1. search_v2: catch per-entry exceptions from run_job('compressor') inside
compress() so asyncio.gather never propagates a compressor failure (e.g.
temporary LLM outage). The failing entry keeps its original body while
remaining entries are still compressed, preserving already-retrieved
search results.
2. read: replace 'context_value or yaml_value' with an existence check so
that a runtime-injected False can explicitly disable a YAML-true
read_step_format_session flag.
Add focused unit tests for both paths.
- Add compressor job and compressor as_llm component to reme/config/beam.yaml
(aligned with lme.yaml) so that compress_session: true works for BEAM
- Add graceful degradation guard in search_v2._compress_session_entries:
when the compressor job is missing from the active config, log a warning
and skip compression instead of raising 'Job compressor not found'.
Skipped when there is no app_context so unit tests mocking run_job still
drive compression behavior.
- Fix BEAM download instructions in README.md/README_ZH.md: add mkdir -p
before cd benchmark/beam/dataset (the directory is gitignored and absent
in a fresh clone)
* docs(benchmark): archive ExpG tool-use results under toolmemory
Add ToolMemory benchmark materials and link them from the root and
benchmark READMEs so ReMe documents the ExpG tool-use enhancement work.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: point ToolMemory news entry directly to the paper
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(benchmark): address ToolMemory review and pre-commit
Restore benchmark index READMEs, link ExpG to WangCan1178/ExpG instead
of ReMe version notes, and format tool_memory.py for CI hooks.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(benchmark): align ToolMemory client with official ReMe APIs
Drop ExpG-only request fields and non-official metadata handling so the archived client matches add/summary/retrieve Tool Memory endpoints.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(benchmark): fix trailing whitespace in ToolMemory READMEs
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* 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
Add _format_history to annotate each turn with [Ln] line numbers and
expose {session_file} in prompts so the agent can emit bare wikilink-style
source markers like [[session/dialog/s1.jsonl#L1-L2,L5-L6]] at the end
of factual entries. Consolidate the per-prompt body/format rules into
references to the system prompt to avoid drift, and add frontmatter-
protection guidance for the edit tool.
Move session-transcript compression from LME hard-coded injection to a
runtime context flag set by evaluation.compress_session in each
benchmark config. Compression is off by default for both BEAM and LME,
and BaseAgenticAnswerStep now conditionally injects the _search compress
payload only when the flag is truthy.
- Add CompressorStep (reme/steps/evolve/compressor.py) for direct LLM
text compression with optional query-guided relevance filtering
- Extend search_v2_step to support query-aware and query-independent
session transcript compression via _compress injected kwargs
- Refactor _source_format.py: split into render_chunk_entries +
join_chunk_entries; session chunks now render line-aligned with
L<n>: prefixes for verbatim/compressed parity
- Add JOB_TOOLS and INJECTED_JOB_KWARGS to BaseAgenticAnswerStep for
per-subclass tool and parameter injection
- LmeAgenticAnswerStep injects _search._compress payload to enable
query-aware compression during benchmark evaluation
- Record compression ablation results in result-longmemeval.md
- Add unit tests for CompressorStep and search compression paths
- Add _format_history hook in AutoMemoryStep with line-number annotation
- Override in BeamAutoMemoryStep to prefix each turn with [Ln] for citation
- Add session_file variable to prompt templates for source marker paths
- Simplify repeated extraction rules by referencing system prompt
- Enhance agentic_answer search strategy (multi-search, read tool hint)
- Add warning log on ReadStep failure
- Add 'read' to job_tools in BaseAgenticAnswerStep for file reading capability
- Document read tool usage in lme/agentic_answer.yaml system prompt
- Update result-beam.md with latest evaluation scores (OVERALL: 0.623/0.580)
- Introduce tolerant AutoFinAgentModel base class allowing extra fields in raw Agent outputs
- Replace strict models with tolerant ones for ETF, historical event, market selection, and report outputs
- Remove redundant field validators and allow empty defaults for key string fields
- Enhance historical source path resolution to safely filter invalid or out-of-workspace paths
- Add normalization of whitespace and validation to historical event references before processing
- Implement normalization in Topic and Market Agent selections to eliminate duplicates, blanks, unknowns
- Limit Topic Agent output to top 20 ETFs and ensure sorting and deduplication of events
- Normalize final Markdown report by removing redundant headers and providing safe fallbacks
- Update agent prompts to clarify task constraints and improve instruction consistency
- Add extensive tests for normalization, filtering, and safe source resolution for historical events
* feat(file_store): upgrade FAISS to HNSW index with async reindex and path constraint
- Replace IndexFlatIP with IndexHNSWFlat for better recall/speed tradeoff
- Add dynamic efSearch (limit * 5) scaled to query request size
- Add async_reindex option: background rebuild with generation-based invalidation
- Extract _delete_nodes() in LocalFileStore for subclass reuse
- Add unit tests for file store consistency
* fix: resolve pylint warnings in faiss store and test file
* refactor(file_store): replace generation-based reindex with event-flag worker
- Replace _reindex_generation/lock/task with a single long-lived worker
coroutine consuming an asyncio.Event flag; repeated submissions coalesce
- Use local index reference in vector_search to avoid TOCTOU on self._faiss_index
- Pass index explicitly to _set_ef_search for consistency
- Track _index_writes to re-arm reindex after concurrent writes
- Update tests to match new internal API
* fix: resolve pylint too-many-return-statements and implicit-booleaness warnings
* feat(file_store): add refine maintenance hook and incremental embedding backfill
- Add refine() idle-time maintenance hook to BaseFileStore/LocalFileStore
- FaissLocalFileStore: incremental vector add on backfill instead of full rebuild
- Dynamic tombstone compaction threshold scaled by index size
- Add RefineStoreStep with daily cron job (refine_store_cron)
- Enable faiss backend and embedding_store by default in default.yaml
- Add unit tests for faiss index maintenance
* chore(deps): promote faiss-cpu to core dependencies
faiss backend is now the default file_store, so faiss-cpu moves from
the optional [core] extra to the base dependencies list.
* feat: rename refine_store to optimize_index and add vecdb_path_constraint
- Rename refine_store step to optimize_index with cron job scheduling
- Add vecdb_path_constraint to file_store components
- Update default.yaml with optimize_index_cron and faiss backend comment
- Update memory_search docs (en/zh) for FAISS vector management
- Update unit tests for index maintenance
* feat(faiss): add embedding digest to reject stale sidecar after partial dump
Add _chunks_embedding_digest() that computes an order-independent SHA-256
over (chunk_id, float16 embedding) pairs. The digest is written into the
idmap sidecar at dump time and verified at load time. A mismatch means the
sidecar vectors belong to a different chunk generation than the authoritative
JSONL — detectable even when the live-ID set is unchanged (same-ID in-place
update crash window).
Add test_faiss_rejects_stale_sidecar_after_partial_dump reproducing the
crash-between-writes scenario and asserting digest-based rejection.
Compress verbose docstrings/comments in existing tests for pylint line
budget.
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
- Add AutoFinHistoricalDirectionReference model to classify historical events by direction
- Remove AutoFinHistoricalSimilarity and related similarity score usage
- Update AutoFinMarketSelection to handle same and opposite direction event lists
- Adjust AutoFinMarketStep to calculate forecasts based on equal weights and direction signs
- Change market.yaml instructions to require direction classification instead of similarity scoring
- Modify tests to reflect direction-based classification and verify uniqueness across direction groups
- Improve DingTalkWaitStep to support reconnect on server request with proper disconnect reason handling
content.split("\n") yields a trailing empty element for any file ending in a
newline, inflating total by 1 and letting a start_line one past real EOF be
silently accepted instead of rejected. Mirrors the trailing-newline correction
default_file_chunker already applies, and matches the large-file path's
line-by-line count.
Fixes#388
- Separate candidate source file resolution from event resolution logic
- Allow fallback to date-derived daily news file if original source is missing
- Check existence and validity of historical source files more robustly
- Handle multiple candidate source files and aggregate matches before validation
- Gather and log resolution limitations without stopping processing
- Return resolved events along with a list of resolution warnings
- Update related code to consume new return signature and merge limitations
- Add detailed validation on source path relativity and file naming conventions
* 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(search): add tool_context-scoped chunk dedup with TTL
Introduce _ToolContextDedupMixin shared by search/vector_search/bm25_search
to skip already-seen chunks within one agent tool_context. Per-context state
lives in app_context.metadata with configurable TTL (default 24h).
* feat(search): unify chunk answer rendering with merge and explicit empty messages
- Refactor SearchStep/VectorSearchStep/Bm25SearchStep to share format_chunks_answer for consistent source rendering and adjacent session-chunk merging.
- Distinguish empty results: ALL_RETURNED_MESSAGE when dedup removes everything vs NO_RESULTS_MESSAGE when nothing matched.
- Bump JsonlFileChunker default max_chars to 4000.
- Add unit tests for source-format merge and empty-result messages.
* refactor(config): reorganize file_chunker components and move jsonl max_chars into config
- Register explicit markdown/json/jsonl chunkers in beam.yaml and lme.yaml with markdown options (embed_toc, max_ast_sections, frontmatter handling) and jsonl max_chars=4000.
- Restrict default chunker to txt/log extensions.
- Revert JsonlFileChunker code default max_chars back to 2000; the 4000 value now lives in config.
* chore(benchmark): increase longmemeval num_items from 64 to 500
* refactor(search): split SearchStep into simplified and v2 variants, extract counter utility
- Extract global_counter_next from ApplicationContext into reme/utils/counter.py
as a standalone function operating on metadata dict with lazy initialization.
- Split SearchStep into two variants:
- SearchStep (simplified): inline chunk.id dedup, single-branch vector/keyword
optimization based on vector_weight, inline answer formatting.
- SearchV2Step (full): preserves _ToolContextDedupMixin with interval-subset-aware
dedup and format_chunks_answer with session-aware chunk merging.
- Update beam.yaml and lme.yaml to use search_v2_step for benchmark jobs.
- Rename existing search tests to test_search_v2_step_* and add new
test_search_step_* tests covering the simplified variant.
* fix: normalise missing trailing newline in _build_union_chunk to prevent line collision
* refactor: lazy-init counter tree in ApplicationContext metadata
- Remove hardcoded _counter_tree and _counter_tree_lock initialization
from ApplicationContext.metadata; rely on lazy initialization in
reme.utils.counter.global_counter_next on first call
- Set longmemeval num_items back to 500
- Remove obsolete trailing-newline collision tests
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
* feat(eval): add LongMemEval evaluation framework with tool_defaults date injection
- Add evaluation/longmemeval/ with run.py, config.yaml, and test scripts
- Add reme/config/longmemeval.yaml for evaluation-specific model config
- Add tool_defaults mechanism to as_agent_wrapper for injecting default
tool kwargs (uses setdefault so LLM-provided values take priority)
- Pass tool_defaults={'daily_write': {'date': day}} in auto_memory to
ensure notes always use the correct historical date
- Add timestamp interpolation (_interpolate_timestamps) in auto_memory
for filling missing created_at fields via linear interpolation
- Evaluation pipeline: ingest sessions -> dream -> search -> answer -> judge
- Uses qwen3.6-flash for memory, qwen3.7-max for answer/judge
* chore: gitignore logs/results/demo.py, keep empty dirs
* chore: update .gitignore
* feat(eval): add multiprocessing and session time filtering to longmemeval runner
- Replace async execution with synchronous + multiprocessing for parallel item evaluation - Add filter_future_sessions option to only ingest sessions <= question date - Add question_types filtering in config - Add result summary with binary accuracy and avg score - Update config defaults (oracle variant, 50 items, 32 workers) - Minor code style fixes in agent_wrapper and auto_memory
* feat: add bench_query_step with ReAct agent for benchmark query phase
- Add BenchQueryStep using agent_wrapper with search job tool
- Replace manual search+LLM answer in run.py with bench_query_job
- Remove unused answer LLM config from longmemeval.yaml
- Register benchmark step module in steps/__init__.py
* feat: add start_date/end_date time filter support for search job
- Add _extract_date_from_path to extract validated YYYY-MM-DD from chunk paths
- Add start_date/end_date filtering in _matches_search_filter
- Implement progressive recall in FaissLocalFileStore.vector_search
- Promote start_date/end_date from context to search_filter in SearchStep
- Add start_date/end_date parameters to search job in default.yaml
- Add unit tests for date filter functionality
* fix: validate/normalize date filters and harden _extract_date_from_path
Address three code-review comments on the time_filter search feature:
1. Validate/normalize start_date and end_date before string comparison.
_matches_search_filter does lexicographic comparison against path_date
(always canonical YYYY-MM-DD). Raw caller values like '2026-2-28' or
'abc' would produce silently wrong results. Now SearchStep normalizes
valid dates via extract_daily_date (with strptime fallback for
non-zero-padded input) and silently ignores invalid dates with a
logger.warning, removing them from the filter.
2. Clarify behavior for paths without embedded dates.
Added optional strict_date_filter parameter (default False). When True
and at least one date bound is active, chunks whose path yields no date
(e.g. digest/personal/topic.md) are excluded. When False (default),
the existing behavior is preserved — dateless paths pass through.
3. Harden _extract_date_from_path against non-standard suffixes.
Previously parts[1].split('.')[0] accepted '2026-05-18.anything' as a
valid date. Now only exact 'YYYY-MM-DD' (dir) and 'YYYY-MM-DD.md'
(day-index) forms are accepted.
* feat(eval): LLM-as-Judge per-type prompt routing, binary-only, progress tracking
- Remove 0-5 score metric, keep only binary (yes/no) classification
- Load per-question-type judge prompts from llm-as-judge.json
(temporal-reasoning, knowledge-update, single-session-preference, __default__)
- Replace SCORE_JUDGE_PROMPT with type-specific BINARY_JUDGE_PROMPT template
- judge_response(): parameter 'metric' -> 'question_type', returns single 'judgment'
- Summary output: add per-type accuracy breakdown, remove score stats
- Add progress tracking: background thread prints PROGRESS every 10min
- Add FINAL progress line and total elapsed time on completion
- Add --log-level, --reme-log-level, -q CLI arguments
- Parallel mode: pool.map -> pool.imap_unordered for real-time progress
- config.yaml: full oracle (10000 items), 32 workers, all question types
- Add kill.sh (process cleanup) and run_async.sh (background eval launcher)
* docs: add LongMemEval oracle evaluation results (61.6% accuracy)
* feat(bench): add MAX_ITERATION limit to BenchQueryStep and add _auto_memory.yaml
* feat: add golden session benchmark & eval_only mode with refined prompt
- Add benchmark/longmemeval/run_golden_session.py for golden session evaluation
- Refine PROMPTED_SYSTEM_PROMPT: concise answer rule, remove 'Information not found' fallback
- Add eval_only mode to run.py (--eval_only flag)
- Add multiple eval config variants (evalonly, full, test5)
- Add analyze_results.py for result parsing
- Update auto_memory.yaml, longmemeval.yaml, application_config
- Update result-longmemeval.md with latest evaluation results
- Add benchmark results to .gitignore
* update: refine answer prompts and increase max iteration to 6 - Tighten prompted-answer system prompt for more concise output - Comment out 'Information not found' fallback rule - Increase MAX_ITERATION from 5 to 6 in bench_query - Add recall_eval.py - Update evaluation results
* feat(chunker): add dedicated JSON and JSONL file chunkers (cherry-pick from upstream #325)
- Add JsonFileChunker: structure-aware chunking preserving nested key paths,
optional list-to-dict conversion, size measured by json.dumps() char count
- Add JsonlFileChunker: line-aligned sliding-window chunking with configurable
overlap, supports char/byte mode switching
- Register both chunkers in default.yaml (json for .json, jsonl for .jsonl)
- Add comprehensive unit tests (21 + 20 test cases)
* feat(service): add CLI service for local job execution (from upstream #334)
- Introduce CliService to execute single jobs locally without serving ports
- Add prepare_start_config and should_precheck_start functions for CLI job setup
- Update reme start command to use CLI service when job argument is provided
- Add show_metadata to client kwargs for optional CLI metadata output
- Add unit tests for CLI service functionality and configuration handling
* feat(steps): add BM25/vector search steps, Python execute step, and draft steps (from upstream #334)
- Add Bm25SearchStep for plain BM25 keyword search with tool_context deduplication
- Add VectorSearchStep for plain vector search with tool_context deduplication
- Add PythonExecuteStep to run Python code in subprocess with timeout handling
- Add AddDraftStep/ReadAllDraftStep for draft accumulation scoped by tool context
- Update SearchStep with tool_context dedup, dynamic default limit via REME_SEARCH_LIMIT env,
and candidate_multiplier default changed from 3.0 to 5.0
- Add comprehensive unit tests for all new steps
* feat(search): add tool context deduplication and improve search configuration (#321)
* feat(search): add tool context deduplication and improve search configuration
- Modify _make_tool methods to accept and inject tool_context_id parameter
- Add tool_context_id handling in AS and CC agent wrappers
- Increase search candidate multiplier from 3.0 to 5.0 in default config
- Extend HTTP client timeout from 30s to 3600s
- Add tool context deduplication logic to prevent duplicate search results
- Implement TTL-based expiration for seen chunks in tool contexts
- Add comprehensive unit tests for tool context deduplication behavior
- Update .gitignore to exclude longmemeval directory
- Add time import for timestamp functionality in search step
* 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
* fix(file_io): fix risk of out-workspace paths (#322)
* fix(file_io): fix risk of out-workspace paths
* chore(file_io): remove unused unittest file
* fix(as_embedding): support both agentscope 2.0.2 and 2.0.3 (#323)
2.0.3 promoted `dimensions` to a required first-class constructor
argument while keeping a backfill from `parameters.dimensions`; 2.0.2
has no such argument and reads `dimensions` from `Parameters`. Keep
`dimensions` in `Parameters` for both versions and, when the model
constructor accepts `dimensions`, pass `dimensions=None` so 2.0.3's
backfill promotes it out of `parameters`.
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Bump version to 0.4.0.7
* refactor: delegate LLM-as-Judge to answer_judge_step and update eval config/results
- run.py: replace inline judge logic with judge_response_via_job using app.run_job('answer_judge')
- longmemeval.yaml: expand benchmark configuration
- bench_query.py: enhance benchmark query step
- result-longmemeval.md: update evaluation results
- judge_all_plus_results.json: add judge all-plus results
* refactor: split longmemeval.yaml into lme.yaml/beam.yaml and unify job names
- Split reme/config/longmemeval.yaml into lme.yaml (LongMemEval) and beam.yaml (BEAM)
- Unify job names across both configs: agentic_answer, answer_judge, context_answer
- Update evaluation/longmemeval/run.py and evaluation/beam/run_beam_eval.py to use unified job names
- Update all evaluation config YAMLs to reference lme.yaml
- Add BEAM benchmark step implementations (agentic_answer, context_answer, llm_judge)
- Remove obsolete config_test5.yaml and test_5sessions.py
* eval: BEAM 100K & LongMemEval cleaned-S 评测结果记录
- BEAM 100K eval-only (32并发, 20 case): Agentic 0.631, Prompted 0.468
- LongMemEval final GT (500题): Agentic 89.0%, Prompted 83.6%
- 新增 benchmark/result-beam.md, benchmark/result-longmemeval.md
- benchmark/beam/config.yaml: num_workers=32
* refactor: restructure benchmark directory and clean up gitignore rules
- Consolidate benchmark outputs to benchmark/results/ with .gitkeep
- Remove old benchmark scripts, configs and result files from benchmark/beam/ and benchmark/longmemeval/
- Add datasets/README.md and datasets/README_EN.md with download instructions
- Add datasets/longmemeval/download.py and final_groundtruth_cleaned_s.json
- Add memory_workspaces .gitkeep placeholders
- Restructure .gitignore: fix duplicate entries, add BEAM dataset exclusion, refine logs/results ignore patterns
- Remove stale result-beam.md and result-longmemeval.md from project root
* chore: clean up longmemeval benchmark scripts and update dataset docs
- Remove obsolete longmemeval benchmark runner/stats scripts
- Update datasets/longmemeval README and add Chinese translation
- Clean up final_groundtruth_cleaned_s.json
* docs(benchmark): add reproduction guide for LongMemEval and BEAM
- Add bilingual README for benchmark runners (EN/ZH)
- Cover prerequisites, dataset download, run commands, configs, outputs, logs, and kill.sh
* refactor: migrate auto_memory steps from evolve to benchmark-specific modules
- Split auto_memory into beam and lme benchmark-specific implementations
- Add auto_memory.py and auto_memory.yaml under steps/benchmark/beam and steps/benchmark/lme
- Slim down evolve/auto_memory.py and auto_memory.yaml to shared base only
- Remove obsolete evolve/_auto_memory.yaml
- Update benchmark run.py, config YAMLs, and step __init__.py registrations
- Update llm_judge and context_answer minor adjustments
- Remove outdated test_lme_final_answer_review.py
* revert(as_agent_wrapper): sync with upstream/main
Remove local-only comment to keep file identical with upstream/main.
* style: add trailing commas in benchmark __init__.py __all__ lists
* chore: disable vector_weight range assertion in SearchStep
* chore: add tests/integration/logs/ to .gitignore
* refactor: replace scipy.stats.kendalltau with pure numpy implementation
scipy is not listed in project dependencies. Implement Kendall's tau-b
rank correlation using only numpy to remove the undeclared dependency.
* feat(benchmark): add binary score metrics, update BEAM 1M results, and improve LLM retry/prompt config
- benchmark/beam/run.py: add binary score calculation per rubric item and per-type/overall binary stats
- benchmark/beam/config.yaml: switch to 1M dataset, reduce workers to 18
- benchmark/result-beam.md: add 1M evaluation results with binary scores
- benchmark/result-longmemeval.md: minor formatting
- reme/config/beam.yaml: increase max_retries to 5 and add retry_delay 5.0 for all LLM components
- reme/config/lme.yaml: increase max_retries to 5 and add retry_delay for judge/prompted/bench components
- reme/steps/benchmark/lme/agentic_answer.yaml: improve search strategy and answer rules prompts
* fix(benchmark): fix line-too-long and add pylint disable for main()
* refactor(longmemeval): use single cleaned-S dataset with embedded ground truth
- Switch to agentscope-ai/ReMe_longmemeval_clean_s_v2 HuggingFace source
- Remove separate final_groundtruth_cleaned_s.json (ground truth now in data file)
- Simplify download.py to fetch only longmemeval_s_reme_cleaned.json
- Remove dataset.variant and dataset.ground_truth_path config options
- Update benchmark and datasets READMEs to reflect new workflow
- Update .gitignore for new dataset filename
* fix: rename loop variable to avoid pylint redefined-outer-name warning
* refactor(benchmark): restructure datasets/memory_workspaces into benchmark and simplify auto_memory steps
* refactor(benchmark): extract BaseAgenticAnswerStep into base module
- Add reme/steps/benchmark/base/agentic_answer.py with shared agentic answer logic
- Refactor beam/lme AgenticAnswerStep to inherit from BaseAgenticAnswerStep
- Simplify lme/context_answer.py and update context_answer.yaml
- Update result-longmemeval.md with latest evaluation results (agentic 91.0%)
* refactor(benchmark): remove context_answer steps and unused configs
- Remove beam/lme context_answer job definitions and step implementations
- Remove prompted LLM component from beam.yaml and lme.yaml
- Delete jinli_lme.yaml (no longer needed)
- Simplify benchmark run.py scripts
- Clean up .gitkeep files and update .gitignore
- Remove unused import in search.py
* chore: remove benchmark/results/.gitkeep
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
Co-authored-by: jinliyl <6469360+jinliyl@users.noreply.github.com>
Co-authored-by: imrewce <wce@pku.edu.cn>
Co-authored-by: Sen Huang <48879559+ployts@users.noreply.github.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat(config): add environment variable configuration for agent subprocesses
- Add environment field to ApplicationConfig to store variables for agent subprocesses
- Remove dynamic loading of .env files in agent wrappers
- Introduce subprocess_environment property in base agent wrapper
- Pass application-level environment variables to Claude Code and Codex agents
- Load environment variables once at startup and pass to ReMe application
- Remove dependency on load_env utility in agent wrapper implementations
- Update tests to use configured environment instead of dynamic loading
- Remove unused environment loading utilities and related test cases
* refactor(mcp): remove channel notification system and related components
- Removed channel notification step implementation
- Removed claim channel step implementation
- Removed ChannelSink class from MCP service
- Removed channel-related documentation from AGENTS.md
- Removed channel instruction text from MCP service
- Removed all channel-related tests
- Updated application context metadata comment to remove channel sink reference
- Removed channel module initialization and imports
* feat(service): add job whitelisting capability to BaseService
- Add optional jobs parameter to BaseService.__init__ to configure job whitelist
- Store jobs as set in self.jobs attribute for efficient lookup operations
- Modify add_jobs method to filter jobs based on whitelist configuration
- Update documentation in both English and Chinese to describe new feature
- Add comprehensive unit tests for job whitelisting behavior
- Implement flowchart update showing new filtering logic
- Preserve existing enable_serve flag behavior alongside new whitelisting
* refactor(service): enhance service job validation and MCP tool injection
- Add strict validation for service jobs whitelist with detailed error messages
- Implement injected job arguments support for MCP services with conflict detection
- Add tool error handling for unsuccessful responses in MCP services
- Remove duplicate job names in Codex agent wrapper using dict.fromkeys
- Update MCP server argument format from single JSON array to repeated --job flags
- Add comprehensive test coverage for job injection and error handling scenarios
- Update documentation to reflect service job validation and MCP features
- Ensure application cleanup occurs even when service lifespan encounters errors
* feat(agent): update skill handling to preserve existing Claude skills
- Change skills parameter processing to use 'all' instead of filtered list
- Add logic to select project skills without restricting Claude's existing skills
- Update variable naming from 'skills' to 'selected_skills' for clarity
- Modify application context metadata documentation to clarify in-memory state usage
- Add test case to verify configured skills are added without filtering existing skills
- Update internal skill directory handling to use renamed variable consistently
* refactor(agent): restructure agent wrapper components and session storage
- Move CcFileSessionStore to separate module for better organization
- Add SDK package version logging in base agent wrapper
- Update Claude Code agent to use new session store structure with project keys
- Refactor Claude Code agent wrapper to use proper type hints and SDK integration
- Add support for server tool use events in Claude Code message processing
- Improve error handling and resource cleanup in streaming operations
- Update Codex agent wrapper with proper type annotations and configuration
- Remove deprecated system prompt mode handling from Claude Code wrapper
- Fix session path construction for Claude Code transcript storage
- Update dependency injection and configuration handling patterns
* fix(cc_agent_wrapper): resolve Claude Code SDK integration issues
- Added dataclass import and created _BlockState for content block metadata tracking
- Implemented proper MCP server name constant and tool context ID validation
- Fixed tool_context_id injection to prevent duplicate assignment errors
- Resolved skills parameter handling in build_options method
- Enhanced job tools integration with MCP servers mapping validation
- Replaced deprecated block_ids/block_types/tool_call_names with block_states dict
- Updated message_delta to emit USAGE chunks instead of REPLY_END
- Fixed stream result handling to ensure proper REPLY_END emission
- Improved error handling for session mirror failures and rate limits
- Added proper cleanup for expected trailing errors in streams
- Refactored Codex agent wrapper initialization and configuration management
- Removed obsolete system_prompt_mode from default config
- Enhanced test coverage for new block state and error handling features
- Fixed async generator handling with aclosing context manager
- Improved chunk type mapping for Claude Code SDK events
* refactor(tests): remove demo config tests from config parser test suite
- Removed test_demo_config_registers_llm_jobs function and its assertions
- Eliminated verification of LLM demo job configurations
- Removed checks for agent wrapper component settings
- Deleted assertions for model configurations and parameters
- Cleaned up deprecated test cases related to demo config parsing
* refactor(evolve): simplify Claude Code session store path structure
- Removed redundant project key subdirectory from session link generation
- Updated CcFileSessionStore initialization to use direct session directory path
- Maintained existing session layout compatibility for backward compatibility
- Added unit tests to verify session persistence behavior with existing transcripts
- Ensured UUID-based session files remain accessible at expected locations
- Preserved existing session directory structure without additional nesting
* refactor(agent): defer optional Codex SDK imports until first use
- Moved openai-codex imports inside functions to avoid mandatory dependencies
- Added TYPE_CHECKING guard for development time type checking only
- Implemented lazy loading mechanism with _get_async_codex_class function
- Updated AsyncCodex initialization to occur on demand rather than at module level
- Maintained backward compatibility while improving import performance
- Added test case to verify package import works without optional Codex SDK
- Updated agentscope dependency to version 2.0.4.post1 in pyproject.toml
* test(embedded): add compatibility tests for in-process ReMe embedding
- Add test suite for QwenPaw-style embedded configurations
- Verify optional defaults remain preserved in embedded configs
- Ensure in-process application API stays compatible
- Test model injection and lifecycle management compatibility
- Remove obsolete hermes agent plugin tests
- Update CLI import test to cover multiple optional SDKs
- Block claude_agent_sdk and openai_codex during import testing
* 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(codex): add authentication mode support with thread-safe logging
- Implement _CodexAuthConfig dataclass for resolved auth settings
- Add auth_mode parameter with auto/api_key/oauth options
- Separate API key and OAuth authentication flows
- Force specific login method based on auth mode
- Add explicit API key validation requirement
- Serialize concurrent logger initialization in thread lock
- Close logging handlers properly during cleanup
- Update default config with auth_mode presets for codex and codex_oauth
- Add comprehensive tests for authentication modes and concurrent logging
* feat(file_store): implement background embedding backfill and keyword index repair
- Add _after_embedding_backfill hook in FAISS local file store
- Schedule startup embedding repair without delaying component readiness
- Cancel and collect embedding backfill task during component shutdown
- Log progress at fixed percentage boundaries for long-running operations
- Process embedding backfill in configurable batch sizes with progress reporting
- Rebuild keyword index in bounded batches with detailed mismatch diagnostics
- Format stdlib logs consistently with QwenPaw console output using relative paths
- Run embedding backfill as background task that doesn't block component startup
- Add comprehensive tests for background embedding and keyword index repair scenarios
* fix(file-store): repair graph-chunk consistency on load
- Add _repair_graph_chunk_consistency method to detect and fix mismatched graph/chunk states
- Clear torn graph/chunk state when missing or orphaned chunks are detected
- Ensure keyword index sync handles empty chunks properly
- Add comprehensive tests for graph-chunk consistency scenarios
- Update test utilities to properly seed graph/chunk snapshots
- Increment version to 0.4.1.2
* feat(file_io): enhance list step response format and add comprehensive logging
- Format list output with bullet points for better readability
- Add explicit "No files found" message when directory is empty
- Include detailed timing information for file store startup phases
- Add logging for chunk loading, graph consistency checks, and keyword indexing
- Provide detailed metrics for embedding backfill operations
- Add comprehensive test coverage for empty directory scenarios
- Include batch processing statistics for embedding operations
* feat(logger): add QwenPaw logging integration with forwarding mechanism
- Introduce _ForwardToLoggerHandler to forward log records to target logger
- Add qwenpaw logger integration that forwards ReMe logs to QwenPaw handlers
- Maintain ReMe logger stability for modules that cache it at import time
- Enable QwenPaw handlers to take effect without ReMe reconfiguration
- Add comprehensive tests for stdlib forwarding to QwenPaw sinks
- Support explicit REME_DISABLE_LOGURU=false to keep original Loguru backend
- Preserve existing logging behavior when QwenPaw is not configured
* fix(file_store): serialize concurrent FAISS dump operations to prevent corruption
- Add asyncio lock to ensure only one FAISS dump operation runs at a time
- Generate unique temporary filenames using UUID tokens for atomic replacement
- Implement proper cleanup of temporary files in finally block
- Add comprehensive test to verify concurrent dumps are serialized
- Ensure atomic writes by replacing both index and idmap files together
- Prevent partial state writes during concurrent access scenarios
- Removed max_file_bytes configuration from index_update_loop, resource_watch_loop, digest_watch_loop, and reindex jobs
- Updated default.yaml to reflect simplified job configurations without file size limits
- Removed corresponding test case that validated the 20 MiB limit behavior
- Simplified watch directories and suffixes to basic configurations
- Cleaned up unnecessary commented configurations in the YAML file
* feat(benchmark): add final answer review step for evaluation
- Introduce FinalAnswerReviewStep to handle answer validation
- Add final_answer_review.jsonl dataset with 24 evaluation cases
- Include detailed reasoning and golden check results for each case
- Support various question types including temporal reasoning and preferences
- Implement time consistency checks for session references
- Add comprehensive test coverage for different evaluation scenarios
* chore(benchmark): remove longmemeval final answer review file
- Removed final_answer_review.jsonl containing 23 evaluation records
- Deleted question_id mappings with detailed reasoning for golden answers
- Removed answer correctness assessments and session time validation checks
- Cleaned up benchmark dataset used for memory evaluation testing
- Eliminated JSONL format evaluation results for temporal reasoning tasks
- Removed references to various session IDs and time-based validations
* config(default): disable shell step configuration by commenting out
- Commented out the shell step configuration in default.yaml
- Disabled asynchronous shell command execution capability
- Removed shell step from available backend operations
- Preserved traverse backend configuration unchanged
* refactor(tests): remove unused shell job test from config parser tests
- Removed test_default_config_registers_shell_job function that was no longer needed
- Kept existing test for frontmatter chunk metadata configuration
- Cleaned up test suite by removing obsolete test case
* feat(index): add file size limits and oversized file handling
- Implement max_file_bytes configuration option for content processing jobs
- Add default 20MB file size limit for background processing in default config
- Skip oversized files during auto_resource step with appropriate metadata
- Clear stale index entries when oversized files are modified
- Add size-based filtering logic to update_changes step with skip reporting
- Include file size validation in UpdateIndexStep with proper response handling
- Add comprehensive tests for oversized file scenarios in auto_resource and update_index
- Document file size limits in constants with appropriate thresholds
* chore(version): bump version to 0.4.1.1
- Update __version__ from 0.4.1.0 to 0.4.1.1 in __init__.py
* fix(index): isolate batch metadata and handle file races
- Changed dimensions property to avoid forcing provider construction
- Added _ensure_model method to construct provider on demand
- Modified __call__ to ensure model exists before use
- Updated _start to defer provider initialization
- Removed eager health check during startup
- Added compact embedding serialization with base64 encoding
- Implemented batch processing for vector search with heap-based ranking
- Added document_ids property to keyword index interface
- Updated chunk persistence to handle legacy JSON embeddings
- Optimized memory usage by avoiding materialization of metadata in document_ids
* feat(core): add shell command execution and memory status reporting
- Introduce ShellStep for executing shell commands with timeout support
- Add StatusStep to report memory estimates for stateful data components
- Register shell and status commands in default configuration
- Update documentation with new reme status and shell command capabilities
- Implement comprehensive unit tests for both new step types
- Add support for asynchronous command execution with proper error handling
* feat(config): add log_config option to suppress config loading logs
- Add log_config parameter to resolve_app_config function with default True
- Conditionally log config loading messages based on log_config flag
- Update reme.py and service_utils.py to use log_config=False for client calls
- Suppress config logging in user-facing contexts to avoid output pollution
refactor(shell): rename command parameter to cmd for clarity
- Change 'command' to 'cmd' in default.yaml configuration schema
- Rename 'timeout' to 'shell_timeout' to avoid parameter name collisions
- Update ShellStep to accept both legacy and new parameter names
- Maintain backward compatibility with existing command/timeout usage
test(shell): add comprehensive tests for shell step parameter handling
- Add test cases for new cmd and shell_timeout parameter names
- Verify legacy command and timeout parameters still work
- Test blank command rejection message updated to use cmd
- Create integration test for shell parameter payload passing
* fix(shell): ensure proper environment loading and process timeout handling
- Move load_env() call to execute before parse_args() in main function
- Add proper process group killing for timeout scenarios on POSIX systems
- Implement recursive child process termination on Windows for proper cleanup
- Change parameter name from 'timeout' to 'shell_timeout' in shell execution
- Remove support for legacy 'command' and 'timeout' parameter names
- Update test cases to verify new timeout behavior and parameter requirements
- Add comments explaining component size tracking implementation details
