- Enhanced tool call result parsing with improved scoring logic (0.0 or 1.0)
- Updated tool memory schema to reflect binary success/failure scoring
- Added deterministic behavior support via seed configuration in mock tools
- Improved evaluation prompts to focus on result quality over success flags
- Extended README with tool memory documentation and usage examples- Added utility functions for generating mock tool call results
- Removed deprecated test file for UseMockSearchOp- Updated default configurations to include use_mock_search operation- Bumped version to0.1.10 and updated flowllm dependency requirement
- Moved deprecation warnings to main init file
- Simplified tool memory summary formatting by removing redundant statistics
- Fixed tool call result processing to handle multiple tool names concurrently
- Added SummaryToolMemoryOp for analyzing tool usage patterns- Implemented RetrieveToolMemoryOp for fetching tool memories
- Enhanced ParseToolCallResultOp with evaluation and scoring logic
- Updated ToolMemory schema with summary, evaluation, and score fields
- Added new prompt templates for tool evaluation and summarization
- Configured tool memory operations in default.yaml
- Fixed success flag logic in ToolCallResult processing
- Improved statistical analysis for tool call history
- Added concurrent processing for tool call evaluations
- Integrated tool memory updates with vector store operations
- Add new ToolCallResult and ToolMemory schemas for tracking tool executions
- Implement tool memory retrieval and summarization flows in default config
- Register new parse_tool_call_result_op for processing tool call results
- Extend memory conversion logic to support tool memory type
- Add test cases for tool memory serialization and deserialization
- Include token counting utilities for text processing tasks
- Remove deprecated `add_dict_filter` method from vector store operations
- Update `async_search` method to use `filter_dict` parameter for advanced filtering
- Modify examples in documentation to use the new `filter_dict` format for advanced filtering
- Update task memory and messages for consistency with new filtering approach
- Add BaseMemoryService abstract class with common memory operations
- Implement PersonalMemoryService and TaskMemoryService concrete classes
- Update default config to use async services for memory operations
- Modify recall and retrieve ops to use context for top_k parameter
- Update vector store action op to handle list action and return result directly
- Update load_today_memory_op.py to use async version of _retrieve_today_memories
- Update memory_deduplication_op.py to use async version of _deduplicate_task_memories and _get_existing_task_memory_embeddings
- Update retrieve_memory_op.py to use async_search instead of search
- Modify LLM-related ops to use async_execute instead of execute
- Replace chat method with achat for asynchronous LLM calls
- Update method signatures and return types to support async operations
- This change affects multiple files across the project
- Update app.py to use async service initialization
- Refactor multiple ops to use async_execute instead of execute
- Add support for stream and use_async flags in config
- Update LLM usage to use achat instead of chat
- Add new LLM models and update existing ones in config
- Improve error handling and logging in several ops
- Update dependencies and Python version requirements
- Rename and restructure personal memory consolidation flow
- Update memory handling in context and operations
- Refactor memory schema to use time_created and time_modified fields
- Improve error handling and logging in memory operations
- Update test cases for new memory consolidation flow
- Rename and restructure memory loading operations
- Enhance time extraction and formatting capabilities- Implement more robust memory filtering and ranking logic
- Refactor observation extraction methods for better reusability
- Improve logging and error handling in memory operations
- Rename `worker` to `op` for operation classes
- Remove `memoryscope` package
- Create `constants` package with common constants
- Update module imports and class names accordingly
- Add agent module to reme_ai package
- Update retrieve module to include agent-related functionality
- Modify summary module to use new agent features
- Update configuration to support agent operations
- Update import statements to use relative imports where appropriate
- Rename classes to match file names for better consistency
- Add new imports for recently added modules
- Add summary module with simple summary and personal summary tasks
- Implement new vector store operations for updating and managing memory
- Refactor retrieve module to improve query building and memory rewriting
- Update schema and utils modules for better data handling and PDF processing
- Rename experiencemaker package to reme_ai
- Move personal modules to new directory structure
- Remove unused classes and imports
- Update module initialization files
- Add BuildQueryOp to construct query for memory retrieval-Implement MergeMemoryOp to combine retrieved memories
- Create RecallVectorStoreOp to fetch memories from vector store
- Develop memory representation and conversion methods
- Establish initial project structure and dependencies
