_send_webhook:
- Dedupe: skip if alert.webhook_sent is already True (guards against
repeated check_alerts ticks re-sending the same alert).
- Retry: up to 3 attempts with exponential backoff (1s, 2s) before
giving up. webhook_sent=True is persisted only on success.
Two xfail guards in test_openclaw_client_and_webhook now pass (876/4 xfail).
T4 auto-scale boundary: busy/total == 0.8 (exact threshold) must not fire
T4 auto-scale oscillation: repeated flips across 0.8 must respect MIN/MAX bounds
T9 stuck-detection critical edge: strict '<' semantics at 10min threshold
T9 detect_stuck_crons ignores non-busy: idle/stuck crons excluded even if stale
- Add metrics.py with pool utilization, task backlog, stuck rate, avg processing time, eval completion rate
- Add alerts.py with alert rules (pool utilization > 90%, task backlog > 50, stuck rate > 10%)
- Implement alert history and webhook notifications
- Add metrics and alerts APIs
- Add database migration for alert history table
- Add 11 unit tests for metrics, 10 unit tests for alerts, 8 integration tests
- Update migration tests to include new alert history table
All 853 tests passing.
- Add config_snapshot.py with save/list/get/compare functions
- Auto-save snapshots on eval creation and plan submission
- Implement snapshot query APIs (list, get single)
- Implement snapshot comparison API (diff two snapshots)
- Add 8 unit tests and 7 integration tests
Snapshots track config changes over time (created/plan_submitted/config_updated).
All 813 tests passing.
- Add OpenClawClient wrapping CLI commands (create/delete/list crons)
- Implement pool initialization, scale up/down, auto-scaling logic
- Implement cron state sync and stuck cron detection
- Add pool status and manual scaling APIs
- Add 13 unit tests and 5 integration tests
Pool automatically scales between 5-20 crons based on load.
All 778 tests passing.
Ticket 05 asks the judge review conclusions to flow into the report
verbatim; the aggregation silently dropped good/acceptable dimensions.
Collect every finding sorted poor-first and color drawer tags by rating.
Exploration sessions aggregate into a single exploration summary
(session counts, goal-achievement rate, issue lists from experience
records, judge conclusions when reviewed) that feeds three exits:
the campaign report gains an exploration dimension, the v0.7 analysis
stage-two input gains the summary (stats only, never full dialogues),
and the Markdown export appends a findings appendix after analysis and
comparison. With no exploration data every output stays unchanged.
Adds unit tests for the campaign report beyond value correctness:
cancellation exclusion from denominators (ADR-0004), window clamping,
naive/aware timestamp equivalence, plus schema validation of the report
dict (key sets, types, rate bounds, contiguous buckets, capability sort,
ISO-UTC timestamps) and consistency with the single aggregate_runs seam.
After an exploration session closes, the platform samples up to 3
conversation rounds and runs an independent judge-role review through
the v0.7 ChatClient seam, persisting quality-dimension conclusions
(attitude, professionalism, hallucination) into the session's
judge_review. The review runs as a background task: failures are
recorded without touching session state or the first-hand experience
record, and a missing model config skips silently.
v0.9 ticket 02. Campaigns now carry an exploration seed set (seed
personas × seed goals — the comparability unit for exploratory
evaluation) and an optional budget override, stored as JSON columns
isomorphic to plan. Empty seeds normalize to null, marking the campaign
as opted out of exploration. resolve_budget merges per-field overrides
into platform defaults; enforcement stays server-side. The create form
gains seed lists and budget inputs (minutes → seconds), submitting null
when left empty.
The scheduler loop enqueues the analysis task when a realtime campaign
completes; accelerated or cancelled campaigns and a missing analysis
model skip silently. The campaign markdown export appends the analysis
appendix (overall, problems, narratives, suggestions) when a completed
analysis exists.
Add the analysis role's execution path: a two-phase orchestration
(per-scenario diagnosis gathered in parallel, then a synthesis pass)
that reads the existing campaign report aggregation plus capped failure
samples, validates the LLM's JSON against the report schema, and strips
fabricated run/scenario references before persisting. Results upsert one
row per campaign (generating/completed/failed) with the model config
snapshot; GET/POST /api/campaigns/{id}/analysis expose the state machine,
guarding non-terminal campaigns and missing analysis models with 400s.
Campaigns can pin an analysis model config instead of following the
global analysis default. Creation validates the referenced config
exists (400 otherwise); the create form offers enabled chat configs
with the global default as the fallback option.
Introduce ModelPurpose.ANALYSIS and a globally-unique is_analysis_default
marker on chat model configs so campaign analysis can resolve its model.
Service rejects disabled or non-chat configs; repo clears the previous
holder on set. Documented the analysis role in CONTEXT.md.
build_campaign_timeline flattens a campaign's child Runs into offset-sorted
per-Run entries (distinct from the report's 12-bucket aggregation), reusing a
shared _run_window_offset口径 so both views place a run identically. Exposes
GET /campaigns/{id}/timeline and the api.ts type/call. (v0.6 ticket 06)
Campaign.summary was a bare Optional[dict] while RunSummary is a typed VO —
scheduler state (spawned_indices/errors) flowed untyped through
set_/get_summary. Introduce CampaignSummary + SchedulerState (extra=allow,
validate_assignment), mirroring RunSummary; campaign_runner reads/writes the
VO. Also converge the ~10 repeated JSON column get/set pairs onto
_json_dumps/_json_loads helpers, unifying ensure_ascii=False and fixing the
set_modalities ensure_ascii=True trap.
Three near-identical Turn(...)+save_turn blocks (send-fail / poll-except /
happy path) collapse into one _persist_turn helper differing only by the
optional fields set. The期望→隐式规则 translation becomes a pure,
unit-testable derive_implicit_rules seam (CONTEXT: 期望与规则叠加生效),
and the rule_type→ModelPurpose map is hoisted to a module constant.
