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