feat(telegram): live editable task tracker (Variant B+), replace 15-message spam

Replace the ~15 separate Telegram messages per task (agent start/finish, stage
transition, QG-pending, tech noise) with ONE live tracker message edited in
place (editMessageText) on every stage transition. Only attention-worthy events
are still sent as SEPARATE, notifying messages: approve-gate, deploy-fail,
agent-fail, task error.

- db.py: idempotent ALTERs — tasks.tracker_message_id, tasks.title,
  tasks.brd_review_started_at/ended_at, agent_runs.model. Helpers for
  tracker message_id + BRD-review clock.
- usage.py: short_model_name() (strip provider/claude- prefix); parse model
  from result-JSON modelUsage; record_usage persists model.
- notifications.py: render_task_tracker(task_id) (stateless render from
  agent_runs), update_task_tracker (sendMessage->store id->editMessageText with
  fallback to a new message, silent), edit_telegram(). Per-stage line
  in↓/out↑·cost·model, ⏸️ Ревью БРД (human time), 💰 totals, finish block
  (⏱️ wall/agents/yours, 🔗 PR · 📦). notify_* are now tracker-only/log-only
  except the four alerts.
- stage_engine.py: stamp brd_review_ended on analysis->architecture advance.
- webhooks/plane.py: persist task title on creation.
- tests/test_telegram_tracker.py: render, short_model_name, send/edit/fallback,
  separate-vs-silent alert behavior.
This commit is contained in:
dev-bot
2026-06-04 11:42:46 +03:00
parent 2801983d7b
commit 9a0298de9d
6 changed files with 893 additions and 35 deletions

View File

@@ -79,9 +79,60 @@ def parse_usage_from_text(text: str) -> dict | None:
usage.get("cache_creation_input_tokens", usage.get("cache_creation_tokens"))
),
"cost_usd": _float(cost),
# Telegram live tracker: the model the run actually used. claude
# --output-format json reports it under modelUsage (a dict keyed by the
# full model id) and/or a top-level "model" field. We keep the FULL name
# here; short_model_name() trims it for the tracker. None when unknown.
"model": _extract_model(candidate),
}
def _extract_model(candidate: dict) -> str | None:
"""Best-effort: pull the model id out of a claude result JSON object.
Prefers modelUsage (a dict keyed by full model ids, e.g.
{"claude-opus-4-8": {...}}) and returns the key with the most output
tokens; falls back to a top-level "model" string. Never raises -> None.
"""
try:
mu = candidate.get("modelUsage")
if isinstance(mu, dict) and mu:
def _out(v):
try:
return int((v or {}).get("outputTokens", 0))
except (TypeError, ValueError, AttributeError):
return 0
best = max(mu.items(), key=lambda kv: _out(kv[1]))
if best and best[0]:
return str(best[0])
model = candidate.get("model")
if isinstance(model, str) and model:
return model
except Exception:
pass
return None
def short_model_name(full: str | None) -> str:
"""Trim a full model id to a short tag for the tracker.
'tokenator/claude-opus-4-8' -> 'opus-4-8'
'vibecode/claude-sonnet-4.6' -> 'sonnet-4.6'
'claude-opus-4-8' -> 'opus-4-8'
Returns '' when full is falsy so callers can omit the ' · <model>' suffix.
"""
if not full:
return ""
name = str(full).strip()
# Drop any provider prefix up to and including the last '/'.
if "/" in name:
name = name.rsplit("/", 1)[-1]
# Drop a leading 'claude-' marketing prefix.
if name.startswith("claude-"):
name = name[len("claude-"):]
return name
def _extract_last_json_object(text: str) -> dict | None:
"""Return the last balanced top-level JSON object in `text` that parses.
@@ -157,13 +208,15 @@ def record_usage(run_id: int, usage: dict | None):
try:
conn.execute(
"UPDATE agent_runs SET input_tokens=?, output_tokens=?, "
"cache_read_tokens=?, cache_creation_tokens=?, cost_usd=? WHERE id=?",
"cache_read_tokens=?, cache_creation_tokens=?, cost_usd=?, "
"model=COALESCE(?, model) WHERE id=?",
(
usage.get("input_tokens"),
usage.get("output_tokens"),
usage.get("cache_read_tokens"),
usage.get("cache_creation_tokens"),
usage.get("cost_usd"),
usage.get("model"),
run_id,
),
)