continuation documentation and examples in README
- Improved error handling and validation across all tools
- Better logging configuration with LOG_LEVEL environment variable support
- Enhanced conversation memory system documentation
🧪 Testing:
- Added comprehensive conversation history bug fix tests
- Added cross-tool continuation functionality tests
- All 132 tests now pass with proper conversation history validation
- Improved test coverage for AI-to-AI conversation threading
✨ Code Quality:
- Applied black, isort, and ruff formatting across entire codebase
- Enhanced inline documentation for conversation memory system
- Cleaned up temporary files and improved repository hygiene
- Better test descriptions and coverage for critical functionality
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
