- M.2: ForgettingCurve, MemoryDecay, MemoryReinforcement (selective forgetting)
- M.3: DailyDigestGenerator, ReminderScheduler, ProactiveInformer (proactive reminders)
- M.4: MemoryExtractor with LLM-based memory extraction from conversations
- M.5: MemoryRecallInjector with token budget control for prompt injection
- All phases include comprehensive unit tests (109 tests passing)
- Updated checklist.md to mark all tasks complete
Align the L3 graph, agent service, and sync tool shims on one canonical continuity contract so clarification resumes and persisted snapshots behave consistently. Add targeted regressions and hardening notes covering system-message coalescing, async bridge usage, and continuity rehydration.
Introduce the backend pieces for brain memory ingestion, routing, and
system telemetry so the new knowledge workflows can project data into a
brain view. The supporting tests lock in the new behavior and keep the
expanded backend surface stable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Normalize uploaded documents into structured markdown, add clearer parser
errors for missing dependencies, and cover the ingestion flow with
backend tests. This also replaces deprecated UTC timestamp helpers in
the touched backend paths so the knowledge pipeline stays warning-free.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implemented a complete log system for tracking:
- Agent logs:智能体调用
- System logs: 系统运行
- Chat logs: 问答对话
Backend:
- Log model with type, level, user_id, message, source, duration_ms
- LogService with methods for logging and querying
- API endpoints: GET /api/logs, GET /api/logs/stats, GET /api/logs/recent
Frontend:
- LogView.vue with filters, stats, pagination, auto-refresh
- log.ts API client with TypeScript interfaces
- Added "运行日志" nav item to sidebar
