* update
* refactor(memory): remove unnecessary type check and update error logging
* refactor(core): standardize logger import and update agentscope dependency
* fix(memory): disable console output and add logging for summarizer component
* feat(core): replace OpenAI token counter with custom ReMe token counter
- Replace OpenAITokenCounter with ReMeTokenCounter implementation
- Add support for HuggingFace mirror and configurable tokenizer
- Register ReMeTokenCounter as default token counter in registry
- Update config to use hf backend with Qwen2.5-7B-Instruct model
refactor(memory): convert token counting methods to async in message handlers
- Change count_str_token, stat_message, count_msgs_token to async methods
- Update format_msgs_to_str and context_check to use async token counting
- Modify _format_tool_result_output to support async token counting
- Adjust all dependent methods to await async token counting calls
feat(memory): add dialog persistence to in-memory storage
- Implement _append_messages_to_dialog for saving messages to JSONL files
- Add dialog_path parameter to ReMeInMemoryMemory constructor
- Persist messages to daily JSONL files based on timestamp grouping
- Update mark_messages_compressed to save and remove compressed messages
- Modify clear_content to persist all messages before clearing memory
refactor(ops): update token counter type hints and initialization
- Change BaseOp to use HuggingFaceTokenCounter instead of TokenCounterBase
- Update type annotations for as_token_counter property and parameters
- Remove direct token counter injection from Compactor and ContextChecker
- Pass as_token_counter parameter through service context mechanism
style(logging): improve error logging with exception details
- Replace logger.error with logger.exception in browser control tool
- Change logger.error to logger.exception in memory get tool error handling
- Add proper exception logging with stack trace information
chore(config): add token counter configuration to light YAML
- Add as_token_counters section with default hf backend configuration
- Configure Qwen/Qwen2.5-7B-Instruct model with mirror support enabled
- Set up pretrained_model_name_or_path and use_mirror parameters
test(context): update context check tests to async implementation
- Convert verify_context_check_invariants to async function
- Update context check test methods to use async calls
- Change stat_message calls to await async implementation
- Modify test_empty_messages and test_below_threshold_returns_all to async
* feat(core): implement context checking and memory management features
* refactor(core): replace direct loguru import with logger utility function
* refactor(reme): remove RuntimeContext dependency and simplify context checking
* feat(docs): add raw conversation persistence to ReMe framework
- Add Weikang Zhou as a contributor in pyproject.toml
- Update README.md with new author in software citation
- Add new paper reference for AgentscopeReMe framework
- Include arXiv link and publication details
- Add full author list for the research paper
- Update bibliography with proper formatting
- Added QdrantVectorStore backend with native async operations
- Implemented advanced filtering capabilities for metadata queries
- Added support for Qdrant Cloud and local deployments- Updated vector store comparison table with Qdrant features
- Enhanced documentation with Qdrant setup and usage examples
- Fixed code block formatting in vector store API guide
- Updated embedding model integration for Qdrant compatibility
- Updated __version__ in reme_ai/__init__.py
- Updated project version in pyproject.toml- Changed flowllm dependency to include reme extra
- Fixed typo in README.md query example
- 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
- Change 'str' to 'string' for consistency in data types
- Rename 'list' to 'array' for JSON compatibility
- Update 'bool' to 'boolean' and 'int' to 'integer' for standardization
- Upgrade flowllm dependency from >=0.1.6 to >=0.1.7
- Update message variable from 'messages' to 'new_messages' in memory services
- Bump package version from 0.1.6 to 0.1.7
- Update FlowLLM dependency version from >=0.1.5 to >=0.1.6
- 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
- Update README content to reflect new project name and version
- Rename README_ZH.md to README.md
- Add contribution guide and update documentation links
- Correct author information and update project description
- Added detailed information about the project's architecture, installation, and usage
- Included sections on task memory and personal memory management
- Provided examples and use cases for the framework
- Updated project metadata and version information
- Add retrieve task memory flow to default.yaml for memory recall
- Update vector store initialization and import RecallVectorStoreOp- Modify app.py to use ConfigParser for configuration parsing
- Remove unnecessary comments and update flow description in default.yaml- Add test script for retrieve task memory using MCP client
- Update project metadata and dependencies in pyproject.toml
- 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
