Files
multica/server/pkg/db/generated/runtime_usage.sql.go
Naiyuan Qing ec4b8e9c1a feat(agent-status): redesign agent runtime status as two orthogonal dimensions
Splits the conflated 5-state agent presence into two independent axes:

- AgentAvailability (3-state): online / unstable / offline — drives the
  dot indicator everywhere a dot appears. Pure runtime reachability;
  never sticky-red because of a past task outcome.

- LastTaskState (5-state): running / completed / failed / cancelled /
  idle — surfaced as text + icon on focused surfaces (hover card,
  agent detail page, agents list, runtime detail). Never colours the dot.

Major changes:

* Domain layer: AgentPresence union → AgentAvailability + LastTaskState.
  derive-presence split into deriveAgentAvailability + deriveLastTaskState
  + deriveAgentPresenceDetail orchestrator. Tests reorganised into three
  groups (availability invariants, last-task invariants, composition).

* Visual config: presenceConfig (5 entries) → availabilityConfig (3) +
  taskStateConfig (5). availabilityOrder + lastTaskOrder for filter chips.

* Workspace-level presence prefetch: new useWorkspacePresencePrefetch
  hook + WorkspacePresencePrefetch mount component, wired into
  DashboardLayout (web) and WorkspaceRouteLayout (desktop). Hover cards
  render synchronously with no skeleton flash on first hover.

* ActorAvatar hover: flipped default — disableHoverCard removed,
  enableHoverCard added (default false). Opt-in at ~14 decision-moment
  surfaces; pickers / decoration sub-chips stay plain. Status dot
  decoupled (showStatusDot prop) so picker rows can show presence
  without nesting popovers.

* Hover cards: AgentProfileCard simplified — availability dot only,
  Detail link top-right (logs live on the detail page). New
  MemberProfileCard mirrors the structure: name + role + email +
  top-2 owned agents (sorted by 30d run count) with click-through to
  agent detail.

* Agents list: split Status into two columns — availability (3-color
  dot + label) and Last run (task icon + label, optional running
  counts). Two independent filter chip groups (Status + Last run);
  combination acts as intersection ("online + failed" finds broken-
  but-alive agents).

* Other UI surfaces (issue list/board/detail, comments, autopilots,
  projects, runtimes, mention autocomplete, subscribers picker)
  updated to the new dot semantics; status dot now strictly 3-color.

Server changes accompany the client redesign — workspace-wide
agent-task-snapshot endpoint, runtime usage queries, etc. — to feed
the derive layer with the data it needs.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 18:54:00 +08:00

241 lines
7.4 KiB
Go

// Code generated by sqlc. DO NOT EDIT.
// versions:
// sqlc v1.30.0
// source: runtime_usage.sql
package db
import (
"context"
"github.com/jackc/pgx/v5/pgtype"
)
const getRuntimeTaskHourlyActivity = `-- name: GetRuntimeTaskHourlyActivity :many
SELECT EXTRACT(HOUR FROM started_at)::int AS hour, COUNT(*)::int AS count
FROM agent_task_queue
WHERE runtime_id = $1 AND started_at IS NOT NULL
GROUP BY hour
ORDER BY hour
`
type GetRuntimeTaskHourlyActivityRow struct {
Hour int32 `json:"hour"`
Count int32 `json:"count"`
}
func (q *Queries) GetRuntimeTaskHourlyActivity(ctx context.Context, runtimeID pgtype.UUID) ([]GetRuntimeTaskHourlyActivityRow, error) {
rows, err := q.db.Query(ctx, getRuntimeTaskHourlyActivity, runtimeID)
if err != nil {
return nil, err
}
defer rows.Close()
items := []GetRuntimeTaskHourlyActivityRow{}
for rows.Next() {
var i GetRuntimeTaskHourlyActivityRow
if err := rows.Scan(&i.Hour, &i.Count); err != nil {
return nil, err
}
items = append(items, i)
}
if err := rows.Err(); err != nil {
return nil, err
}
return items, nil
}
const getRuntimeUsageByHour = `-- name: GetRuntimeUsageByHour :many
SELECT
EXTRACT(HOUR FROM tu.created_at)::int AS hour,
tu.model,
SUM(tu.input_tokens)::bigint AS input_tokens,
SUM(tu.output_tokens)::bigint AS output_tokens,
SUM(tu.cache_read_tokens)::bigint AS cache_read_tokens,
SUM(tu.cache_write_tokens)::bigint AS cache_write_tokens,
COUNT(DISTINCT tu.task_id)::int AS task_count
FROM task_usage tu
JOIN agent_task_queue atq ON atq.id = tu.task_id
WHERE atq.runtime_id = $1
AND tu.created_at >= DATE_TRUNC('day', $2::timestamptz)
GROUP BY EXTRACT(HOUR FROM tu.created_at), tu.model
ORDER BY hour, tu.model
`
type GetRuntimeUsageByHourParams struct {
RuntimeID pgtype.UUID `json:"runtime_id"`
Since pgtype.Timestamptz `json:"since"`
}
type GetRuntimeUsageByHourRow struct {
Hour int32 `json:"hour"`
Model string `json:"model"`
InputTokens int64 `json:"input_tokens"`
OutputTokens int64 `json:"output_tokens"`
CacheReadTokens int64 `json:"cache_read_tokens"`
CacheWriteTokens int64 `json:"cache_write_tokens"`
TaskCount int32 `json:"task_count"`
}
// Per-(hour, model) token aggregates (hour ∈ 0..23) for a runtime since a
// cutoff. Powers the "By hour" tab — shows when in the day this runtime is
// doing real work, with model preserved for client-side cost calculation
// (same reason as ListRuntimeUsageByAgent above). Hours with zero activity
// are omitted; the client fills the 24-bucket axis.
func (q *Queries) GetRuntimeUsageByHour(ctx context.Context, arg GetRuntimeUsageByHourParams) ([]GetRuntimeUsageByHourRow, error) {
rows, err := q.db.Query(ctx, getRuntimeUsageByHour, arg.RuntimeID, arg.Since)
if err != nil {
return nil, err
}
defer rows.Close()
items := []GetRuntimeUsageByHourRow{}
for rows.Next() {
var i GetRuntimeUsageByHourRow
if err := rows.Scan(
&i.Hour,
&i.Model,
&i.InputTokens,
&i.OutputTokens,
&i.CacheReadTokens,
&i.CacheWriteTokens,
&i.TaskCount,
); err != nil {
return nil, err
}
items = append(items, i)
}
if err := rows.Err(); err != nil {
return nil, err
}
return items, nil
}
const listRuntimeUsage = `-- name: ListRuntimeUsage :many
SELECT
DATE(tu.created_at) AS date,
tu.provider,
tu.model,
SUM(tu.input_tokens)::bigint AS input_tokens,
SUM(tu.output_tokens)::bigint AS output_tokens,
SUM(tu.cache_read_tokens)::bigint AS cache_read_tokens,
SUM(tu.cache_write_tokens)::bigint AS cache_write_tokens
FROM task_usage tu
JOIN agent_task_queue atq ON atq.id = tu.task_id
WHERE atq.runtime_id = $1
AND tu.created_at >= DATE_TRUNC('day', $2::timestamptz)
GROUP BY DATE(tu.created_at), tu.provider, tu.model
ORDER BY DATE(tu.created_at) DESC, tu.provider, tu.model
`
type ListRuntimeUsageParams struct {
RuntimeID pgtype.UUID `json:"runtime_id"`
Since pgtype.Timestamptz `json:"since"`
}
type ListRuntimeUsageRow struct {
Date pgtype.Date `json:"date"`
Provider string `json:"provider"`
Model string `json:"model"`
InputTokens int64 `json:"input_tokens"`
OutputTokens int64 `json:"output_tokens"`
CacheReadTokens int64 `json:"cache_read_tokens"`
CacheWriteTokens int64 `json:"cache_write_tokens"`
}
// Bucket by tu.created_at (usage report time, ~= task completion time), not
// atq.created_at (task enqueue time), so tasks that queue one day and execute
// the next are attributed to the day tokens were actually produced. The since
// cutoff is truncated to start-of-day so `days=N` yields full calendar days.
func (q *Queries) ListRuntimeUsage(ctx context.Context, arg ListRuntimeUsageParams) ([]ListRuntimeUsageRow, error) {
rows, err := q.db.Query(ctx, listRuntimeUsage, arg.RuntimeID, arg.Since)
if err != nil {
return nil, err
}
defer rows.Close()
items := []ListRuntimeUsageRow{}
for rows.Next() {
var i ListRuntimeUsageRow
if err := rows.Scan(
&i.Date,
&i.Provider,
&i.Model,
&i.InputTokens,
&i.OutputTokens,
&i.CacheReadTokens,
&i.CacheWriteTokens,
); err != nil {
return nil, err
}
items = append(items, i)
}
if err := rows.Err(); err != nil {
return nil, err
}
return items, nil
}
const listRuntimeUsageByAgent = `-- name: ListRuntimeUsageByAgent :many
SELECT
atq.agent_id,
tu.model,
SUM(tu.input_tokens)::bigint AS input_tokens,
SUM(tu.output_tokens)::bigint AS output_tokens,
SUM(tu.cache_read_tokens)::bigint AS cache_read_tokens,
SUM(tu.cache_write_tokens)::bigint AS cache_write_tokens,
COUNT(DISTINCT tu.task_id)::int AS task_count
FROM task_usage tu
JOIN agent_task_queue atq ON atq.id = tu.task_id
WHERE atq.runtime_id = $1
AND tu.created_at >= DATE_TRUNC('day', $2::timestamptz)
GROUP BY atq.agent_id, tu.model
ORDER BY atq.agent_id, tu.model
`
type ListRuntimeUsageByAgentParams struct {
RuntimeID pgtype.UUID `json:"runtime_id"`
Since pgtype.Timestamptz `json:"since"`
}
type ListRuntimeUsageByAgentRow struct {
AgentID pgtype.UUID `json:"agent_id"`
Model string `json:"model"`
InputTokens int64 `json:"input_tokens"`
OutputTokens int64 `json:"output_tokens"`
CacheReadTokens int64 `json:"cache_read_tokens"`
CacheWriteTokens int64 `json:"cache_write_tokens"`
TaskCount int32 `json:"task_count"`
}
// Per-(agent, model) token aggregates for a runtime since a cutoff. Powers
// the runtime-detail "Cost by agent" tab. task_usage only carries task_id,
// so we join the queue to expose agent_id. The model dimension is kept on
// purpose: cost is computed client-side from a per-model pricing table, so
// collapsing models server-side would erase the information needed to do
// that arithmetic. The client groups by agent_id and sums cost per agent.
func (q *Queries) ListRuntimeUsageByAgent(ctx context.Context, arg ListRuntimeUsageByAgentParams) ([]ListRuntimeUsageByAgentRow, error) {
rows, err := q.db.Query(ctx, listRuntimeUsageByAgent, arg.RuntimeID, arg.Since)
if err != nil {
return nil, err
}
defer rows.Close()
items := []ListRuntimeUsageByAgentRow{}
for rows.Next() {
var i ListRuntimeUsageByAgentRow
if err := rows.Scan(
&i.AgentID,
&i.Model,
&i.InputTokens,
&i.OutputTokens,
&i.CacheReadTokens,
&i.CacheWriteTokens,
&i.TaskCount,
); err != nil {
return nil, err
}
items = append(items, i)
}
if err := rows.Err(); err != nil {
return nil, err
}
return items, nil
}