* feat(daily-paper): add Hugging Face mirror switch
* refactor(daily-paper): simplify the HF mirror switch and warn on ignored env
The switch was a three-state bool|None where None preserved the legacy
environment-driven selection, but no production caller ever passes None --
collect.py always resolves an explicit bool. Collapse it to a plain bool
defaulting to False.
HF_MIRROR_URL no longer redirects traffic on its own, so warn when it is
configured while the mirror stays disabled; a mirror-only setup would
otherwise fall back to the official site with no signal. Both READMEs now
record the behavior change and stop presenting the two mirror variables as
symmetric -- arXiv remains environment-driven while Hugging Face is gated on
the job parameter.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(daily-paper): address mirror configuration feedback
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* refactor: rebuild auto-fin and daily-paper cookbooks on structured-output agents
Rework the auto-fin and daily-paper cookbooks to run on structured-output
LLM agents instead of Claude Code agent wrappers, replace the SSH proxy with
data-source mirrors, and rewrite the affected unit tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* refactor(auto_fin): unify JSON output serialization and writing
- Extracted _write_output static method to serialize and write Pydantic models as compact JSON
- Replaced inline JSON dump and write calls with _write_output usage across auto_fin steps
- Added _report_path and _current_report for managing intra-day reports in AutoFinMergeStep
- Updated auto_fin merge step to write output via new _write_output method
- Enhanced news reading with caching in AutoFinHistoryStep
- Refined returns calculation to handle events before close on non-trading days correctly
feat(daily_paper): improve note path resolution and metadata handling
- Introduced iter_note_metadata generator for safe Markdown frontmatter iteration
- Added resolve_unique_note_path to avoid note filename conflicts on disk and in used titles
- Updated analyze, collect, digest, and select steps to use centralized constants and helpers
- Used utc_now_iso for consistent timestamping in metadata
- Replaced direct frontmatter loads with iter_note_metadata in collect and analyze steps
- Replaced hardcoded paper selection count with PAPER_COUNT constant in all relevant places
- Added _MAX_SELECT_ATTEMPTS constant in select step for attempt management
- Improved error messages for filename validation in daily paper title normalization
feat(auto_fin): add multi-run cron schedules for intraday refinement
- Defined three auto_fin cron jobs at 09:30, 11:30, and 18:00 Shanghai time for gradual report updates
- Each intraday run adds evidence cumulatively instead of replacing prior output wholly
- Updated daily_cookbook.yaml to register new cron schedules and remove legacy 12:00 cron
refactor(auto_fin_data): clean ETF code handling and page limits
- Replaced hardcoded DEFAULT_ETF_CODES with required non-empty config value "etf_codes"
- Added constants for major news and fund page limits to control pagination
- Improved ETF name extraction logic to handle missing fields consistently
fix(auto_fin_merge): fix report retrieval and merging logic
- Added support for getting current intra-day report in addition to previous day's report
- Modified merge template to include prior and current report sections for better context
- Adjusted report path handling to consistently use Path objects
test(auto_fin): add coverage for returns calculation and report retrieval
- Added test for returns when event occurs before close on non-trading day, checking next session entry
- Added test for previous and current report retrieval feeding merge context with disk files
- Extended test asserts for auto_fin cron schedule changes in config
style(daily_paper): reorder and cleanup imports
- Reorganized imports in _common.py for clarity and added missing collections.abc.Iterator import
- Cleaned up commented and unused imports across daily_paper steps
* feat: add configurable upstream mirror proxy
* style: format auto-fin data step
* fix: align cookbook mirrors and contracts
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* feat: add ssh proxy
* feat: add ssh proxy
* feat: add ssh proxy
* feat: add ssh proxy
* feat: add prompt
* feat: add agent wrapper
* feat: add agent wrapper
* feat: add agent wrapper
* feat: add tushare skill
* feat: add tushare skill
* feat: add tushare skill
* feat: add none stream
* chore(deps): update dependency versions in pyproject.toml
- Bump claude-agent-sdk from 0.2.123 to 0.2.126
- Upgrade pre-commit to version 4.6.1 or higher
- Upgrade pytest to version 9.1.1 or higher
* feat(agent_wrapper): add session compaction support and unify session commands
- Introduce compact_session method to BaseAgentWrapper and implement it in AsAgentWrapper, CcAgentWrapper, and CodexAgentWrapper
- Add session_command module with SessionCommandResult dataclass and handle_session_command function for /clear and /compact commands
- Update __init__.py exports to include session_command handlers
- Modify DingTalkWaitStep to handle session commands via handle_session_command function
- Remove streaming mode from DingTalkWaitStep and simplify reply handling to final Markdown replies only
- Add unit tests for session compaction methods and session command handling across wrappers and DingTalk integration
- Clean up and remove obsolete streaming and card rendering code from DingTalk wait step
- Adjust daily_cookbook.yaml to remove stream and card_update_interval config entries for DingTalk wait step
* feat(auto_fin): add Auto Fin simulated portfolio cookbook workflow
- Add comprehensive Auto Fin schema exports for multiple models and enums
- Implement base class and helpers for Auto Fin analysis steps
- Create file, state, and formatting utilities for Auto Fin with atomic file writes and locking
- Define Auto Fin pipeline with four analysis agents: backtest, event, portfolio, and US correlation
- Register Auto Fin package in cookbook workflows and schema initialization
- Add detailed documentation in markdown describing the system design, workflow, and data contracts
* feat(outbound_proxy): add application-scoped outbound HTTP proxy components
- Introduce BaseOutboundProxy and OutboundProxyEndpoint as core contracts
- Implement FixedHttpOutboundProxy for external HTTP proxy integration
- Add SshHttpOutboundProxy providing SSH-backed local HTTP proxy tunnels
- Register outbound proxy components in component registry and enumeration
- Update components package to include outbound_proxy module
- Add dependency on pproxy for SSH HTTP proxy bridging
- Include comprehensive unit tests covering proxy lifecycle, validation,
environment merging, error handling, readiness, and monitoring mechanisms
* refactor(network): replace SSH proxy with explicit HTTP outbound proxy
- Remove SSH proxy helper implementation and references in codebase
- Add support for explicit HTTP proxy URL in arXiv and HuggingFace clients
- Modify clients to use async context manager for consistent resource handling
- Update daily paper steps to forward outbound proxy configuration explicitly
- Change tests to cover new proxy usage model and remove SSH proxy mocks
- Add outbound proxy component configuration in daily_cookbook.yaml
- Ensure proxy URL usage disables environment trust in HTTP clients
- Fix app context component enum access to be defensive against missing keys
* feat(agent_wrapper): add managed proxy support for command environments
- Introduce BaseOutboundProxy binding in BaseAgentWrapper for outbound proxy management
- Add bash_environment and command_proxy_environment properties to apply proxy settings
- Update WorkspaceBackend instantiation in AsAgentWrapper to use bash_environment
- Inject managed proxy export commands into Claude Code Bash commands via hooks
- Enhance CodexAgentWrapper to include managed proxy in shell environment policy
- Modify daily_cookbook.yaml steps to specify outbound_proxy as default where needed
- Add comprehensive unit tests verifying managed proxy injection and environment isolation
- Ensure subprocess_environment remains unchanged while proxy is applied selectively to commands
* refactor(memory): replace search job_tools with memory in daily cookbook config
- Change workspace_dir default from .reme to reme_workspace
- Replace search job_tools with memory across multiple components and jobs
- Update descriptions to reflect long-term memory retrieval instead of search
- Modify system prompts to instruct using memory for retrieving notes
- Adjust unit tests to verify memory job_tools and job presence instead of search
- Ensure consistency in configuration and tests for memory backend usage
* refactor(config): rename memory to memory_search in daily cookbook config
- Change all occurrences of "memory" to "memory_search" in job_tools and job definitions
- Update related system prompts to reflect the new memory_search terminology
- Modify unit tests to assert the presence of memory_search instead of memory
- Ensure consistency across skills, job tools, and backend configurations in multiple components
* feat(auto_fin): add deterministic quantitative research and ranking fusion
- Introduce new schema models: EtfScore, RankingMetrics, ExtremeAnalysis,
DimensionRanking, and FusionRanking to represent deterministic research outputs
- Add ranking data to event, backtest, us_correlation, and portfolio analysis outputs
- Implement ranking_section renderer to format Top20 scores and diagnostics in Markdown
- Develop AutoFinQuantStep for deterministic ETF ranking using TuShare data, Polars,
and a custom extremely randomized tree ensemble
- Integrate quantitative rankings into backtest and portfolio analysis steps and reports
- Extend auto_fin pipeline with new quant_enabled and quant_required config options
- Enforce ranking constraints like unique codes, contiguous ranks, and normalized fusion weights
- Update analysis YAMLs with rules limiting data freshness, universe, and ranking usage
- Incorporate ranking outputs into all major markdown report bodies in Auto Fin pipeline
- Add concurrency-limited asynchronous TuShare client to fetch required market data
- Introduce cross-sectional rank correlation and NDCG metrics for ranking quality evaluation
* feat(auto_fin): implement stage-wise notification and reporting for analysis pipeline
- Refactor notification config in daily_cookbook.yaml to support dispatch steps
- Update AutoFinNotificationStep to deduplicate notifications per run stage
- Add _notify_stage method in pipeline to send notifications for each analysis stage
- Implement persistence and notification for event, backtest, US correlation, and portfolio stages
- Modify pipeline flow to persist reports and notify after each stage completion
- Adjust metadata to track notifications and errors per stage
- Update tests to verify stage-wise notification sending and deduplication
- Remove older combined report persistence in favor of modular stage handling
* feat(auto_fin): add outbound proxy support for Tushare API usage
- Introduce BaseOutboundProxy reference in AutoFinPipelineStep and AutoFinQuantStep
- Update TushareResearchClient and trade calendar fetch to accept and use proxy URL
- Create _ProxiedTushareApi adapter to route Tushare requests via explicit HTTP proxy
- Modify create_tushare_api utility to optionally return proxied API client
- Add unit tests covering proxy forwarding and client behavior with managed proxies
- Ensure proxy usage respects explicit proxy URL over environment fallback
- Integrate outbound proxy into data fetching and quantitative research steps
* feat(auto_fin): enforce checkpoint time validation and add state models
- Introduce AnalysisState base class and specific states for event, backtest, and US correlation analyses
- Replace analysis output types with corresponding state classes in run schemas
- Add require_checkpoint_reached method to validate decision_at/data_cutoff against current time
- Enforce checkpoint time checks before analysis steps in event, backtest, portfolio, and quant analyses
- Refactor quant data loading to include adjustment factors and apply price adjustments without fallback
- Update analysis YAML docs to require real-time checkpoint validation and forbid using future data
- Improve portfolio run serialization by excluding redundant legacy fields and nested proposed actions
- Add helper to extract readable sections from persisted checkpoint documents
- Fix event analysis output validation to reject events and sources with future timestamps
* feat(auto_fin): auto-select latest reached checkpoint if none specified
- Extend checkpoint config to accept empty string for auto selection
- Add static method to compute latest checkpoint reached by current time
- Modify pipeline step to auto-select checkpoint based on trade calendar and time
- Adjust force flag default depending on whether checkpoint is explicit or auto
- Log details when checkpoint is auto-selected to improve observability
- Add comprehensive tests for auto checkpoint selection logic and edge cases
- Remove deprecated default and required constraints from force parameter in config
* refactor(auto_fin): unify datetime comparison with compare_datetimes utility
- Replace direct datetime comparisons with compare_datetimes function calls
- Use cmp_to_key with compare_datetimes for sorting datetime tuples and lists
- Update validation logic in backtest, event, analysis, and ledger modules for consistent datetime handling
- Add unit tests to verify handling of naive and aware datetime comparisons in event and backtest validations
- Ensure marked_at and interval_end timestamps are set and compared consistently using compare_datetimes
- Improve correctness of ordering and conditional checks related to timestamps throughout auto_fin steps and ledger code
* feat(auto_fin): add datetime comparison helper for mixed timezone data
- Implement compare_datetimes function to handle naive and aware datetimes
- Ensure naive datetime is interpreted in the known timezone of the counterpart
- Facilitate comparisons between legacy and timezone-aware Auto Fin data
- Add module docstring explaining purpose of the helpers
* docs(auto_fin): enforce unique ETF representative per sub-theme in analysis rules
- Update backtest.yaml to recommend or highlight only one ETF per sub-theme for ETF analyses
- Modify event.yaml to map only one representative ETF per sub-theme, avoiding duplicate recommendations
- Revise portfolio.yaml to restrict holdings/buys to a single ETF per sub-theme, preventing repeated buys of highly overlapping ETFs
- Adjust us_correlation.yaml to retain only one representative A-share ETF per sub-theme for mapping or recommendation
- Add test to verify presence of new sub-theme uniqueness guidance in step prompts
* feat(auto_fin): separate draft model and include deterministic fusion ranking
- Introduce _PortfolioProposalDraft pydantic model for agent-authored fields before ranking
- Discard any "fusion_ranking" data from draft to prevent conflicts with canonical ranking
- Modify AutoFinPortfolioStep to receive draft, enrich with fusion_ranking, and produce final output
- Update tests to use _PortfolioProposalDraft and validate deterministic fusion ranking propagation
- Add async test verifying fusion ranking is correctly set in portfolio output with no errors
* refactor(auto_fin): rewrite and simplify Auto Fin schema and steps
- Remove legacy Auto Fin analysis step modules and helpers
- Replace complex ranking and portfolio models with simplified current-news models
- Update schema to focus on news-case workflow with new domain models
- Remove A-share decision checkpoints and backtest details from schema
- Simplify recommendation and decision output structures
- Clean up deprecated state and utility functions
- Update Auto Fin steps initialization to new pipeline steps only
- Improve uniqueness validation for themes and ETFs in research plan
* feat(auto_fin): implement full local cache and analysis workflow for Auto Fin
- Add AutoFinDataStep to prepare and cache daily TuShare data with lookback
- Add AutoFinAnalysisStep to analyze cached data and generate Markdown report
- Implement detailed time window, ETF filtering, and historical case validation
- Introduce YAML prompts for planning and decision-making steps
- Update .gitignore to include reme_workspace/
- Clean up config and import structure for auto_fin steps
- Remove old pipeline.py and consolidate functionality into new modules
- Use polars for efficient CSV reading and data processing
- Ensure atomic writes and strict JSON serialization for cache files
- Enforce rules on news timing, ETF universe, and historical case usage
* fix(auto_fin): restrict news data source to '财联社' in analysis and cache
- Update analysis templates to specify current news as from '财联社' only
- Modify news fetching functions to filter by source '财联社'
- Add validation method to check cached news source correctness
- Update news caching logic to exclude non-'财联社' news
- Enhance unit tests with multiple sources to ensure filtering works
- Confirm news API calls include source filter parameter as '财联社'
* refactor(auto_fin): convert I/O methods to asynchronous implementations
- Change _news, _dataset, and _theme_data methods to async for improved concurrency
- Move JSONL and CSV reading operations to asynchronous wrappers using asyncio.to_thread
- Remove synchronous _read_jsonl and _read_csv functions, integrate them as static async class methods
- Update cache validation methods to async, awaiting I/O operations accordingly
- Adjust usage of dataset and news retrieval in analysis step to await asynchronous methods
- Add async unit test to validate JSONL reading with unicode line separators
- Preserve existing functionality while enabling non-blocking file and data access
* fix(nx_file_graph): defer networkx import and improve dependency handling
- Move networkx import inside NxFileGraph constructor for lazy loading
- Raise ImportError with original exception context if networkx is missing
- Remove module-level fallback assignment of nx to None
- Expand test to block loading of multiple optional core dependencies eagerly
- Change exception type in test from ModuleNotFoundError to AssertionError
- Update test comments to reflect broader optional dependency checks
* feat(embedding_store): add quota retry delay mechanism for embedding requests
- Introduce quota_retry_delay parameter to configure wait time before retry on quota exhaustion
- Implement detection of insufficient quota errors in LocalEmbeddingStore without external SDK
- Add retry logic with custom delay when quota is insufficient during embedding requests
- Update configuration to set max_retries and quota_retry_delay defaults for embedding store
- Add unit tests covering quota exhaustion retry behavior with delay and opt-in control
- Ensure existing retry behavior remains unchanged if quota_retry_delay is not set
* feat(auto_fin): add detailed logging to analysis and data fetching steps
- Add _preview static method for bounded diagnostic output in analysis.py
- Log prompt start, completion, errors, and validation details in _reply method
- Add info logs for major processing steps in execute method of analysis.py
- Add debug and info logs for cache validation, data fetching, and pagination in data.py
- Log conditions for skipping reports and cache plans in data.py execute method
- Log download summaries and cache writes for news and ETF data
- Improve error logging with exception details in cache validation functions
- Ensure all logs include context such as record counts, paths, and parameters
* refactor(auto_fin): overhaul Auto Fin workflow and schema contracts
- Replace old Auto Fin schema models with comprehensive new data classes
- Remove legacy Auto Fin analysis step in favor of modular agent-based steps
- Introduce AutoFinAgentStep for validating structured agent replies
- Simplify data cleaning and JSONL writing utilities for news cache
- Remove synchronous and asynchronous dataset methods from analysis step
- Redefine Auto Fin analysis configuration for 360-day news retention and multi-step pipeline
- Remove embedded analysis prompt templates and replace with agent-driven logic
- Update __init__.py exports to match new step implementations and remove deprecated classes
- Improve error handling and validation in agent step reply processing
- Clean up redundant imports and unused code in analysis and data preparation modules
* feat(auto_fin): add detailed logging for analysis and data processing steps
- Add timing logs to measure agent prompt processing duration in analysis.py
- Log news cache hits and news write paths with record counts in data.py
- Include detailed info logs for news download start and completion in data.py
- Add start, progress, and completion logs with topic and event counts in history.py
- Log start and completion of merge step including path and ETF count in merge.py
- Add start and done logs with window and news counts in topic.py
* feat(auto_fin): enhance schema and steps with detailed ETF and event modeling
- Replace and add multiple AutoFin schema classes to support detailed ETF selection,
historical research, market analysis, forecast models, and report output with validation
- Implement Shanghai timezone normalization and strict validation in schema models
- Remove deprecated AutoFin analysis agent step and consolidate reply handling in base step
- Introduce AutoFinStep base class with shared helpers for prompt handling, data fetching,
logging, and JSONL file operations
- Add AutoFinDataStep to manage daily news data complete with schedule validation, caching,
and source validation logic
- Update cookbook configuration to customize auto_fin step parameters and simplify
outbound proxy settings
- Refactor imports and clean unused code for better maintainability
* feat(auto_fin): introduce detailed historical event resolution and market similarity analysis
- Add AutoFinHistoricalEventReference and AutoFinHistoricalSimilarity models for refined event referencing and similarity judgment
- Implement validation to ensure non-empty critical fields and uniqueness of historical news IDs
- Develop method to resolve Agent-selected historical event references from workspace files with strict path and existence checks
- Enrich historical events with market entry and future returns data after resolution
- Redesign market step to calculate similarity-weighted ETF forecasts based on matched historical event similarities
- Enforce validation on matched historical events for uniqueness and proper weight summation
- Simplify merge step output to final Markdown report without YAML frontmatter and redundant fields
- Update user instructions for history search, market, and merge steps to reflect new data structures and responsibilities
- Adjust test suite to cover new schema and step behavior changes, including enhanced validation and JSON output formats
* feat(auto_fin): add new cron jobs and output analysis jsonl
- Add new cron jobs auto_fin_1145_cron and auto_fin_1800_cron with auto_fin_steps
- Change auto_fin_0930_cron schedule to run Monday to Sunday
- Extend merge step to write analysis data to auto_fin_analysis.jsonl
- Update unit tests to verify new cron jobs and their steps configuration
* fix(auto_fin): improve atomic file write and refresh daily index
- Change temporary file naming to include UUID for uniqueness and hidden prefix
- Replace atomic write method from using Path.replace to os.replace with safe unlink
- Add import and use os.replace for safer file replace operation
- Refresh daily index after writing auto finance markdown and JSONL files
- Import and call refresh_day_index in merge step to update file index asynchronously
* docs(cookbook): add optional SSH proxy configuration in README files
- Introduce optional SSH proxy setup in auto-fin and daily_paper cookbooks
- Provide instructions to enable outbound proxy via `daily_cookbook.yaml` and environment variables
- Add `REME_PROXY_IP` and `REME_PROXY_ACCOUNT` environment variables descriptions in multiple README files
- Update English and Chinese README and README_ZH documents with proxy details
- Maintain consistent formatting of environment variable tables across documents
* fix(file_io): include schema_version in hidden metadata keys
- Added "schema_version" to _INDEX_HIDDEN_METADATA_KEYS in _daily_index.py
- Updated _render_notes_block to always include additional keys regardless of schema_version
fix(deps): move pproxy dependency to later in pyproject.toml
- Removed pproxy from early dependencies list
- Added pproxy back near the end of dependency list for better ordering
fix(outbound_proxy): require pproxy package for ssh_http proxy
- Added importlib.util check for pproxy package presence
- Raise RuntimeError if pproxy is not installed when using SSH HTTP outbound proxy
- Improved error message suggests installing reme-ai with 'core' extra
* docs(readme): update News section with new Cookbook workflows
- Clarify introduction of optional Cookbooks with Daily Paper and Auto Fin workflows
- Update English README to reflect both paper discovery and file-native ETF event research
- Revise Chinese README to include financial news and historical market data research capability
- Maintain announcement of paper acceptance at Findings of ACL 2026
* feat(auto_fin): add calculation results to final Markdown output
- Implement _calculation_results to summarize forecast for each ETF analyzed
- Include program-calculated results in the JSON input for the Markdown report
- Update YAML template to incorporate calculation results and adjust recommendation rules
- Refine recommendation logic to rely on event impact judgments combined with calculation outputs
- Modify tests to verify presence of calculation results and updated report content and format
* up prompt
* fix(keyword_index): ignore non-indexable chunks during keyword sync
- Add is_indexable method to base and BM25 keyword index classes to check text tokenizability
- Update local file store to exclude non-indexable chunks from expected document IDs to prevent rebuild
- Fix JSONL chunker to correctly handle Unicode line separator U+2028 inside JSON strings without splitting
- Add test to ensure non-empty but non-indexable chunk does not trigger keyword index rebuild
- Add test to verify U+2028 character does not cause incorrect JSONL record splitting
* feat(daily-paper): add daily paper cookbook workflow with schema and tests
- Introduce daily paper schema types (DailyBriefOutput, PaperInfo, PaperNoteOutput, etc.)
- Create daily paper cookbook module with analyze, collect, digest, rank, and select steps
- Add cookbook entry point and integrate into main steps module
- Replace job config export with daily brief output in schema exports
- Add comprehensive unit tests covering pipeline, filtering, and output generation
- Update dependencies including openai-codex and pypdf packages
- Configure standalone daily paper cron job with proper scheduling and routing
* test(daily_paper): update tests to use Claude Code wrapper exclusively
- Add test to verify web search is disallowed by default in Claude Code
- Update imports to include DailyBriefOutput, PaperNoteOutput, and PaperSelection schemas
- Change test name from standalone_config_has_backend_split to reflect Claude Code only usage
- Remove default agent wrapper and configure all steps to use Claude Code wrapper
- Rename select_wrapper to cc_wrapper for clarity and consistency
- Remove duplicate Claude Code wrapper initialization
- Update test assertions to verify output schema usage matches expected sequence
- Remove unused as_llm component from standalone configuration test
* refactor(agent-wrapper): simplify skill resolution logic across all wrappers
- Replace duplicate skill resolution code with centralized _resolve_project_skills method
- Add project_path property with configurable relative path resolution
- Introduce proper validation for skill names and directory existence
- Change Codex wrapper to use project_path instead of workspace_path for skills
- Add SKILL.md requirement validation for project skills
- Remove redundant skill processing logic from individual wrappers
* feat(daily_paper): add daily paper workflow with PDF analysis and brief generation
- Implement shared state management and file helpers for daily-paper steps
- Add PDF download and text extraction capabilities with arXiv integration
- Create paper collection step with Hugging Face weekly/monthly rankings
- Build ranking system using reciprocal-rank fusion with memory keyword scoring
- Add Claude Code integration for paper analysis and detailed note generation
- Implement digest step to create final five-minute brief from detailed notes
- Add configuration for standalone daily cookbook application with cron scheduling
- Create typed schema for paper information, selection, and output formats
- Add atomic file writing with temporary file safety mechanisms
- Implement exclusion logic for previously recommended papers and daily filters
* feat(daily_paper): add DingTalk notification integration and enhance logging
- Integrate DingTalk markdown send step to notify groups about daily paper briefs
- Add comprehensive logging throughout daily paper workflow including start/finish events
- Update daily paper analysis prompt to include code repository context requirement
- Configure DingTalk notification in daily_cookbook.yaml with app credentials
- Add dingtalk-stream dependency for proactive message API integration
- Enhance daily paper README with DingTalk notification section and updated flow chart
- Implement detailed logging for each step including paper processing and agent calls
- Add test coverage for DingTalk markdown sending functionality and configuration
- Update pre-commit config to exclude skills directory from checks
- Add .claude/skills to gitignore for local development environment
* refactor(dingtalk): move dingtalk_stream import to local scope and improve code safety
- Moved global dingtalk_stream import to local scope in send.py to avoid eager loading
- Added dynamic import with error handling for optional dependency cases
- Updated test suite to verify lazy loading behavior works correctly
- Fixed markdown title generation by using safe variable naming in wait.py
- Enhanced test coverage for arxiv PDF download caching functionality
- Updated application context initialization with proper resource directory configuration
- Modified paper metadata to include source PDF path reference in output files
* refactor(daily_paper): remove manifest system and store selection metadata in digest files
- Remove JSON manifest creation and storage functionality
- Store selection data directly in digest file frontmatter instead of separate manifest files
- Add load_saved_selection method to rebuild selection from digest and paper-note metadata
- Update README documentation to reflect new cookbook workflow architecture
- Modify test cases to verify selection metadata in digest files instead of manifest JSON
- Remove unused json import from multiple daily paper modules
- Integrate PaperSelection schema for proper data validation in stored metadata
* docs(daily_paper): add bilingual cookbook guides
* feat(memory): add ContextChecker component for context size management
* refactor(memory): restructure file-based memory tools and update imports
* docs(readme): update documentation with detailed architecture and components
* docs(readme): update Chinese documentation with enhanced memory management diagrams
* refactor(cookbook): move cookbook files to test directory and clean up docs
* docs(readme): update link path for old version documentation
* docs(readme): update documentation with improved architecture diagrams and component details
* docs(readme): update documentation with improved clarity and structure
* refactor(docs): update in-memory memory documentation
* docs(readme): add experiment reproduction link to quickstart guide
- 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
- 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 LLMMockSearchOp for simulating search operations with configurable complexity- Created SearchToolA, SearchToolB, and SearchToolC with distinct performance profiles- Implemented UseMockSearchOp for intelligent tool selection based on query analysis
- Added test scripts and query datasets for evaluating tool memory effectiveness
- Integrated tool memory service for storing and retrieving tool performance data
- Created documentation for tool memory benchmark testing methodology
- Refactored agent module structure and imports
- Increased summary tool memory recent call count from20 to 30
- 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
- Update memory entries with more specific and detailed content
- Improve structure and clarity of analysis, especially for complex subjects
- Refine synthesis process to create more coherent and insightful summaries
- Adjust scores and confidence levels based on improved analysis quality
- Update tags to better reflect the nature of analysis and synthesis tasks
- Update personal memory demo to use new memory format
- Remove unused task memory and message files
- Adjust vector store configuration in default.yaml
- Modify load_today_memory_op to use new search method
- 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
- Update AppWorld quickstart guide to use ReMe instead of ExperienceMaker
- Update FrozenLake quickstart guide to use ReMe and improve clarity
- Refactor FrozenLake experiment implementation and documentation
- Add more detailed explanation of task memory mechanism in FrozenLake
- Rename and rebrand experience-related variables and functions to task memory
- Update Appworld and FrozenLake agents to use task memory instead of experience
- Add new functions for handling API responses, deleting workspaces, and dumping/loading memories
- Modify run scripts to incorporate task memory creation and usage
- Update logging and print statements to reflect new task memory terminology
- Move retrieve_personal_memory and summary_personal_memory flows to new positions in default.yaml
- Update info_filter_op to handle trajectories instead of messages
- Adjust use_personal_memory_demo to use trajectories in API requests
- Add SimpleReactOp to react module
- Update reme_ai/__init__.py to include react module
- Modify contra_repeat_op.py to use memory_id instead of id
- Adjust datetime_handler.py to handle string datetime conversion
- Update default.yaml to include react flow content
- Modify get_observation_op.py and get_observation_with_time_op.py to use workspace_id from context
- Update test/http_client_test.py to test new react functionality
- Adjust messages.jsonl to reflect new analysis approach for Xiaomi Corporation
- Add SimpleReactOp to react module
- Update reme_ai/__init__.py to include react module
- Modify contra_repeat_op.py to use memory_id instead of id
- Adjust datetime_handler.py to handle string datetime conversion
- Update default.yaml to include react flow content
- Modify get_observation_op.py and get_observation_with_time_op.py to use workspace_id from context
- Update test/http_client_test.py to test new react functionality
- Adjust messages.jsonl to reflect new analysis approach for Xiaomi Corporation
- Add SimpleReactOp to react module
- Update reme_ai/__init__.py to include react module
- Modify contra_repeat_op.py to use memory_id instead of id
- Adjust datetime_handler.py to handle string datetime conversion
- Update default.yaml to include react flow content
- Modify get_observation_op.py and get_observation_with_time_op.py to use workspace_id from context
- Update test/http_client_test.py to test new react functionality
- Adjust messages.jsonl to reflect new analysis approach for Xiaomi Corporation
- Rename experiencemaker package to reme_ai
- Move personal modules to new directory structure
- Remove unused classes and imports
- Update module initialization files
