mirror of
https://github.com/multica-ai/multica.git
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0e31a9ca58
* fix(agent/runtimes): show Cursor Composer token usage and billing Attribute Cursor stream-json usage to the configured runtime model when result events omit `model`, and add Composer/Auto pricing so dashboard cost estimates resolve for composer-2.5 runs. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(views): align Cursor Composer pricing Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Eve <eve@multica-ai.local> Co-authored-by: multica-agent <github@multica.ai>
850 lines
34 KiB
TypeScript
850 lines
34 KiB
TypeScript
import type {
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AgentRuntime,
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RuntimeUsage,
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RuntimeUsageByAgent,
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} from "@multica/core/types";
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import { getCustomPricing } from "@multica/core/runtimes/custom-pricing-store";
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// A live local daemon re-registers itself within seconds of a server-side
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// delete (daemon self-heal, #2404), so deleting an online local runtime from
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// the UI has no lasting effect. Both the detail page and the list row menu
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// gate their Delete affordance on this same predicate.
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export function isSelfHealingRuntime(runtime: AgentRuntime): boolean {
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return runtime.runtime_mode === "local" && runtime.status === "online";
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}
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// ---------------------------------------------------------------------------
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// Formatting helpers
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// ---------------------------------------------------------------------------
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// Compound-unit relative timestamp ("2m 14s ago", "1d 4h ago", "6d 19h ago")
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// — gives the user enough precision to tell "just lost" from "long lost"
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// at a glance without forcing them to mouse-over for a full timestamp.
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export function formatLastSeen(lastSeenAt: string | null): string {
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if (!lastSeenAt) return "Never";
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const diffMs = Date.now() - new Date(lastSeenAt).getTime();
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if (diffMs < 5_000) return "Just now";
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const seconds = Math.floor(diffMs / 1000);
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const minutes = Math.floor(seconds / 60);
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const hours = Math.floor(minutes / 60);
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const days = Math.floor(hours / 24);
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if (minutes < 1) return `${seconds}s ago`;
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if (hours < 1) {
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const s = seconds % 60;
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return s > 0 ? `${minutes}m ${s}s ago` : `${minutes}m ago`;
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}
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if (days < 1) {
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const m = minutes % 60;
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return m > 0 ? `${hours}h ${m}m ago` : `${hours}h ago`;
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}
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const h = hours % 24;
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return h > 0 ? `${days}d ${h}h ago` : `${days}d ago`;
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}
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// Turns the back-end's `device_info` string ("MacBook-Pro · darwin-amd64",
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// "some-host · linux-amd64") into something humans recognise. We don't have
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// hardware model or geo data on the wire today, so we settle for an OS-aware
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// rewrite of the GOOS/GOARCH suffix while preserving the hostname.
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export function formatDeviceInfo(raw: string | null): string | null {
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if (!raw) return null;
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const trimmed = raw.trim();
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if (!trimmed) return null;
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return trimmed
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.split(" · ")
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.map((part) => prettifyOsArch(part))
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.join(" · ");
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}
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function prettifyOsArch(part: string): string {
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const lower = part.toLowerCase();
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// Pattern: <os>-<arch>; e.g. darwin-amd64, linux-arm64, windows-amd64.
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const match = lower.match(/^(darwin|linux|windows|freebsd|openbsd|netbsd)-(amd64|arm64|386|arm)$/);
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if (!match) return part;
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const os = match[1] ?? "";
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const arch = match[2] ?? "";
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const osLabel = OS_LABEL[os] ?? os;
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const archLabel = ARCH_LABEL[arch] ?? arch;
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return `${osLabel} (${archLabel})`;
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}
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const OS_LABEL: Record<string, string> = {
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darwin: "macOS",
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linux: "Linux",
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windows: "Windows",
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freebsd: "FreeBSD",
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openbsd: "OpenBSD",
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netbsd: "NetBSD",
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};
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const ARCH_LABEL: Record<string, string> = {
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amd64: "x86_64",
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arm64: "arm64",
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"386": "x86",
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arm: "arm",
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};
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// Strip leading "v" from version strings — GitHub releases ship `v0.2.17`,
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// daemon metadata reports `0.2.15`; normalising lets us compare both.
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function stripVersionPrefix(v: string): string {
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return v.replace(/^v/, "");
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}
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// True iff `latest` is strictly newer than `current` by dotted-numeric
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// comparison. Non-numeric / missing segments compare as 0 ("0.2" < "0.2.1").
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// Used by the runtime-list CLI column to decide whether to surface the ↑
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// marker; same logic also lives inline in update-section.tsx for now.
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export function isVersionNewer(latest: string, current: string): boolean {
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const l = stripVersionPrefix(latest).split(".").map(Number);
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const c = stripVersionPrefix(current).split(".").map(Number);
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for (let i = 0; i < Math.max(l.length, c.length); i++) {
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const lv = l[i] ?? 0;
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const cv = c[i] ?? 0;
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if (lv > cv) return true;
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if (lv < cv) return false;
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}
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return false;
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}
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export function formatTokens(n: number): string {
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if (n >= 1_000_000) {
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const m = n / 1_000_000;
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return m % 1 < 0.05 ? `${Math.round(m)}M` : `${m.toFixed(1)}M`;
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}
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if (n >= 1_000) {
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const k = n / 1_000;
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return k % 1 < 0.05 ? `${Math.round(k)}K` : `${k.toFixed(1)}K`;
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}
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return n.toLocaleString();
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}
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// ---------------------------------------------------------------------------
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// Cost estimation
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// ---------------------------------------------------------------------------
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// Pricing per million tokens (USD). Sources, each authoritative for the
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// rows tagged under it — keep in sync when providers release new models
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// or adjust prices.
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//
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// Anthropic: https://platform.claude.com/docs/en/about-claude/pricing
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// OpenAI: https://openai.com/api/pricing
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// DeepSeek: https://api-docs.deepseek.com/quick_start/pricing
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// Moonshot: https://www.kimi.com/resources/kimi-k2-6-pricing
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// Zhipu: https://docs.z.ai/guides/overview/pricing
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//
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// Anthropic's cacheWrite reflects the 5-minute cache TTL (1.25× input); the
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// daemon reports cache_creation_input_tokens without TTL metadata, so 5m is
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// the safest / cheapest assumption (matches the API default). OpenAI,
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// DeepSeek, Moonshot and Zhipu do not bill cache writes separately (cached
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// input is just discounted on subsequent reads), so cacheWrite mirrors
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// input there.
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//
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// The resolver matches exact keys after stripping a trailing date snapshot
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// (see `resolvePricing` below). It deliberately does NOT do startsWith
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// fallbacks: every catalog SKU needs its own row. That keeps unfamiliar
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// variants (`gpt-5.5-mini`, hypothetical `gpt-5.4-foo`) from silently
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// inheriting the price of a near-named relative; they surface in the
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// unmapped diagnostic instead. Mirror new entries in
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// `server/pkg/agent/models.go` so the catalog and pricing stay in sync.
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const MODEL_PRICING: Record<
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string,
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{ input: number; output: number; cacheRead: number; cacheWrite: number }
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> = {
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// -- Anthropic: current generation. Fable 5 is a Mythos-class SKU at 10/50;
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// Opus 4.5+ stays on the lower 5/25 Opus tier. --
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"claude-fable-5": { input: 10, output: 50, cacheRead: 1.00, cacheWrite: 12.50 },
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"claude-haiku-4-5": { input: 1, output: 5, cacheRead: 0.10, cacheWrite: 1.25 },
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"claude-sonnet-4-5": { input: 3, output: 15, cacheRead: 0.30, cacheWrite: 3.75 },
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"claude-sonnet-4-6": { input: 3, output: 15, cacheRead: 0.30, cacheWrite: 3.75 },
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"claude-opus-4-5": { input: 5, output: 25, cacheRead: 0.50, cacheWrite: 6.25 },
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"claude-opus-4-6": { input: 5, output: 25, cacheRead: 0.50, cacheWrite: 6.25 },
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"claude-opus-4-7": { input: 5, output: 25, cacheRead: 0.50, cacheWrite: 6.25 },
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"claude-opus-4-8": { input: 5, output: 25, cacheRead: 0.50, cacheWrite: 6.25 },
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// -- Anthropic: pre-4.5 Opus (legacy, still served at original price tier) --
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"claude-opus-4-1": { input: 15, output: 75, cacheRead: 1.50, cacheWrite: 18.75 },
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"claude-opus-4": { input: 15, output: 75, cacheRead: 1.50, cacheWrite: 18.75 },
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// -- Anthropic: Sonnet 4.0 (deprecated; same price as the 4.x family) --
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"claude-sonnet-4": { input: 3, output: 15, cacheRead: 0.30, cacheWrite: 3.75 },
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// -- Anthropic: older Haiku tier (defensive entry for the rare runtime still on it) --
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"claude-haiku-3-5": { input: 0.80, output: 4, cacheRead: 0.08, cacheWrite: 1.00 },
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// -- OpenAI: dotted-minor Codex catalog SKUs. Each generation is priced
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// independently — no fallback to `gpt-5`. Entries track
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// `server/pkg/agent/models.go` (Codex provider list).
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"gpt-5.5": { input: 5, output: 30, cacheRead: 0.50, cacheWrite: 5 },
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"gpt-5.4-mini": { input: 0.75, output: 4.50, cacheRead: 0.075, cacheWrite: 0.75 },
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"gpt-5.4": { input: 2.50, output: 15, cacheRead: 0.25, cacheWrite: 2.50 },
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"gpt-5.3-codex": { input: 1.75, output: 14, cacheRead: 0.175, cacheWrite: 1.75 },
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// -- OpenAI: GPT-5 family (Codex CLI's default is gpt-5-codex; -codex/-mini/-nano variants priced per OpenAI tiers) --
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"gpt-5-codex": { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 1.25 },
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"gpt-5-mini": { input: 0.25, output: 2, cacheRead: 0.025, cacheWrite: 0.25 },
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"gpt-5-nano": { input: 0.05, output: 0.40, cacheRead: 0.005, cacheWrite: 0.05 },
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"gpt-5": { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 1.25 },
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// -- OpenAI: o-series reasoning models --
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"o3-mini": { input: 1.10, output: 4.40, cacheRead: 0.55, cacheWrite: 1.10 },
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"o3": { input: 2, output: 8, cacheRead: 0.50, cacheWrite: 2 },
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"o4-mini": { input: 1.10, output: 4.40, cacheRead: 0.275, cacheWrite: 1.10 },
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// -- OpenAI: GPT-4o family (legacy, kept for runtimes still configured against it) --
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"gpt-4o-mini": { input: 0.15, output: 0.60, cacheRead: 0.075, cacheWrite: 0.15 },
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"gpt-4o": { input: 2.50, output: 10, cacheRead: 1.25, cacheWrite: 2.50 },
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// -- DeepSeek (api-docs.deepseek.com/quick_start/pricing).
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// The official catalog lists exactly two current SKUs; `deepseek-chat`
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// and `deepseek-reasoner` are aliases that route to `deepseek-v4-flash`
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// (non-thinking and thinking mode respectively) per the same page.
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// `deepseek-v4-pro` is currently under a 75%-off promo that ends
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// 2026-05-31 15:59 UTC; we price at the post-promo standard rate
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// ($1.74/$3.48) so the dashboard does not jump 4× on June 1 — accept
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// a brief over-estimate during the promo over a sudden cliff after it. --
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"deepseek-v4-flash": { input: 0.14, output: 0.28, cacheRead: 0.0028, cacheWrite: 0.14 },
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"deepseek-v4-pro": { input: 1.74, output: 3.48, cacheRead: 0.0145, cacheWrite: 1.74 },
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"deepseek-chat": { input: 0.14, output: 0.28, cacheRead: 0.0028, cacheWrite: 0.14 },
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"deepseek-reasoner": { input: 0.14, output: 0.28, cacheRead: 0.0028, cacheWrite: 0.14 },
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// -- Moonshot Kimi (kimi.com/resources/kimi-k2-6-pricing).
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// Only K2.6 is on the official price sheet today; earlier K2 variants
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// are intentionally omitted until Moonshot publishes their rates. --
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"kimi-k2.6": { input: 0.95, output: 4.00, cacheRead: 0.16, cacheWrite: 0.95 },
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// -- Zhipu z.ai (docs.z.ai/guides/overview/pricing). Free flash tiers
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// are priced at 0 so they resolve cleanly instead of falling through
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// to the "unmapped" diagnostic. --
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"glm-5.1": { input: 1.4, output: 4.4, cacheRead: 0.26, cacheWrite: 1.4 },
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"glm-5": { input: 1.0, output: 3.2, cacheRead: 0.2, cacheWrite: 1.0 },
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"glm-5-turbo": { input: 1.2, output: 4.0, cacheRead: 0.24, cacheWrite: 1.2 },
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"glm-4.7": { input: 0.6, output: 2.2, cacheRead: 0.11, cacheWrite: 0.6 },
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"glm-4.7-flashx": { input: 0.07, output: 0.4, cacheRead: 0.01, cacheWrite: 0.07 },
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"glm-4.7-flash": { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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"glm-4.6": { input: 0.6, output: 2.2, cacheRead: 0.11, cacheWrite: 0.6 },
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"glm-4.5": { input: 0.6, output: 2.2, cacheRead: 0.11, cacheWrite: 0.6 },
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"glm-4.5-x": { input: 2.2, output: 8.9, cacheRead: 0.45, cacheWrite: 2.2 },
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"glm-4.5-air": { input: 0.2, output: 1.1, cacheRead: 0.03, cacheWrite: 0.2 },
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"glm-4.5-airx": { input: 1.1, output: 4.5, cacheRead: 0.22, cacheWrite: 1.1 },
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"glm-4.5-flash": { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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// -- Cursor Composer / Auto (cursor.com/docs/models-and-pricing,
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// cursor.com/docs/models/cursor-composer-2,
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// cursor.com/docs/models/cursor-composer-2-5).
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// Cursor result events often omit `model`, so the daemon falls back to
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// the configured runtime model or the legacy key `cursor`.
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// Cursor does not publish a cache-write rate for these rows; keep it at
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// 0 so reported cache_write_tokens don't invent spend from input pricing.
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"auto": { input: 1.25, output: 6, cacheRead: 0.25, cacheWrite: 0 },
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"composer-2.5-fast": { input: 3, output: 15, cacheRead: 0.5, cacheWrite: 0 },
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"composer-2.5": { input: 0.5, output: 2.5, cacheRead: 0.2, cacheWrite: 0 },
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"composer-2-fast": { input: 1.5, output: 7.5, cacheRead: 0.35, cacheWrite: 0 },
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"composer-2": { input: 0.5, output: 2.5, cacheRead: 0.2, cacheWrite: 0 },
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"composer-1.5": { input: 3.5, output: 17.5, cacheRead: 0.35, cacheWrite: 0 },
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"composer-1": { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0 },
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// Legacy fallback bucket when neither the result event nor the runtime
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// model is known — price at the current Composer 2.5 Fast default.
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"cursor": { input: 3, output: 15, cacheRead: 0.5, cacheWrite: 0 },
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};
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// Resolve a model string to its pricing tier. Exact match, with four
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// tolerances applied in order:
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//
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// 1. Provider-prefixed IDs (`anthropic/claude-opus-4.7` from openclaw /
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// opencode) — the `<provider>/` segment is routing metadata, not part
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// of the SKU, so we strip it before lookup.
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// 2. Anthropic dot↔dash normalization — Claude Code reports
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// `claude-opus-4-7`, GitHub Copilot reports `claude-opus-4.7`. Same
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// SKU, two transports. We canonicalize `claude-*` IDs to the dashed
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// form Anthropic itself publishes. Scoped to `claude-*` because for
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// OpenAI the separator IS semantic (`gpt-5.4` ≠ `gpt-5-4`).
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// 3. Trailing dated snapshots (`claude-sonnet-4-5-20250929`,
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// `gpt-5-2025-08-07`) — the family is what we price, the date is
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// volatile, so we strip a trailing date / "latest" tag.
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// 4. Trailing context-window tag (`claude-opus-4-7[1m]`) — Anthropic's
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// 1M-context beta is the same SKU at standard rates for prompts
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// ≤200K input tokens, with a 2× surcharge above that. Aggregated
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// usage rows don't carry per-request prompt sizes, so we price the
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// bracketed variant at the standard tier. Slight under-estimate
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// beats the previous behaviour of dropping the row entirely.
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//
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// Anything still unmapped falls back to the user-supplied custom pricing
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// store. No startsWith fallback: variants like `gpt-5.5-mini` must have
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// their own row to be priced (otherwise they'd inherit `gpt-5.5`).
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function resolvePricing(model: string) {
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if (!model) return undefined;
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for (const candidate of canonicalCandidates(model)) {
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const hit = MODEL_PRICING[candidate];
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if (hit) return hit;
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}
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for (const candidate of canonicalCandidates(model)) {
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const hit = getCustomPricing(candidate);
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if (hit) return hit;
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}
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return undefined;
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}
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// Generate the lookup candidates for a model string, in priority order:
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// the raw string first (preserves explicit user / catalog spellings),
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// then the canonicalized forms. Deduped so we don't repeat lookups.
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function canonicalCandidates(model: string): string[] {
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const seen = new Set<string>();
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const out: string[] = [];
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const push = (s: string) => {
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if (!s || seen.has(s)) return;
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seen.add(s);
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out.push(s);
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};
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const stripDate = (s: string) =>
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s.replace(/-(20\d{2}-\d{2}-\d{2}|20\d{6}|latest)$/, "");
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const stripProvider = (s: string) => {
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const i = s.indexOf("/");
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return i > 0 && /^[a-z][a-z0-9_-]*$/i.test(s.slice(0, i)) ? s.slice(i + 1) : s;
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};
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// Only Anthropic IDs are dot↔dash equivalent. OpenAI separators are
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// semantic, so we leave `gpt-5.4` etc. alone.
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const canonAnthropic = (s: string) =>
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s.startsWith("claude-") ? s.replace(/\./g, "-") : s;
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// Trailing context-window tag (`claude-opus-4-7[1m]`). Same family,
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// same price tier — see resolver comment above for the 1M-context
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// pricing trade-off.
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const stripContextTag = (s: string) => s.replace(/\[[^\]]+\]$/, "");
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const raw = model;
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const noProvider = stripProvider(raw);
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const dashed = canonAnthropic(noProvider);
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const noTag = stripContextTag(dashed);
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push(raw);
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push(noProvider);
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push(dashed);
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push(noTag);
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push(stripDate(raw));
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push(stripDate(noProvider));
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push(stripDate(dashed));
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push(stripDate(noTag));
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return out;
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}
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// Cheap predicate for the empty-state diagnostic: which model strings in a
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// usage batch failed pricing resolution. Useful when the user is staring at
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// "$0.00 / 2M tokens" and wants to know why.
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export function isModelPriced(model: string): boolean {
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return resolvePricing(model) !== undefined;
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}
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// Returns the unique, sorted list of model strings present in `rows` that
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// don't resolve to a price. Empty when everything's priced or there are no
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// rows.
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export function collectUnmappedModels(rows: readonly Priceable[]): string[] {
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const set = new Set<string>();
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for (const r of rows) {
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if (r.model && !isModelPriced(r.model)) set.add(r.model);
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}
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return Array.from(set).toSorted();
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}
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// Anything carrying per-model token totals can be priced — RuntimeUsage,
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// RuntimeUsageByAgent, RuntimeUsageByHour all share this shape on purpose
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// (the back-end keeps the model dimension specifically so the client can
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// run this calculation for any aggregation axis).
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type Priceable = Pick<
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RuntimeUsage,
|
||
"model" | "input_tokens" | "output_tokens" | "cache_read_tokens" | "cache_write_tokens"
|
||
>;
|
||
|
||
export function estimateCost(usage: Priceable): number {
|
||
const pricing = resolvePricing(usage.model);
|
||
if (!pricing) return 0;
|
||
return (
|
||
(usage.input_tokens * pricing.input +
|
||
usage.output_tokens * pricing.output +
|
||
usage.cache_read_tokens * pricing.cacheRead +
|
||
usage.cache_write_tokens * pricing.cacheWrite) /
|
||
1_000_000
|
||
);
|
||
}
|
||
|
||
export interface CostBreakdown {
|
||
input: number;
|
||
output: number;
|
||
cacheRead: number;
|
||
cacheWrite: number;
|
||
}
|
||
|
||
export function estimateCostBreakdown(usage: Priceable): CostBreakdown {
|
||
const pricing = resolvePricing(usage.model);
|
||
if (!pricing) {
|
||
return { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 };
|
||
}
|
||
return {
|
||
input: (usage.input_tokens * pricing.input) / 1_000_000,
|
||
output: (usage.output_tokens * pricing.output) / 1_000_000,
|
||
cacheRead: (usage.cache_read_tokens * pricing.cacheRead) / 1_000_000,
|
||
cacheWrite: (usage.cache_write_tokens * pricing.cacheWrite) / 1_000_000,
|
||
};
|
||
}
|
||
|
||
// Cache savings: what cache *reads* would have cost at full input pricing
|
||
// minus what they actually cost at the discounted cache-hit rate. This is a
|
||
// reconstruction of "money the cache saved you", not real-world spend.
|
||
export function estimateCacheSavings(usage: Priceable): number {
|
||
const pricing = resolvePricing(usage.model);
|
||
if (!pricing) return 0;
|
||
const wouldHaveCost = (usage.cache_read_tokens * pricing.input) / 1_000_000;
|
||
const actualCost = (usage.cache_read_tokens * pricing.cacheRead) / 1_000_000;
|
||
return wouldHaveCost - actualCost;
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Data aggregation
|
||
// ---------------------------------------------------------------------------
|
||
|
||
export interface DailyTokenData {
|
||
date: string;
|
||
label: string;
|
||
input: number;
|
||
output: number;
|
||
cacheRead: number;
|
||
cacheWrite: number;
|
||
}
|
||
|
||
export interface DailyCostData {
|
||
date: string;
|
||
label: string;
|
||
cost: number;
|
||
}
|
||
|
||
// Stacked variant — splits the daily $ figure into the three components that
|
||
// drive billing (cache reads excluded; their cost is tracked separately as
|
||
// "savings" since they're typically dominated by the cached-input discount).
|
||
export interface DailyCostStackData {
|
||
date: string;
|
||
label: string;
|
||
input: number;
|
||
output: number;
|
||
cacheWrite: number;
|
||
total: number;
|
||
}
|
||
|
||
export interface ModelDistribution {
|
||
model: string;
|
||
tokens: number;
|
||
cost: number;
|
||
}
|
||
|
||
export interface WeeklyTokenData {
|
||
weekStart: string;
|
||
weekEnd: string;
|
||
// X-axis tick — Monday of the week, e.g. "May 12".
|
||
label: string;
|
||
// Tooltip header — inclusive range, e.g. "May 12 – May 18".
|
||
rangeLabel: string;
|
||
// True when `weekEnd` is in the future (today is mid-week). Surface this
|
||
// in the chart so the bar can be drawn at reduced opacity / striped to
|
||
// signal "don't read this as a finished week".
|
||
partial: boolean;
|
||
daysCovered: number;
|
||
input: number;
|
||
output: number;
|
||
cacheRead: number;
|
||
cacheWrite: number;
|
||
}
|
||
|
||
export interface WeeklyCostStackData {
|
||
weekStart: string;
|
||
weekEnd: string;
|
||
label: string;
|
||
rangeLabel: string;
|
||
partial: boolean;
|
||
daysCovered: number;
|
||
input: number;
|
||
output: number;
|
||
cacheWrite: number;
|
||
total: number;
|
||
}
|
||
|
||
export function aggregateByDate(usage: RuntimeUsage[]): {
|
||
dailyTokens: DailyTokenData[];
|
||
dailyCost: DailyCostData[];
|
||
dailyCostStack: DailyCostStackData[];
|
||
modelDist: ModelDistribution[];
|
||
} {
|
||
const dateMap = new Map<string, Omit<DailyTokenData, "label">>();
|
||
const costMap = new Map<string, number>();
|
||
const stackMap = new Map<
|
||
string,
|
||
{ input: number; output: number; cacheWrite: number }
|
||
>();
|
||
const modelMap = new Map<string, { tokens: number; cost: number }>();
|
||
|
||
for (const u of usage) {
|
||
const existing = dateMap.get(u.date) ?? {
|
||
date: u.date,
|
||
input: 0,
|
||
output: 0,
|
||
cacheRead: 0,
|
||
cacheWrite: 0,
|
||
};
|
||
existing.input += u.input_tokens;
|
||
existing.output += u.output_tokens;
|
||
existing.cacheRead += u.cache_read_tokens;
|
||
existing.cacheWrite += u.cache_write_tokens;
|
||
dateMap.set(u.date, existing);
|
||
|
||
const dayCost = (costMap.get(u.date) ?? 0) + estimateCost(u);
|
||
costMap.set(u.date, dayCost);
|
||
|
||
const breakdown = estimateCostBreakdown(u);
|
||
const stack = stackMap.get(u.date) ?? {
|
||
input: 0,
|
||
output: 0,
|
||
cacheWrite: 0,
|
||
};
|
||
stack.input += breakdown.input;
|
||
stack.output += breakdown.output;
|
||
stack.cacheWrite += breakdown.cacheWrite;
|
||
stackMap.set(u.date, stack);
|
||
|
||
const modelName = u.model || u.provider;
|
||
const m = modelMap.get(modelName) ?? { tokens: 0, cost: 0 };
|
||
m.tokens +=
|
||
u.input_tokens + u.output_tokens + u.cache_read_tokens + u.cache_write_tokens;
|
||
m.cost += estimateCost(u);
|
||
modelMap.set(modelName, m);
|
||
}
|
||
|
||
const formatLabel = (d: string) => {
|
||
const date = new Date(d + "T00:00:00");
|
||
return `${date.getMonth() + 1}/${date.getDate()}`;
|
||
};
|
||
|
||
const dailyTokens = Array.from(dateMap.values())
|
||
.toSorted((a, b) => a.date.localeCompare(b.date))
|
||
.map((d) => ({ ...d, label: formatLabel(d.date) }));
|
||
|
||
const dailyCost = Array.from(costMap.entries())
|
||
.toSorted(([a], [b]) => a.localeCompare(b))
|
||
.map(([date, cost]) => ({
|
||
date,
|
||
label: formatLabel(date),
|
||
cost: Math.round(cost * 100) / 100,
|
||
}));
|
||
|
||
const dailyCostStack = Array.from(stackMap.entries())
|
||
.toSorted(([a], [b]) => a.localeCompare(b))
|
||
.map(([date, s]) => {
|
||
const round = (n: number) => Math.round(n * 100) / 100;
|
||
const input = round(s.input);
|
||
const output = round(s.output);
|
||
const cacheWrite = round(s.cacheWrite);
|
||
return {
|
||
date,
|
||
label: formatLabel(date),
|
||
input,
|
||
output,
|
||
cacheWrite,
|
||
total: round(input + output + cacheWrite),
|
||
};
|
||
});
|
||
|
||
const modelDist = [...modelMap.entries()]
|
||
.map(([model, data]) => ({ model, ...data }))
|
||
.sort((a, b) => b.tokens - a.tokens);
|
||
|
||
return { dailyTokens, dailyCost, dailyCostStack, modelDist };
|
||
}
|
||
|
||
// Fold daily-grain rows into ISO calendar weeks (Mon–Sun). Reuses the same
|
||
// 180-day cache the daily aggregation reads from — no extra request. The
|
||
// latest week is flagged `partial` when today (in the runtime's tz) is
|
||
// before Sunday, so the chart can render the in-progress bar at half
|
||
// opacity instead of letting the user misread "this week" as a dip.
|
||
//
|
||
// `weekCount` pins the output to exactly that many trailing calendar weeks
|
||
// ending at the week that contains today (in `tz`). Buckets are pre-zeroed,
|
||
// so sparse data — including weeks with no usage — renders as empty bars
|
||
// rather than disappearing. Rows whose week falls outside the window are
|
||
// dropped; without this guard `.slice(-weekCount)` on a sparse 180-day
|
||
// aggregate would surface old populated weeks instead of the empty
|
||
// in-range buckets the user asked for (MUL-2382 weekly window scoping).
|
||
// Accepts any row carrying `date` + token counts + the model needed for
|
||
// pricing. Both `RuntimeUsage` (runtime detail) and `DashboardUsageDaily`
|
||
// (workspace dashboard) match this shape — there's no behavioural difference,
|
||
// just slightly different surrounding fields neither aggregator cares about.
|
||
type WeeklyAggregable = Pick<
|
||
RuntimeUsage,
|
||
| "date"
|
||
| "model"
|
||
| "input_tokens"
|
||
| "output_tokens"
|
||
| "cache_read_tokens"
|
||
| "cache_write_tokens"
|
||
>;
|
||
|
||
export function aggregateByWeek(
|
||
usage: readonly WeeklyAggregable[],
|
||
tz: string,
|
||
weekCount: number,
|
||
): {
|
||
weeklyTokens: WeeklyTokenData[];
|
||
weeklyCostStack: WeeklyCostStackData[];
|
||
} {
|
||
const count = Math.max(1, Math.floor(weekCount));
|
||
const today = todayIso(tz);
|
||
const currentWeekStart = weekStartIso(today);
|
||
const firstWeekStart = addDaysIso(currentWeekStart, -(count - 1) * 7);
|
||
|
||
type TokenAgg = Omit<WeeklyTokenData, "label" | "rangeLabel" | "partial" | "daysCovered" | "weekEnd">;
|
||
const tokenMap = new Map<string, TokenAgg>();
|
||
const stackMap = new Map<string, { input: number; output: number; cacheWrite: number }>();
|
||
|
||
// Pre-seed every trailing calendar week in the window so sparse / empty
|
||
// weeks still render as zero bars instead of being dropped.
|
||
for (let i = 0; i < count; i++) {
|
||
const wkStart = addDaysIso(firstWeekStart, i * 7);
|
||
tokenMap.set(wkStart, {
|
||
weekStart: wkStart,
|
||
input: 0,
|
||
output: 0,
|
||
cacheRead: 0,
|
||
cacheWrite: 0,
|
||
});
|
||
stackMap.set(wkStart, { input: 0, output: 0, cacheWrite: 0 });
|
||
}
|
||
|
||
for (const u of usage) {
|
||
const wkStart = weekStartIso(u.date);
|
||
if (wkStart < firstWeekStart || wkStart > currentWeekStart) continue;
|
||
const tokens = tokenMap.get(wkStart);
|
||
if (!tokens) continue;
|
||
tokens.input += u.input_tokens;
|
||
tokens.output += u.output_tokens;
|
||
tokens.cacheRead += u.cache_read_tokens;
|
||
tokens.cacheWrite += u.cache_write_tokens;
|
||
|
||
const breakdown = estimateCostBreakdown(u);
|
||
const stack = stackMap.get(wkStart);
|
||
if (!stack) continue;
|
||
stack.input += breakdown.input;
|
||
stack.output += breakdown.output;
|
||
stack.cacheWrite += breakdown.cacheWrite;
|
||
}
|
||
|
||
const decorate = (weekStart: string) => {
|
||
const weekEnd = addDaysIso(weekStart, 6);
|
||
const partial = today < weekEnd;
|
||
// Inclusive count of how many days of this week have actually elapsed.
|
||
// Sits at 7 for closed weeks, 1..6 for the current week.
|
||
const elapsedDays = Math.min(
|
||
7,
|
||
Math.max(
|
||
1,
|
||
// Day index of `today` within [weekStart, weekEnd] + 1.
|
||
diffDaysIso(weekStart, today < weekStart ? weekStart : today < weekEnd ? today : weekEnd) + 1,
|
||
),
|
||
);
|
||
return {
|
||
weekStart,
|
||
weekEnd,
|
||
label: formatShortDate(weekStart),
|
||
rangeLabel: `${formatShortDate(weekStart)} – ${formatShortDate(weekEnd)}`,
|
||
partial,
|
||
daysCovered: partial ? elapsedDays : 7,
|
||
};
|
||
};
|
||
|
||
const weeklyTokens: WeeklyTokenData[] = Array.from(tokenMap.values())
|
||
.toSorted((a, b) => a.weekStart.localeCompare(b.weekStart))
|
||
.map((t) => ({ ...t, ...decorate(t.weekStart) }));
|
||
|
||
const weeklyCostStack: WeeklyCostStackData[] = Array.from(stackMap.entries())
|
||
.toSorted(([a], [b]) => a.localeCompare(b))
|
||
.map(([weekStart, s]) => {
|
||
const round = (n: number) => Math.round(n * 100) / 100;
|
||
const input = round(s.input);
|
||
const output = round(s.output);
|
||
const cacheWrite = round(s.cacheWrite);
|
||
return {
|
||
...decorate(weekStart),
|
||
input,
|
||
output,
|
||
cacheWrite,
|
||
total: round(input + output + cacheWrite),
|
||
};
|
||
});
|
||
|
||
return { weeklyTokens, weeklyCostStack };
|
||
}
|
||
|
||
// Slice a daily-grain usage series into the user's selected window AND the
|
||
// immediately prior window of equal length. "Today" is read in the runtime's
|
||
// timezone so the cutoff lands on the same calendar boundary the backend
|
||
// used when bucketing rows — without this the browser/runtime tz gap could
|
||
// shift the boundary by a day at the edges (#MUL-2382 sliceWindow tz bug).
|
||
export function sliceWindow(
|
||
usage: readonly RuntimeUsage[],
|
||
days: number,
|
||
tz: string,
|
||
): { filtered: RuntimeUsage[]; prevFiltered: RuntimeUsage[] } {
|
||
const today = todayIso(tz);
|
||
const isoCurrent = addDaysIso(today, -days);
|
||
const isoPrev = addDaysIso(today, -days * 2);
|
||
return {
|
||
filtered: usage.filter((u) => u.date >= isoCurrent),
|
||
prevFiltered: usage.filter(
|
||
(u) => u.date >= isoPrev && u.date < isoCurrent,
|
||
),
|
||
};
|
||
}
|
||
|
||
function diffDaysIso(from: string, to: string): number {
|
||
const [y1, m1, d1] = from.split("-").map(Number);
|
||
const [y2, m2, d2] = to.split("-").map(Number);
|
||
const a = Date.UTC(y1 ?? 1970, (m1 ?? 1) - 1, d1 ?? 1);
|
||
const b = Date.UTC(y2 ?? 1970, (m2 ?? 1) - 1, d2 ?? 1);
|
||
return Math.round((b - a) / 86_400_000);
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Calendar helpers — all date math runs on YYYY-MM-DD strings in the
|
||
// runtime's IANA timezone. The backend already groups daily usage by
|
||
// `start-of-day in runtime tz`, so we keep the entire frontend aggregation
|
||
// on the same axis (Daily / Weekly) to avoid one-day drift when the browser
|
||
// and runtime sit in different time zones.
|
||
// ---------------------------------------------------------------------------
|
||
|
||
// Today's calendar date (YYYY-MM-DD) in the given IANA timezone. `en-CA`
|
||
// gives ISO-shaped output without us having to assemble Intl parts by hand.
|
||
export function todayIso(tz: string): string {
|
||
return new Intl.DateTimeFormat("en-CA", {
|
||
timeZone: tz,
|
||
year: "numeric",
|
||
month: "2-digit",
|
||
day: "2-digit",
|
||
}).format(new Date());
|
||
}
|
||
|
||
// Pure date arithmetic on a YYYY-MM-DD string. Uses UTC under the hood so
|
||
// DST transitions never shift the result by an hour and round to a
|
||
// neighbouring day.
|
||
export function addDaysIso(iso: string, days: number): string {
|
||
const [y, m, d] = iso.split("-").map(Number);
|
||
const dt = new Date(Date.UTC(y ?? 1970, (m ?? 1) - 1, d ?? 1));
|
||
dt.setUTCDate(dt.getUTCDate() + days);
|
||
return dt.toISOString().slice(0, 10);
|
||
}
|
||
|
||
// Monday-of-week as YYYY-MM-DD. ISO 8601 week-start, matching the heatmap
|
||
// and the team's day-to-day "this week" mental model. Pure string math —
|
||
// no `new Date()` reads — so it's stable under any host timezone.
|
||
export function weekStartIso(iso: string): string {
|
||
const [y, m, d] = iso.split("-").map(Number);
|
||
const dt = new Date(Date.UTC(y ?? 1970, (m ?? 1) - 1, d ?? 1));
|
||
const day = dt.getUTCDay(); // 0 = Sun, 1 = Mon, ..., 6 = Sat
|
||
const offset = (day + 6) % 7; // distance back to Monday
|
||
dt.setUTCDate(dt.getUTCDate() - offset);
|
||
return dt.toISOString().slice(0, 10);
|
||
}
|
||
|
||
// "May 12" — short, locale-aware month/day for a YYYY-MM-DD string. Parsing
|
||
// via UTC keeps the displayed day stable regardless of the browser's tz.
|
||
export function formatShortDate(iso: string): string {
|
||
const [y, m, d] = iso.split("-").map(Number);
|
||
const dt = new Date(Date.UTC(y ?? 1970, (m ?? 1) - 1, d ?? 1));
|
||
return dt.toLocaleString("en", {
|
||
month: "short",
|
||
day: "numeric",
|
||
timeZone: "UTC",
|
||
});
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Cost-by-X aggregations
|
||
//
|
||
// All three "Cost by …" tabs share the same shape: a sorted list of rows
|
||
// where each row carries a key (agent name, model name, or hour-of-day),
|
||
// total tokens and total cost. The chart / list components are oblivious
|
||
// to which axis they're rendering — they just see {key, tokens, cost}.
|
||
// ---------------------------------------------------------------------------
|
||
|
||
export interface CostByKey {
|
||
key: string;
|
||
tokens: number;
|
||
cost: number;
|
||
taskCount: number;
|
||
}
|
||
|
||
// Per-(agent, model) rows → per-agent totals. Cost is summed across all
|
||
// models for that agent, then the list is sorted by cost desc so the
|
||
// heaviest-spending agent appears first.
|
||
export function aggregateCostByAgent(rows: RuntimeUsageByAgent[]): CostByKey[] {
|
||
const map = new Map<string, CostByKey>();
|
||
for (const r of rows) {
|
||
const entry = map.get(r.agent_id) ?? {
|
||
key: r.agent_id,
|
||
tokens: 0,
|
||
cost: 0,
|
||
taskCount: 0,
|
||
};
|
||
entry.tokens +=
|
||
r.input_tokens + r.output_tokens + r.cache_read_tokens + r.cache_write_tokens;
|
||
entry.cost += estimateCost(r);
|
||
entry.taskCount += r.task_count;
|
||
map.set(r.agent_id, entry);
|
||
}
|
||
return Array.from(map.values()).toSorted((a, b) => b.cost - a.cost);
|
||
}
|
||
|
||
// Per-(date, model) rows → per-model totals (the "By model" tab reuses the
|
||
// daily-grain data we already cache, so no extra request is needed).
|
||
export function aggregateCostByModel(rows: RuntimeUsage[]): CostByKey[] {
|
||
const map = new Map<string, CostByKey>();
|
||
for (const r of rows) {
|
||
const key = r.model || r.provider || "unknown";
|
||
const entry = map.get(key) ?? { key, tokens: 0, cost: 0, taskCount: 0 };
|
||
entry.tokens +=
|
||
r.input_tokens + r.output_tokens + r.cache_read_tokens + r.cache_write_tokens;
|
||
entry.cost += estimateCost(r);
|
||
map.set(key, entry);
|
||
}
|
||
return Array.from(map.values()).toSorted((a, b) => b.cost - a.cost);
|
||
}
|
||
|
||
// Sum of estimated cost over the trailing window
|
||
// [today − offsetDays − daysBack, today − offsetDays).
|
||
// `offsetDays = 0, daysBack = 7` → last 7 days.
|
||
// `offsetDays = 7, daysBack = 7` → the 7 days *before* the last 7 (the
|
||
// "previous" window for the runtime-list ↑/↓ delta).
|
||
//
|
||
// "Today" is read in `tz` (the viewer's timezone) so the cutoff lands on
|
||
// the same calendar boundary the backend used when bucketing rows — the
|
||
// rows arrive bucketed in the viewer's tz, so slicing them with the JS
|
||
// engine's local tz would shift the window by a day at the edges.
|
||
//
|
||
// Walks the same daily-grain `RuntimeUsage` rows that `aggregateByDate` uses,
|
||
// so the runtime-list cost stays consistent with the runtime-detail KPIs
|
||
// (and crucially, hits the same TanStack Query cache key).
|
||
export function computeCostInWindow(
|
||
rows: readonly RuntimeUsage[],
|
||
daysBack: number,
|
||
tz: string,
|
||
offsetDays: number = 0,
|
||
): number {
|
||
const today = todayIso(tz);
|
||
const isoEnd = addDaysIso(today, -offsetDays);
|
||
const isoStart = addDaysIso(today, -offsetDays - daysBack);
|
||
let total = 0;
|
||
for (const r of rows) {
|
||
if (r.date >= isoStart && r.date < isoEnd) total += estimateCost(r);
|
||
}
|
||
return total;
|
||
}
|
||
|
||
export function pctChange(current: number, previous: number): number | null {
|
||
if (previous <= 0) return null;
|
||
return Math.round(((current - previous) / previous) * 100);
|
||
}
|