- Call init_pain_storage() in Tauri .setup() so pain persistence activates on boot
- Integrate useColdStart hook into FirstConversationPrompt for auto-greeting
- Add UI mode toggle section to Settings/General (already had imports)
- Add "简洁" mode switch-back button to TopBar in professional layout
- Update SemanticSkillRouter @reserved annotation to reflect active status
New ButlerRouterMiddleware (priority 80) intercepts user messages,
classifies intent using keyword-based domain detection, and injects
routing context into the system prompt. Supports healthcare, data
report, policy compliance, and meeting coordination domains.
- New: butler_router.rs — keyword classifier + MiddlewareContext injection
- Registered in Kernel::create_middleware_chain() at priority 80
- 9 tests passing (classification + middleware integration)
P1: identity.rs get_identity() returns empty soul/instructions for agents
without explicit identity files. This prevents the default ZCLAW personality
from overriding agent_config.system_prompt. New get_identity_or_default()
method added for the DEFAULT agent.
P2: messaging.rs now uses agent_config.model.model when available, falling
back to global Kernel config. This allows per-agent model selection.
P2: agentStore.ts loadClones retries up to 3 times (300ms interval) when
getClient() returns null, handling the coordinator initialization race.
P2: agent_create Tauri command auto-populates identity files (soul +
instructions) from creation parameters, ensuring build_system_prompt()
has content for new agents.
Also fixes conversationStore upsertActiveConversation to persist generated
conversation IDs, preventing duplicate entries on new conversations.
Tool execution (ShellExec, WebFetch, etc.) had no timeout, causing the
entire streaming response to hang indefinitely when a tool fails or stalls.
Now wraps execute_tool calls in tokio::time::timeout(30s) with a graceful
error message on timeout.
Root cause: start_consumer() was called in new() before any register() calls,
so the consumer's cloned HashMap was always empty. Workers like log_operation
and record_usage were never found, causing "Unknown worker" errors.
- Add WorkerDispatcher::start() method to be called after all register()s
- Update main.rs to call dispatcher.start() after 7 workers registered
- key_pool.rs: cast cooldown_until to timestamptz for comparison with NOW()
- key_pool.rs: cast request_count to bigint (INT4→INT8) for sqlx decoding
- service.rs: cast cooldown_until to timestamptz in quota sort query
- scheduler.rs: cast last_seen_at to timestamptz in device cleanup
- totp.rs: use DateTime<Utc> instead of rfc3339 string for updated_at
- Enable multi-agent feature by default in desktop build
- Add butler delegation logic: task decomposition, expert assignment
- Add ExpertTask, DelegationResult, butler_delegate() to Director
- Add butler_delegate_task Tauri command bridging Director to frontend
- 13 Director tests passing (6 original + 7 new butler tests)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Priority 90 — runs before Compaction@100 and Memory@150.
Detects and replaces company names, money amounts, phone numbers,
emails, and ID card numbers with deterministic tokens (__ENTITY_N__).
External callers can restore originals via DataMasker::unmask().
Task 1.1 verification: OpenViking Extractor chain is confirmed working.
- Tauri commands registered ✅
- Frontend triggers after each conversation ✅
- Rust extractor with LLM + rule-based fallback ✅
- SqliteStorage persistence verified via 3 new e2e tests
All 78 tests in zclaw-growth pass with no regressions.
HIGH fixes:
- H-NS-1: Non-streaming agent_chat now builds ChatModeConfig and calls
send_message_with_chat_mode(), matching the streaming path
- H-FE-1: ClarificationCard component renders structured clarification
questions with type badge, question text, and numbered options
- H-SEM-1: SemanticSkillRouter annotated as @reserved (Phase 3 wiring)
MEDIUM fixes:
- M-SETTINGS-1: Settings menu restructured with "高级" section separator;
skills/audit/tasks/heartbeat/semantic-memory grouped under advanced
- M-MAN-1: LoopEvent→StreamChatEvent mapping completeness checklist
added as documentation comment in agent_chat_stream loop
- M-ORPH-1: Deleted orphaned Automation/ and SkillMarket/ files,
plus transitively orphaned types, hooks, and adapters
CRITICAL:
- ask_clarification now terminates Agent Loop in both run() and run_streaming()
paths, preventing the LLM from continuing after requesting user clarification
HIGH:
- SaaS relay now forwards plan_mode and subagent_enabled to backend
- GatewayClient.chatStream now supports onThinkingDelta, onSubtaskStatus,
and token-bearing onComplete — aligned with kernel-types StreamCallbacks
- ZclawStreamEvent type extended with thinking_delta, subtask_status variants
and input_tokens/output_tokens fields for token tracking via Gateway path
- C-1: Add event_sender: None to ToolContext in file_write.rs and
file_read.rs test helper functions (compilation fix)
- I-1: file_write tool now echoes content preview in output JSON,
enabling streamStore.ts artifact auto-creation pipeline to work
- S-2: Fix typo "LLM 锥应错误" → "LLM 响应错误" in loop_runner.rs
- New ask_clarification tool (crates/zclaw-runtime/src/tool/builtin/ask_clarification.rs)
with 5 clarification types: missing_info, ambiguous_requirement, approach_choice, risk_confirmation, suggestion
- Registered as built-in tool in builtin.rs
- Added clarification system prompt instructions to messaging.rs system prompt
- Fixed messaging.rs skill injection: when SkillIndexMiddleware is active,
only inject usage instructions (not full skill list), avoiding duplicate injection
- Fixed pre-existing unicode arrow character causing string literal parse error
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When SkillIndexMiddleware is active, build_system_prompt_with_skills no
longer injects the full categorized skill list. Instead it only adds
usage instructions, while the middleware handles the lightweight index.
This reduces ~2000 tokens per request for the 75-skill system.
Phase 1.0 — Butler Mode UI:
- Hide "自动化" and "技能市场" entries from sidebar navigation
- Remove AutomationPanel and SkillMarket view rendering from App.tsx
- Simplify MainViewType to only 'chat'
- Main interface is now: chat + conversation list + detail panel only
Phase 1.1 — Mode Differentiation:
- Add subagent_enabled field to ChatModeConfig (Rust), StreamChatRequest (Tauri),
gateway-client, kernel-client, saas-relay-client, and streamStore
- TaskTool is now only registered when subagent_enabled=true (Ultra mode)
- System prompt includes sub-agent delegation instructions only in Ultra mode
- Frontend transmits subagent_enabled from ChatMode config through the full stack
This connects the 4-tier mode selector (Flash/Thinking/Pro/Ultra) to actual
backend behavioral differences — Ultra mode now truly enables sub-agent delegation.
P3-03: HTML export now renders key_points in format_scene_content
P3-07: SKILL.md/YAML parser handles both single and double quotes
P3-09: auto_classify covers 20 categories with keyword matching
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- P2-18: TOTP QR code local generation via qrcode lib (no external service)
- P2-21: Suspend foreign LLM providers (OpenAI/Anthropic/Gemini) for early stage
- P3-04: get_progress() now calculates actual percentage from completed/total steps
- P3-05: saveSaaSSession calls now have .catch() error logging
- P3-06: SaaS relay chatStream passes session_key/agent_id to backend
- P3-02: Whiteboard unification plan document created
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
P1-04: GenerationPipeline hardcoded model="default" causing classroom
generation 404. Added model field to GenerationPipeline struct, passed
from kernel config via with_driver(driver, model). Static scene
generation now receives model parameter.
P1-03: LLM API concurrent 500 DATABASE_ERROR. Added transient DB error
retry (PoolTimedOut/Io) in create_relay_task with 200ms backoff.
Recommend setting ZCLAW_DB_MIN_CONNECTIONS=10 for burst resilience.
- Add IF NOT EXISTS to accounts_template_assignment ALTER COLUMN
- Add IF NOT EXISTS to webhooks CREATE INDEX statements
- Add created_at/updated_at columns + ON CONFLICT DO NOTHING to industry templates
- Bump SCHEMA_VERSION 13→14 to force migration re-run on existing DB
- 16 down SQL files in migrations/down/ for each incremental migration
- db::run_down_migrations() executes rollback files in reverse order
- migrate_down CLI task: task=migrate_down timestamp=20260402
- Initial schema and seed data excluded (would be destructive)
M1-01: Move Gemini API key from URL query param to x-goog-api-key header,
preventing key leakage in logs/proxy/telemetry (matches Anthropic/OpenAI pattern)
M1-03/M1-04: Replace Mutex .unwrap() with .unwrap_or_else(|e| e.into_inner())
in MemoryMiddleware and LoopGuardMiddleware — recovers from poison
instead of panicking async runtime
M2-08: Add input validation to agent_create — reject empty names,
out-of-range temperature (0-2), and zero max_tokens
M11-06: Replace Date.now() message ID with crypto.randomUUID()
to prevent collisions in classroom chat
- cache: insert-then-retain pattern avoids empty-window race during refresh
- relay: manage_task_status flag for proper failover state transitions
- relay: retry_task re-resolves model groups instead of blind provider reuse
- relay: filter empty-member groups from available models list
- relay: quota cache stale entry cleanup (TTL 5x expiry)
- error: from_sqlx_unique helper for 409 vs 500 distinction
- model_config: unique constraint handling, duplicate member check
- model_config: failover_strategy whitelist, model_id vs group name conflict check
- model_config: group-scoped member removal with group_id validation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sprint 2: 产品体验打磨 + 行业模板
- Create PipelineResultPreview component with tab-based output switching
- Connect workflow/hand messages to PresentationContainer in ChatArea
- Add auto-trigger first Hand after onboarding (industry-specific queries)
- Seed 3 industry agent templates (education, healthcare, design-shantou)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Model Groups provide logical model names that map to multiple physical
models across providers, with automatic failover when one provider's
key pool is exhausted.
Backend:
- New model_groups + model_group_members tables with FK constraints
- Full CRUD API (7 endpoints) with admin-only write permissions
- Cache layer: DashMap-backed CachedModelGroup with load_from_db
- Relay integration: ModelResolution enum for Direct/Group routing
- Cross-provider failover: sort_candidates_by_quota + OnceLock cache
- Relay failure path: record failure usage + relay_dequeue (fixes
queue counter leak that caused connection pool exhaustion)
- add_group_member: validate model_id exists before insert
Frontend:
- saas-relay-client: accept getModel() callback for dynamic model selection
- connectionStore: prefer conversationStore.currentModel over first available
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Kernel orchestration bridge: execute_orchestration, auto_compose_skills,
validate_orchestration methods on Kernel struct
- True parallel execution: replace sequential for-loop with tokio::JoinSet
for concurrent node execution within parallel groups
- Tauri commands: orchestration_execute (auto-compose or pre-defined graph),
orchestration_validate (dry-run validation)
- Full type conversions: OrchestrationRequest/Response with camelCase serde
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- SemanticSkillRouter: add RuntimeLlmIntent trait and with_llm_fallback() builder
- route(): call LLM fallback when TF-IDF/embedding confidence < threshold
- CJK tokenization: generate bigrams for Chinese/Japanese/Korean text
- Fix: previous tokenizer treated entire CJK string as one huge token
- SemanticSkillRouter: add RuntimeLlmIntent trait and with_llm_fallback() builder
- route(): call LLM fallback when TF-IDF/embedding confidence < threshold
- CJK tokenization: generate bigrams for Chinese/Japanese/Korean text
- Fix: previous tokenizer treated entire CJK string as one huge token
- LlmSkillFallback: concrete RuntimeLlmIntent using LlmDriver
- Asks LLM to pick best skill from ambiguous candidates list
- Parses structured JSON response from LLM output
- Includes tests for LLM fallback and CJK tokenization
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- get_messages_paginated(session_id, limit, offset) for batch loading
- count_messages(session_id) for total count queries
- Enables frontend to load messages progressively instead of all-at-once
- Schema: migrations now execute ALTER TABLE ADD COLUMN for state/message_count
- MemoryStore: add update_agent_runtime() and list_agents_with_runtime()
- Registry: add register_with_runtime() to accept persisted state/message_count
- Kernel boot: restore agents with their persisted state (not always Running)
- Kernel shutdown: persist all agent states/message_counts before terminating
Agents that were suspended stay suspended after restart. Message counts
survive restarts instead of resetting to 0.
- Add effective_importance() with exponential time decay (30-day half-life)
and access count boost for fair scoring of stale vs fresh memories
- Add SqliteStorage::decay_memories() for periodic maintenance:
reduces stored importance per interval, archives (deletes) below threshold
- Update find() scoring to use time-decayed importance in sort
- Add DecayResult type and effective_importance re-export in lib.rs
- Remove dead frontend active-learning.ts (370 lines, zero imports)
- saas test harness: align WorkerDispatcher::new and AppState::new
signatures with SpawnLimiter addition and init_db(&DatabaseConfig)
- growth sqlite: add CJK fallback (LIKE-based) when FTS5 unicode61
tokenizer fails on Chinese queries (unicode61 doesn't index CJK)
