* feat: add entry-point plugin system
* fix: harden plugin config and client loading
* docs(workflow): add detailed manual for publishing reme-auto-fin to PyPI
- Provide step-by-step instructions for updating project.version and merging branches
- Explain dependency verification for reme-ai on PyPI during build
- Specify requirements for GitHub Actions secret configuration and version uniqueness
- Describe manual workflow triggering and input of version number
- Recommend publishing order for related projects
- Clarify that only manual dispatch triggers publishing, no automatic triggers on push or tag
* feat: support plugin-defined component types
* refactor: simplify plugin configuration
* fix: isolate plugin loading and defer client fallback
* refactor: freeze built-in component registry
* fix: isolate config entry point loading
* fix: complete auto-fin package metadata
* fix(packaging): harden the Studio release workflow
* chore(daily-paper): tune scheduled discovery defaults
* fix(docs): link the ReMe blog to GitHub Pages
* fix(docs): increase Chinese hero title spacing
* refactor(docs): share hero title line spacing
* fix(docs): keep desktop hero copy on two lines
* fix(docs): widen the home hero description
* style(docs): loosen hero title line height
* fix(docs): hide Markdown frontmatter in rendered pages
* docs(readme): simplify installation and remove standalone ReMe Studio instructions
- Remove references to separate ReMe Studio package and static build steps
- Clarify that `core` extra includes common integrations including Studio
- Update installation instructions to use `pip install -e ".[core]"`
- Remove detailed Studio usage and frontend development instructions
- Note that Studio is included with `core` and optional via `web` extra
- Simplify Quick Start guide by removing Studio usage step
- Remove mentions of serving Studio with HTTP service when using extras
- Update both English and Chinese README files accordingly
* docs(readme): streamline and clarify memory design and operations
- Remove redundant explanations about core extra installation
- Simplify memory processing flow description for clarity
- Clarify memory workspace directory default and customization
- Condense automatic memory flow to emphasize rebuildable metadata
- Refine search functionality explanation with RRF fusion details
- Shorten and clarify agent integration description, removing redundancy
- Update and simplify the operations command list, removing less common commands
- Revise community and support section for conciseness and clarity
- Maintain parallel updates in both English and Chinese README files
* test(bump_version): add tests for version bumping and consistency checks
- Add dynamic loading of bump_version and package_studio scripts for testing
- Test that studio package and dependencies have matching versions
- Implement fixtures to write temporary version files for testing
- Add test ensuring bump_version updates all relevant files and dependencies
- Add test to reject inconsistent version sources before writing
- Refactor tests to use common REPOSITORY path variable
- Include imports and setup for pytest in test file
feat(bump_version): create script to update ReMe and Studio versions
- Implement version reading from __init__.py and pyproject.toml files
- Validate current versions are consistent across files before updating
- Update version strings atomically to avoid partial writes
- Ensure exact pinning of studio dependency in main package extras
- Validate new version format against a safe pattern
- Provide CLI interface to bump versions from command line
- Raise errors if expected version declarations or pins are missing or duplicated
* fix(release): validate split package publishing
* fix(release): improve validation diagnostics
* fix(release): sync docs and workflow inputs
* fix(release): split PyPI publish jobs
* feat: simplify local links and support line anchors
* fix: align line anchor tests with CI lint
* fix: preserve local links across file moves
* fix: encode markdown paths when rewriting links
* refactor(read): keep explicit line range parameters
* fix: simplify legacy link predicate compatibility
* docs: align local link behavior with implementation
* fix: skip unsupported markdown destination escapes
* fix: normalize workspace link paths across platforms
* fix: bound markdown link scanning
* fix: keep local link processing linear
* docs: clarify permissive markdown link parsing
* fix: handle local link processing failures
* refactor: limit file links to wikilink syntax
* docs: align wikilink contract with implementation
* fix: normalize dream and neighbor paths on Windows
* fix: resolve workspace path for neighbor expansion
* test(background_steps): add comprehensive tests for batch processing and memory management
- Add test for catalog upserts in batches of at most 100 files
- Add test for catalog deletes in batches of at most 100 paths
- Add test for index memory budget reducing batches to one file
- Add test for memory target limiting cumulative batch size
- Add test for invalid batch memory settings rejection
- Add test for continuing after one batch fails
- Add test for yielding to event loop while building batch
- Add test for modified file reusing unchanged embedding
- Add test for reporting memory estimation failure without aborting
feat(update_changes): implement bounded batch processing with memory management
- Add configurable batch parameters with default values
- Implement memory budget calculation based on available system memory
- Add file inspection and memory estimation before processing
- Implement batch flushing when limits are reached
- Add proper error handling for batch operations
- Support async yielding during batch building
- Add comprehensive validation for batch configuration parameters
- Implement memory estimation for indexing operations
- Add batch size limiting for delete operations
* test(steps): add tests for memory estimation failure handling
- Add test case for isolated file processing when memory estimation fails
- Add test case for proper release of flushed items before building next file
- Implement weak reference tracking to verify payload lifetime management
- Create parametrized tests for both source and item memory estimation methods
- Add assertions to verify single-item batch behavior on estimation failures
- Include comprehensive error handling verification for memory budget calculations
* chore(version): bump version to 0.4.1.3
- Update __version__ from 0.4.1.2 to 0.4.1.3 in __init__.py
* feat(index): support batch settings from environment
* refactor(index): use direct batch defaults
* refactor(index): configure memory estimates through step args
* ci: simplify Windows smoke dependencies
* feat(benchmark): add golden answer validation and session review for LongMemEval
- Introduce GoldenCheckStep to validate LongMemEval golden answers using structured verdicts
- Add SessionReviewStep to extract query/answer-relevant evidence from all sessions
- Implement concurrent session processing with configurable concurrency limits
- Create check_golden job configuration with lme_review and lme_judge agent wrappers
- Add Qwen3.7-plus model configuration for enhanced processing capabilities
- Include python_execute tool integration for agent-based reasoning and date validation
- Generate comprehensive JSON output with session summaries and validation verdicts
- Add run_check_golden.py script for batch processing across all LongMemEval samples
- Configure proper logging initialization with console and file output options
- Update component registry and file I/O modules to support new benchmark features
* feat(scripts): add script to summarize LongMemEval check_golden verdicts
- Parse check_golden.json files across all LongMemEval samples
- Calculate accuracy metrics for golden answers and session IDs
- Provide breakdown by question type with percentage calculations
- Add command line options for listing bad samples and JSON output
- Include progress tracking showing completed vs pending samples
- Display confidence scores and date sanity checks statistics
* refactor(benchmark): move golden check scripts to longmemeval directory
- Moved run_check_golden.py from scripts/ to benchmark/longmemeval/
- Moved stats_check_golden.py from scripts/ to benchmark/longmemeval/
- Updated path resolution to use parents[2] instead of parent.parent
- Added new --list-run-failed option to stats script
- Added logging directory constant and functions for tracking launched samples
- Enhanced stats output with launched count and run failure information
- Improved error reporting with run failure details and log file paths
* feat(benchmark): add LongMemEval agentic answer workflow with session extraction
- Add LmeAgenticAnswerStep, LmeAutoMemoryStep, and LmeExtractSessionStep to __init__.py
- Create shared helper render_with_source for displaying search results with session_id
- Implement agentic_answer step with vector_search, bm25_search, and extract_session_by_id tools
- Add auto_memory step to convert each session into search-friendly daily notes
- Create extract_session step to retrieve and analyze raw session content by session_id
- Update jinli_lme.yaml with auto_memory, vector_search, bm25_search, and agentic_answer jobs
- Configure lme_memory, lme_extract, and lme_agentic_answer agent wrappers
- Enhance search steps with include_source option to show session_id metadata
- Add proper session_id tracking and collision handling in daily note generation
* feat(benchmark): add LongMemEval agentic answer evaluation pipeline
- Add session_id tracking to agentic_answer.py result metadata
- Introduce run_agentic_answer.py driver for complete pipeline execution
- Implement auto_memory, update_index, and agentic_answer job orchestration
- Add concurrent execution with configurable limits and staggering
- Create aggregation script for collecting tool-call trails and results
- Add stats_agentic_answer.py for comprehensive result analysis
- Implement resume capability with existing output detection
- Generate aggregate.json with per-sample breakdown and tool call summaries
* feat(steps): add ClearPathsStep for cleaning workspace outputs before rebuild
- Introduce ClearPathsStep to remove stale workspace files/directories
- Add support for specifying paths and config_keys as targets to clear
- Implement safety checks to prevent deletion of files outside workspace
- Add logging for cleared paths and warnings for invalid paths
- Configure clear_paths_step in jinli_lme.yaml to clean daily_dir
- Add clear_paths_step to clean mem_answer.json before rebuilds
* feat(benchmark): add resume functionality to agentic answer runner
- Replace --force flag with --resume flag for controlling job execution
- By default every job reruns with clean rebuild behavior using config clear steps
- Add --resume option to skip samples whose output already exists and continue interrupted batches
- Update documentation to reflect new default clean rebuild behavior
- Modify job skipping logic to honor resume flag instead of force flag
- Update dry-run output to show correct todo jobs based on resume status
- Change default example command to use --resume for continuing interrupted runs
* feat(benchmark): generate JSONL output for check golden records
- Add write_check_golden_list function to create JSONL file
- Write all readable check_golden records as JSONL format
- Include check_golden_list path in stats output
- Display generated JSONL file path in summary report
- Maintain UTF-8 encoding with non-ASCII character support
* refactor(benchmark): rename answer judge step and integrate LME LLM judge
- Rename AnswerJudgeStep to LmeLlmJudgeStep and update imports
- Add new llm_judge configuration in jinli_lme.yaml
- Update run_agentic_answer.py to include llm_judge in pipeline
- Modify LmeLlmJudgeStep to read from query.json and answer.json
- Write LLM judgement results back to mem_answer.json
- Add command line options for start/end sample range selection
- Update aggregate.json generation to include LLM judgement data
- Add resume capability for llm_judge job based on judgement presence
* refactor(benchmark): rename answer judge step and integrate LME LLM judge
- Rename AnswerJudgeStep to LmeLlmJudgeStep and update imports
- Add new llm_judge configuration in jinli_lme.yaml
- Update run_agentic_answer.py to include llm_judge in pipeline
- Modify LmeLlmJudgeStep to read from query.json and answer.json
- Write LLM judgement results back to mem_answer.json
- Add command line options for start/end sample range selection
- Update aggregate.json generation to include LLM judgement data
- Add resume capability for llm_judge job based on judgement presence
* feat(steps): add wait_for_paths_step to block until workspace files exist
- Introduce WaitForPathsStep class that polls for required workspace-relative paths
- Add step registration with 'wait_for_paths_step' backend identifier
- Implement path validation to ensure targets are within workspace boundaries
- Add polling mechanism with configurable intervals via poll_seconds parameter
- Include logging functionality with log_every_seconds parameter for status updates
- Add metadata tracking of waited paths and duration in response object
- Register step in index module and expose in public API
- Configure step in jinli_lme.yaml to wait for session_review.json before golden check
- Add script rename from run_check_golden.py to run_golden_check.py with enhanced options
* feat(benchmark): enhance longmemeval benchmarking with concurrency and progress tracking
- Add benchmark extra dependency group with portalocker requirement
- Introduce concurrent execution support for golden_check and session_review workflows
- Add progress reporting interval option with real-time status updates
- Implement global throttling mechanism for session review requests using file locks
- Enhance golden check validation with current schema verification
- Add active task tracking and graceful shutdown handling
- Rename check_golden scripts to golden_check for consistency
- Update statistics reporting with correct/incorrect terminology instead of reasonable
- Add stale format detection and compatibility handling for verdict fields
- Include both_correct rate calculation in accuracy metrics
- Add concurrency and staggering options for better resource management
* ci(workflow): add Windows smoke test workflow
- Create new workflow file .github/workflows/windows-smoke.yml
- Configure workflow to trigger on push and pull request events
- Set up Python environment with version 3.11
- Install package dependencies using pip
- Run version job as smoke test for CLI functionality
- Enable concurrency control to prevent duplicate runs
- Use matrix strategy for Python version testing
* feat(benchmark): add retry mechanism and health check for session review
- Added retry configuration options (retry_initial_seconds, retry_max_seconds, retry_max_attempts) to jinli_lme.yaml
- Implemented exponential backoff retry logic with configurable parameters in session_review step
- Added output_is_healthy function to verify session_review.json integrity and absence of failed reviews
- Updated resume functionality to skip only healthy outputs instead of all existing files
- Integrated JSON parsing and validation to check for failed reviews in output files
- Enhanced error handling and logging for retry attempts and recovery scenarios
* feat(benchmark): add LongMemEval session review statistics script
- Create stats_session_review.py to summarize session_review.json artifacts
- Add command line options for listing failed, missing, and run failed samples
- Implement JSON output mode for programmatic consumption
- Calculate and display health statistics including total samples, healthy outputs, failed sessions
- Provide detailed failure information with session IDs and error messages
- Generate re-run commands for samples with failed reviews
- Add percentage calculations for better statistical overview
- Include support for multiple output formats and detailed logging
* feat(benchmark): add LongMemEval output cleanup script and enhance golden check retry logic
- Added clean_sample_outputs.py script to remove generated LongMemEval files while preserving source inputs
- Implemented configurable retry mechanism in golden_check.py with exponential backoff strategy
- Added retry parameters (initial/max seconds and max attempts) to control failure recovery behavior
- Integrated asyncio support for asynchronous sleep during retry intervals
- Configured default retry settings in jinli_lme.yaml with 5s initial and 300s maximum intervals
- Preserved core files (query.json, answer.json, session/) while cleaning generated artifacts
* feat(benchmark): add AppleDouble file cleanup to sample output cleaner
- Remove AppleDouble files starting with '._' recursively including under session/
- Add is_under helper function to check if path is inside parent directory
- Track targets in set to avoid duplicate processing
- Include AppleDouble files in cleanup targets when not already covered by existing targets
- Maintain dry-run mode as default behavior with --apply flag for actual deletion
* refactor(benchmark): update LongMemEval sample output cleaning script
- Add time and Iterator imports for enhanced functionality
- Add --progress-every argument to control progress reporting frequency
- Replace is_under function with iter_sample_targets generator
- Implement detailed progress tracking with timing measurements
- Add sample-by-sample processing with elapsed time reporting
- Include AppleDouble file detection within session directory
- Update target counting and deletion statistics display
- Add conditional progress updates based on progress-every setting
- Improve dry-run mode with would-delete indication
* chore(benchmark): increase initial interval for session review step
- Changed START_INTERVAL_SECONDS from 1.0 to 3.0 seconds
- Adjusted timing parameters for better benchmark stability
* refactor(benchmark): implement coordinated retry mechanism for session reviews
- Add retry gate condition to coordinate concurrent review attempts
- Implement wait_for_healthy_start_slot to handle sequential retries
- Create mark_retrying and mark_recovered functions to track retry states
- Update reply_with_retry to accept index parameter for coordination
- Add has_prior_retry logic to prevent race conditions during recovery
- Ensure proper cleanup of retry state on success or failure
- Maintain backward compatibility while adding coordination features
* chore(benchmark): adjust session review start interval timeout
- Changed START_INTERVAL_SECONDS from 3.0 to 5.0 seconds
- Increased initial delay for session review benchmark step
- Updated timeout configuration for improved stability
* refactor(benchmark): update session review concurrency and throttling mechanism
- Replace global throttle with per-process concurrency control
- Add concurrency parameter with default value of 30 in config
- Add start_interval_seconds parameter with default value of 2 seconds
- Change default concurrency from 3 to 1 in command line interface
- Update documentation to reflect new throttling behavior
- Implement semaphore-based concurrency limiting for review tasks
- Modify retry mechanism to use local locking instead of global files
- Remove portalocker dependency for cross-process throttling
* refactor(config): update session review configuration and concurrency settings
- Removed deprecated retry configuration parameters from jinli_lme.yaml
- Increased MAX_CONCURRENCY from 30 to 60 in session_review.py
- Reduced START_INTERVAL_SECONDS from 2.0 to 1.0 in session_review.py
- Cleaned up redundant backend specifications in configuration file
- Simplified agent wrapper configurations by removing obsolete retry settings
* feat(benchmark): enhance LME auto memory step with advanced scheduling and error handling
- Add datetime parsing functionality for LongMemEval timestamps with regex pattern
- Implement configurable concurrency limits with MAX_CONCURRENCY of 60
- Introduce retry mechanism with exponential backoff for agent interactions
- Add session filtering based on date comparison with question_date validation
- Create rate limiting with start interval control between requests
- Implement sophisticated retry coordination using asyncio conditions
- Add comprehensive error tracking for failed and filtered session extracts
- Remove deprecated concurrency parameter from jinli_lme.yaml configuration
- Add structured output validation in session review step
- Include detailed metadata reporting with session statistics and errors
* fix(benchmark): adjust default concurrency for auto_memory job
- Changed default concurrency from 3 to 1 for auto_memory job to prevent API overload
- Updated help text to reflect new default value of 1 for concurrency parameter
- Modified documentation to clarify concurrency behavior varies by job type
* refactor(search): replace hardcoded candidate multiplier with constant
- Introduced _CANDIDATE_MULTIPLIER constant set to 10
- Replaced hardcoded factor of 5 with _CANDIDATE_MULTIPLIER in BM25 search
- Replaced hardcoded factor of 5 with _CANDIDATE_MULTIPLIER in vector search
- Updated test to verify both search steps use ten times limit for candidates
- Imported VectorSearchStep and Bm25SearchStep in test module
- Added comprehensive test case for candidate count calculation logic
* feat(lme): add data inspection error handling with fallback mechanism
- Implemented non-retryable data inspection error markers detection
- Added _is_data_inspection_error method to identify inspection failures
- Created fallback handling for data inspection errors in auto memory extraction
- Added fallback handling for data inspection errors in session review
- Extended failed extracts tracking with non-retryable and fallback flags
- Separated fallback extracts from regular failed extracts in reporting
- Enhanced error logging with specific data inspection failure messages
- Updated metrics to track fallback extractions and reviews separately
- Maintained existing retry logic for other exception types
* feat(benchmark): enhance session review statistics with fallback tracking
- Add support for identifying and listing non-retryable fallback reviews
- Introduce --list-fallback argument to display fallback review details
- Separate retryable failures from non-retryable fallbacks in reporting
- Track fallback samples and sessions separately from failed ones
- Update console output to show both retryable and non-retryable categories
- Include fallback details in JSON output with reasons and session info
- Modify failure counting logic to distinguish between retryable and fallback reviews
* feat(benchmark): add question_id tracking and enhanced fallback reporting
- Add question_id function to extract query.question_id from data
- Initialize question_id_by_id dictionary to store question IDs by index
- Store question_id for each sample during data processing
- Enhance fallback output to include question IDs and session information
- Format sample labels with question IDs when available
- Display session IDs associated with each fallback case
* feat(benchmark): add question_id support and improve bad sample reporting
- Add question_id_for function to extract question_id from multiple sources
- Add sample_label function to format samples as idx(question_id) when available
- Store question_id in data dictionary during processing
- Change bad_golden and bad_sessions to store full records instead of just indices
- Update list_bad output to show formatted labels with question_id information
- Improve error reporting with more detailed sample identification
* feat(benchmark): enhance golden check stats with structured output
- Add related_session_ids function to extract session IDs from verdict records
- Create grouped_records function to group records by question type
- Replace flat list output with JSON-formatted grouped records in list_bad option
- Replace flat list output with JSON-formatted grouped records in list_bad_sessions option
- Maintain Chinese labels while adding structured data presentation
- Improve readability of bad verdict record display with hierarchical grouping
* feat(benchmark): update data structure for question indexing
- Replace sample_label with _idx field for index tracking
- Add question_id field to store _question_id values
- Maintain backward compatibility with empty string defaults
- Preserve existing session_id functionality
- Update data mapping to include new fields in grouped results
* refactor(benchmark): streamline golden answer verification process
- Replace relevance filtering with comprehensive information extraction
- Remove is_relevant field and simplify session summary structure
- Change relevant_info to extracted_info for clarity
- Update golden check logic to work with full extractions instead of filtered summaries
- Simplify prompt instructions to focus on complete information extraction
- Remove redundant schema validation and structured output requirements
- Adjust statistics calculation to match new extraction approach
- Update metadata field names to reflect extraction rather than relevance checking
* feat(benchmark): add selective file deletion option to clean_sample_outputs
- Add --filename argument to delete only specific root-level files
- Modify iter_sample_targets function to accept optional filenames filter
- Implement validation for root-level filename constraints
- Update function calls to pass filenames parameter
- Add example usage for selective file deletion in documentation
* feat(benchmark): add error count metrics to golden check statistics
- Added golden_bad, session_bad, and both_bad calculation fields
- Updated console output format to include error counts per question type
- Modified table display to show both accuracy rates and error numbers
- Enhanced statistical summary with additional error breakdown metrics
* test(search): update search step tests with include_source parameter
- Added include_source=False parameter to VectorSearchStep initialization
- Added include_source=False parameter to Bm25SearchStep initialization
- Maintained existing RuntimeContext parameters for both search steps
- Updated test calls to match new constructor signature with include_source option
* chore(workflow): update package installation to include core extra dependencies
- Modified pre-commit workflow to install with [dev,core] extras
- Updated python-publish workflow to install wheel with core extra dependency
- Changed from direct dist/*.whl install to variable assignment for wheel path
- Ensured core dependencies are included during test installation phase
* chore(workflow): remove docs deployment workflow
- Delete the entire docs.yml workflow file that was used for deploying documentation
- Remove all related configuration including build and deploy jobs
- Stop automatic deployment of docs on pushes to main branch
- Remove GitHub Actions workflow for docs/ directory changes
* refactor(search): replace time module with datetime for timestamp generation
- Removed unused time import
- Added static method _now_ts using datetime.timestamp
- Updated clock parameter to use _now_ts method instead of time.time
- Maintained same timestamp precision and functionality
* test(http): add tests for HTTP client display formatting
- Add test for default metadata hiding behavior in CLI output
- Add test for metadata display when show_metadata is enabled
- Verify _format_for_display method correctly formats response text
- Test both success case and metadata inclusion scenarios
* chore(build): remove longmemeval from gitignore
- Removed longmemeval directory from gitignore list
- Kept evaluation and datasets directories in ignore list
- Updated gitignore configuration for proper version control
* refactor(embedding): replace embedding model with embedding store architecture
- Remove as_token_counter component and its estimated token counter implementation
- Replace BaseEmbeddingModel with BaseEmbedding that wraps AgentScope embedding models
- Add support for multiple embedding providers (OpenAI, DashScope, Gemini, Ollama)
- Introduce BaseEmbeddingStore and LocalEmbeddingStore for caching and persistence
- Update component registry to use new embedding and embedding_store types
- Modify file stores to use embedding_store instead of embedding_model
- Update health check to monitor embedding_store instead of embedding_model
- Change default config to use embedding_store with local backend
- Add estimate_token_count utility function to utils module
* refactor(llm): replace as_llm components with unified llm implementation
- Remove deprecated as_llm and as_llm_formatter modules
- Add new llm module with BaseLLM and provider-specific implementations
- Update component registry to use LLM instead of AS_LLM
- Replace all as_llm/as_llm_formatter references with llm in steps
- Update configuration schema to use llm instead of as_llm
- Rename integration test file from test_as_llm to test_llm
- Add proper docstrings to embedding store dimension property
- Add pylint disable comment for embedding model call
- Remove unused FormatterBase import in base_step
- Update token_utils with function docstring
* refactor(evolve): replace ReActAgent with Agent and update message handling
- Removed FlexReActAgent class and direct ReActAgent imports
- Updated Agent instantiation to use new constructor parameters
- Changed message content to use TextBlock format instead of plain strings
- Modified timestamp access from msg.timestamp to msg.created_at
- Updated metadata access pattern for structured outputs
- Replaced Msg.from_dict with Msg.model_validate in auto_memory.py
- Updated test mocks to patch Agent instead of ReActAgent
- Changed message serialization from to_dict to model_dump in tests
- Moved component references to base class definition
- Updated demo tools to return strings instead of ToolResponse objects
* feat(step): migrate to FunctionTool and add streaming support
- Replace deprecated ToolResponse with FunctionTool in base_step.py
- Remove unused TextBlock import from base_step.py
- Update job registration to use new FunctionTool API
- Add thinking_budget parameter to llm_demo configuration
- Introduce StreamLLMDemoStep with streaming output capability
- Add structured output support to LLMDemoStep via generate_structured_output
- Implement streaming event handling for text/thinking/tool calls
- Add integration tests for embedding functionality
- Add integration tests for structured output and streaming features
- Update tool usage in demo steps to use new function naming convention
* fix(ci): correct package installation path in unittest workflow
- Updated pip install command to use proper package path "./reme4[dev,core]"
- Fixed dependency installation step in CI workflow configuration
* chore(workflow): update python versions in unittest workflow
- Remove Python 3.10 from test matrix
- Add Python 3.11 to test matrix
- Add Python 3.12 to test matrix
- Keep Python 3.13 in test matrix
- Update matrix configuration for better version coverage
* fix(health): handle missing dimensions attribute in embedding status
- Wrap dimensions access in try-except to prevent AttributeError
- Return None when dimensions attribute is not available
- Maintain backward compatibility for components without dimensions
test(component): add comprehensive tests for BaseComponent and related classes
- Add tests for Dependency class including repr and attribute access
- Add tests for bind method with various scenarios and edge cases
- Add tests for lifecycle management and async context handling
- Add tests for standalone and context-bound dependency resolution
- Add tests for ComponentMixin path utilities
test(common): update LocalFileStore initialization parameter
- Change embedding_model parameter to embedding_store in test setup
- Update all affected test files consistently
test(registry): add complete test suite for ComponentRegistry
- Add tests for register method with explicit names and defaults
- Add tests for decorator registration pattern
- Add tests for get_all method returning copies
- Add tests for unregister and clear operations
- Add tests for error handling of invalid registrations
test(job): add comprehensive tests for BaseJob and BackgroundJob
- Add tests for step resolution and exception handling
- Add tests for backoff delay calculation with jitter
- Add tests for supervisor loop restart behavior
- Add tests for task shutdown and cancellation
test(prompt): add complete test suite for PromptHandler
- Add tests for prompt loading from dictionaries and files
- Add tests for internationalization and language fallback
- Add tests for flag filtering and variable substitution
- Add tests for format validation and error handling
test(runtime): add basic tests for RuntimeContext dictionary access
- Add tests for item getting, setting and containment checks
- Add tests for missing key error handling
* feat(evolve): add permission context and agent state management
- Import PermissionContext, PermissionMode and AgentState modules
- Add state configuration with bypass permission mode to AutoDream agents
- Add state configuration with bypass permission mode to AutoMemory agents
- Implement static _to_msg method for message validation and formatting
- Refactor message processing to use the new _to_msg method
- Ensure proper content structure for text blocks in message conversion
* style(tests): update test files with linting rules and code improvements
- Add missing pylint disable directives for docstring and attribute warnings
- Replace lambda expressions with proper function definitions in test cases
- Import Path directly instead of using lambda with __import__
- Simplify assertion checks by using truthiness instead of equality to empty dict
- Remove unused imports and reorder imports consistently
- Format dictionary literals with proper indentation and line breaks
* refactor(steps): update naming conventions in components and configuration
Updated naming conventions across multiple files, changing colon-separated names to underscore-separated format, and added new step definitions along with documentation updates.
Key changes:
- Replaced `Synchronizer` with `AutoMemory` as the counterpart component for cold-write operations
- Updated naming conventions in all related configuration files (e.g., `frontmatter:read` → `frontmatter_read`)
- Added new step definitions such as `submit_slug_updates` and `auto_memory`
- Updated relevant documentation
- Modified log output format for improved readability
* refactor(evolve): Refactor the auto-memory module and update related configurations
- Remove the old slug update commit step file
- Add new auto-memory planner and writer steps
- Update __init__.py to export the new step classes
- Modify the auto_memory configuration structure in default.yaml
- Update the slug field description for clearer explanation of its purpose
* up
* up
* refactor(tests): Move unit test directory from `tests4/unittest` to `tests4/unit`
Additionally, the assertion logic in test files has been updated: direct comparisons of `payload["notes"]` have been replaced with checks verifying the presence of paths and metadata within the response content. Furthermore, some test expectations have been simplified—for example, using `count` instead of asserting against specific note lists.
Specific changes include:
- Updating workflow configurations to align with the new test directory structure
- Modifying assertions across multiple test methods to make them more flexible and maintainable
- Cleaning up and optimizing parts of the test code structure
This is a comprehensive test refactoring effort aimed at improving test readability and robustness.
* Refactor(steps): Update memory writing logic and optimize JSON schema structure
Improved the write strategy description in `auto_memory_writer.yaml` to emphasize using `edit` over `write`.
Adjusted the `json_schema` structure in `base_step.py` to support the new function definition format.
Also corrected grammatical issues in the related documentation.
* Fix: Improve frontend data parsing error handling and update test files
Added capture and handling logic for YAML parsing exceptions, providing more detailed error messages when frontend data format issues occur. Also corrected the description text in a test file.
