10 commits

Author	SHA1	Message	Date
jinliyl	618e8cec66	feat: add entry-point plugin system and extract Auto Fin (#459) ... * feat: add entry-point plugin system * fix: harden plugin config and client loading * docs(workflow): add detailed manual for publishing reme-auto-fin to PyPI - Provide step-by-step instructions for updating project.version and merging branches - Explain dependency verification for reme-ai on PyPI during build - Specify requirements for GitHub Actions secret configuration and version uniqueness - Describe manual workflow triggering and input of version number - Recommend publishing order for related projects - Clarify that only manual dispatch triggers publishing, no automatic triggers on push or tag * feat: support plugin-defined component types * refactor: simplify plugin configuration * fix: isolate plugin loading and defer client fallback * refactor: freeze built-in component registry * fix: isolate config entry point loading * fix: complete auto-fin package metadata	2026-08-19 17:23:23 +08:00
jinliyl	e05b201da9	feat(backend): improve workspace support for web clients (#420) ... * feat(backend): improve workspace support for web clients * fix(config): preserve the default workspace directory * chore(reme): bump version to 0.4.1.5 - Update __version__ from 0.4.1.4 to 0.4.1.5 in initialization file * fix(chat): disable builtin tools in read-only mode * fix(agent): make builtin tools opt-in * fix(list): tolerate files removed during mtime sort * fix(chat): expose complete read-only job set	2026-08-07 23:52:56 +08:00
jinliyl	9218a2d0e3	refactor: derive dialog paths from session_dir (#421) ... * refactor: derive dialog paths from session directory * fix: normalize configured session paths * fix: align dialog watch paths with writers * fix: reject absolute session directories	2026-08-06 17:07:14 +08:00
jinliyl	f31daf1949	Revert "feat(backend): improve workspace support for web clients (#417)" (#419) ... This reverts commit b00eb0a9ea.	2026-08-05 23:17:08 +08:00
jinliyl	b00eb0a9ea	feat(backend): improve workspace support for web clients (#417)	2026-08-05 23:07:05 +08:00
xyf2020	7b1da5a9ee	feat(benchmark): add BEAM & restructure LongMemEval evaluation framework (#375) ... * 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>	2026-07-21 19:09:50 +08:00
jinliyl	e7d44f6f3b	refactor(agent): unify agent subprocess env, sessions, skills, and MCP/service jobs (#382) ... * 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	2026-07-20 23:52:14 +08:00
jinliyl	38cf16071b	refactor(embedding): update embedding model initialization and session storage paths (#329) ... * refactor(embedding): update embedding model initialization and session storage paths - Remove unused inspect import from as_embedding module - Pass dimensions directly to embedding model constructor instead of using parameters - Update session state file paths to use mem_session directory instead of resource - Add mem_session_dir configuration option to application config schema - Update workspace directory creation to include new mem_session directory - Change AgentScope and Claude Code session paths to use mem_session directory - Move embedding dimensions from parameters to top-level configuration - Update AgentScope dependency version from 2.0.3 to 2.0.4 - Update integration tests to reflect new session file location paths * chore(version): bump version to 0.4.0.8 - Update __version__ from 0.4.0.7 to 0.4.0.8 in __init__.py	2026-07-08 12:30:46 +09:00
Sen Huang	e31db5fe19	docs: rename vault_dir to workspace_dir in documentation and examples (#286) ... * docs: rename vault_dir to workspace_dir in documentation and examples * refactor(extract): format long method call across multiple lines * refactor(extract): format system prompt parameters for better readability	2026-06-22 16:58:57 +08:00
jinliyl	206a53e5ed	init: reme version 0.4.0 (#284)	2026-06-22 15:41:19 +08:00

Renamed from reme4/schema/application_config.py (Browse further)