* refactor(packaging): reorganize published packages
* fix(packaging): install AgentScope extra in wheel smoke
* docs: align package guides and documentation site
* ci(workflow): add core dependency verification step in Python package build
- Add a workflow step to verify released core dependencies by installing the wheel with core extras
- Assert the presence of the static index.html file to ensure proper package contents
- Create and use a temporary virtual environment for isolation during verification
- Keep existing artifacts upload step intact and conditional on inputs.upload_artifacts flag
* fix(ci): update package installation dependencies in Windows workflow
- Change pip install from editable reme_studio and core to only dev and as extras
- Remove installation of reme_studio and core to streamline dependency setup
- Ensure Windows CI uses the correct extras for testing environment
* fix(tests): add missing commas in toml file reads in package version tests
- Added trailing commas in the tomllib.loads calls for auto-fin and daily_paper configs
- Ensured consistent syntax to prevent potential tuple misinterpretation
- Improved readability and correctness of the test setup code
* fix(packaging): protect qwenpaw releases and test Studio health
* feat: refine local-first research workflows
* fix: delegate structured output tool choice
* refactor(auto-fin): fetch and filter rolling CLS news
* fix(auto-fin): keep imports portable across platforms
* feat(auto-fin): expose CLS fetch controls
* fix(auto-fin): propagate configurable news window
* feat(auto_fin): normalize hybrid wikilinks in report body
- Add _normalize_hybrid_wikilinks method to remove redundant Markdown destinations
- Use regex to identify hybrid wikilinks with optional destinations
- Replace redundant destinations with simpler wikilink format for clarity
- Ensure normalization is failure-safe with exception handling and logging
- Update report body normalization process to apply hybrid wikilink fix
- Add unit tests to verify correct normalization and failure safety behavior
* fix(dream): serialize integration with application-wide asyncio lock
- Add application-wide asyncio.Lock to serialize digest writes during integration
- Update _snapshot_digest to capture metadata per bucket
- Validate bucket association when recovering from file changes
- Add tests ensuring recovery only from the correct bucket
- Add tests confirming integration lock is shared across application context
- Enhance strict topic YAML loading validation in dream utils
- Add tests for strict topic loading rejecting invalid or lossy fields
* fix(cookbook): enable configurable job_tools for digest and merge steps
- Update daily_cookbook.yaml to add job_tools: [memory_search, read] in digest steps
- Modify DailyPaperDigestStep to read job_tools from kwargs instead of fixed list
- Modify AutoFinMergeStep to similarly read job_tools from kwargs
- Update tests to pass job_tools explicitly when invoking these steps
- Remove hardcoded _TOOLS constants and replace with dynamic job_tools handling
* fix: retry incomplete dream receipts
* perf(pdf): increase max PDF pages limit from 20 to 35
- Updated configuration max_pdf_pages from 20 to 35 in daily_cookbook.yaml
- Modified code to extract up to 35 pages instead of 20 in analyze.py
- Updated README and README_ZH to document the increased max_pdf_pages
- Adjusted unit test assertions to reflect new max_pdf_pages limit of 35
* fix memory integration and daily paper links
* docs clarify cookbook tool usage
* feat(bench): adding eval adapter for proactiveness on Pi-Bench
* Revise README for π-Bench evaluation suite
Updated the README to reflect the new project name and description.
* fix(bench): refining pi-bench scripts according to cr comments
* fix(bench): restore agent builtin tools in prebuilt toolkit
* chore(benchmark): isolate dataset/workspaces/results per benchmark
- Move shared benchmark/{datasets,memory_workspaces,results} into per-benchmark subdirs benchmark/<name>/{dataset,workspaces,results}
- Update beam/longmemeval config.yaml and run.py path defaults
- Relocate longmemeval download.py to benchmark/longmemeval/ (downloads into dataset/ subdir); inline dataset download docs into README
- Update .gitignore: benchmark/*/{dataset,workspaces,results}/
- Move result-{beam,longmemeval}.md to benchmark/results_md/ and drop result- prefix; update README links
- Fix stale path refs in llm_judge.py and logs/demo_search_format.py
* feat(benchmark): add read tool to agentic answer and update BEAM results
- 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)
* feat(auto_memory): add source line-number markers for note traceability
- 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
* feat(beam): enhance auto_memory with source markers and pilot ingest tooling
* refactor(beam): rename max_chunk_words to max_segment_words, drop one-off pilot scripts
* feat: add CompressorStep and search_v2 dual-mode session compression
- 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
* refactor(compress): relax session compression to lenient format-preserving strategy and update LME results
* refactor(benchmark): make session compression config-driven via compress_session flag
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.
* feat(lme/auto_memory): add source attribution markers with line numbers
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.
* feat: improve agentic answer prompt and update beam 100K results
- Strengthen abstention rule: prohibit extrapolation from related but
non-direct evidence
- Add multi-angle search after preliminary answer to check for
conflicting/supplementary/updated information
- Add max-iteration fallback to 'Information not found'
- Update beam.md with 100K results (agentscope 2.0.4.post1, from scratch)
including per-type token consumption and memory construction stats
- config.yaml: 100K dataset, 20 workers for BEAM evaluation
- run.py: add memory construction token usage tracking (default agent)
- Overall: 0.635 → 0.654 (+0.019), contradiction_resolution: 0.338 → 0.478
(+0.140), abstention: 0.500 → 0.525 (+0.025)
* feat(read): add session-aware formatting for read tool and update BEAM eval
- Add truncate_session_output in _file_io.py to render jsonl session
lines as [speaker @ time] content before byte-budget truncation
- Add read_step_format_session flag to ReadStep, honoring injected
job kwargs (precedence) and YAML fallback
- Inject read_step_format_session=True into BaseAgenticAnswerStep
so agentic answer reads render session transcripts human-readably
- Refine BEAM agentic_answer prompt: continue multi-angle search
after preliminary answer, forbid fabrication/extrapolation
- Update BEAM config to 1M variant and add sequential 100K-eval /
1M-build shell script
- Refresh benchmark/results_md/beam.md with latest results
* chore(config): disable expand_links in beam and lme search_v2 configs
* refactor(beam): drop one-off sequential 100K-eval-then-1M-build script
* fix(benchmark): add compressor job to beam config and fix BEAM clone instructions
- 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)
* fix(steps): guard compressor exceptions and fix ReadStep boolean override
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.
* fix(search_v2): use existence check for strict_date_filter boolean override
Replace 'context_value or yaml_value' with an existence-based check so
that a runtime-injected False can explicitly disable a YAML-true
strict_date_filter flag, consistent with the read_step_format_session fix.
* refactor(search): simplify strict_date_filter fallback to truthiness-or
* style(test): rename unused param to satisfy pylint W0613
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
* 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>
* 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(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>
* refactor(search): replace time module with datetime for timestamp generation
- Removed unused time import
- Added static method _now_ts using datetime.timestamp
- Updated clock parameter to use _now_ts method instead of time.time
- Maintained same timestamp precision and functionality
* test(http): add tests for HTTP client display formatting
- Add test for default metadata hiding behavior in CLI output
- Add test for metadata display when show_metadata is enabled
- Verify _format_for_display method correctly formats response text
- Test both success case and metadata inclusion scenarios
* chore(build): remove longmemeval from gitignore
- Removed longmemeval directory from gitignore list
- Kept evaluation and datasets directories in ignore list
- Updated gitignore configuration for proper version control
* 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
* 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.
* refactor(components): extract shared component state into mixin
- Introduce ComponentMixin class with shared state for components and steps
- Move identity, config, and vault path functionality to ComponentMixin
- Update BaseComponent to inherit from ComponentMixin
- Update BaseStep to inherit from ComponentMixin
- Consolidate vault path helper methods in ComponentMixin
- Remove duplicate vault path implementations from BaseComponent and BaseStep
- Add ComponentMixin to components module exports
* refactor(file_io): implement path locks cache eviction mechanism
- Add _PATH_LOCKS_MAX constant set to 1024 for cache size limit
- Implement cache eviction logic when locks exceed maximum capacity
- Remove half of unlocked entries when cache limit is reached
- Use list comprehension to identify unlocked locks for removal
- Maintain existing path normalization and locking behavior
fix(edit): correct method call from public to private fail method
- Change self.fail to self._fail for internal error handling
- Maintain consistent private method usage within class
fix(mcp_client): change pop to get for optional command and args
- Replace kwargs.pop with kwargs.get to avoid removing keys
- Preserve original kwargs dictionary contents
- Maintain default empty string and list values
feat(reme): add client backend validation with error raising
- Check if client_cls is None before instantiation
- Raise ValueError with descriptive message for unknown backends
- Provide clear error feedback for invalid backend configurations
* fix(components): move directory creation to start method
- Moved component_metadata_path.mkdir call from __init__ to _start in base_keyword_index
- Moved component_metadata_path.mkdir call from __init__ to _start in local_file_graph
- Moved component_metadata_path.mkdir call from __init__ to _start in local_file_store
- Ensures directory creation happens after component initialization
- Prevents potential issues with path creation during object construction
* fix(steps): replace assertions with runtime errors for app_context validation
- Replace assert statements with explicit RuntimeError exceptions when app_context is None
- Add descriptive error messages for better debugging when resolving components
- Replace assert in resolve_component method with proper exception handling
- Replace assert in get_file_parser method with proper exception handling
- Maintain same functionality while improving error reporting clarity
* refactor(file_io): split file IO utilities into modular components
- Move daily note helpers to separate _daily_index module
- Extract path validation and resolution to new _path module
- Remove unused code and imports from _file_io module
- Update import statements across affected modules
- Introduce WikilinkHandler utility for link parsing
- Replace regex-based link extraction with WikilinkHandler
- Add integration JSONL files to gitignore
- Consolidate file locking mechanism in _file_io module
* style(formatter): fix spacing issues in file IO and chunked file parser
- Fixed whitespace around colon in slice notation in file_io.py
- Corrected spacing around colon in slice notation in chunked_file_parser.py
- Applied consistent formatting for array slicing operations
- Improved code readability by standardizing space placement in ranges
* refactor(steps): replace property-based component resolution with Ref descriptor
- Introduce Ref descriptor class for lazy component dependency resolution
- Replace _resolve method and individual properties with Ref descriptors
- Add as_llm, as_llm_formatter, as_token_counter, file_store, and embedding Ref attributes
- Remove legacy property methods and resolve logic from BaseStep
- Add cache clearing mechanism for Ref values during step calls
- Update UpdateCatalogStep to use Ref instead of property-based resolution
* feat: rename working_dir to vault_dir and update documentation
- Rename working_dir to vault_dir across the application
- Update documentation to reflect vault_dir instead of working_dir
- Change FileFrontMatter title field to name field
- Update .gitignore to include vault directory
- Modify file path descriptions to reference vault instead of working_dir
- Update related configuration and property names accordingly
* refactor(steps): rename working_path to vault_path in CRUD operations
- Rename parameter from `working_path` to `vault_path` in `resolve_path` function
- Update all usages in append, edit, read, and write steps to use `self.vault_path`
- Update documentation comments to reflect the new parameter name
- Update docstring in read.py to mention `vault_dir` instead of `vault`
test(chunked_file_parser): update frontmatter field from title to name
- Change frontmatter field from `title` to `name` in test cases
- Update comment in background steps test to reference `vault_path` instead of `working_path`
* refactor(schema): remove unused ResourceEntry import
* feat(file_graph): add link scope filtering to get_inlinks/get_outlinks
* feat(file-store): add scope parameter to link methods
* feat(seekdb): add Seekdb file and vector stores with pyseekdb>=1.2.0
* refactor(seekdb): add pyseekdb_conn and remote-only host/port config
* refactor(embedding): remove env fallbacks from BaseEmbeddingModel; pass credentials in tests
* refactor(seekdb): drop tenant from client kwargs; default database test and empty password
* fix(deps): gate pyseekdb to Python >=3.11 for CI 3.10 compatibility
* fix(seekdb): satisfy pre-commit pylint and formatting for seekdb stores
* feat: add Neo4j file graph support and markdown parser with wikilink extraction
- Add Neo4jFileGraph implementation for property-graph storage with
virtual/real node handling and link management
- Introduce LinkedFileParser for markdown files with frontmatter,
wikilink graph extraction, and full-skeleton chunking
- Update pyproject.toml to include pyyaml, mistletoe, and neo4j
dependencies
- Modify .gitignore to exclude /vault and structure.md
- Change reme CLI entry point from reme_ai.main to remecli.reme
- Register new neo4j and md components in respective registries
* refactor(file-graph): add chunk_ids support to Neo4jFileGraph
Add chunk_ids field to File node properties in Neo4jFileGraph to
enable better content chunk tracking and management.
BREAKING CHANGE: File node schema now includes chunk_ids property
which may affect existing integrations.
feat(parser): implement wikilink resolution logic
Move path resolution logic from utils/path_resolver to
linked_file_parser module and enhance wikilink resolution with
folder-note rule support and improved error handling.
fix(tests): update test assertions and variable names
Update test cases to reflect changes in data structures and
variable naming conventions across various components.
chore(config): update package entry point reference
Change reme CLI entry point from remecli.reme:main to
reme_ai.reme:main in pyproject.toml.
refactor(utils): remove deprecated path_resolver module
Remove the old path_resolver utility module as its functionality
has been moved to linked_file_parser.
docs(file-graph): update Neo4jFileGraph documentation
Update class docstrings and comments to reflect new chunk_ids
property and other structural changes.
style(formatting): adjust code formatting and line breaks
Minor formatting improvements including line length optimization
and consistent spacing adjustments throughout the codebase.
* fix(pyproject.toml): correct entry point for reme command
Change the entry point from "reme_ai.reme:main" to "reme_ai.main:main"
to fix the module reference for the reme command in project scripts.
- Added detailed information about the project's architecture, installation, and usage
- Included sections on task memory and personal memory management
- Provided examples and use cases for the framework
- Updated project metadata and version information
- Add BuildQueryOp to construct query for memory retrieval-Implement MergeMemoryOp to combine retrieved memories
- Create RecallVectorStoreOp to fetch memories from vector store
- Develop memory representation and conversion methods
- Establish initial project structure and dependencies
