ai-sdk-token-usage/dist/index.mjs.map

A lightweight Typescript library to track and visualize token usage across multiple AI model providers.

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"index.mjs","sourcesContent":["/**\n * Formats a numeric token count into a compact, human-readable string.\n *\n * Uses the built-in `Intl.NumberFormat` API with `\"compact\"` notation\n * (e.g., `1K`, `2.5M`) and a U.S. English locale.\n *\n * @example\n * ```ts\n * formatTokenAmount(15230) // \"15.2K\"\n * formatTokenAmount(1000000) // \"1M\"\n * ```\n *\n * @param tokens - The number of tokens to format.\n * @returns A compact, localized string representation of the token amount.\n */\nexport function formatTokenAmount(tokens: number) {\n  return new Intl.NumberFormat(\"en-US\", {\n    notation: \"compact\",\n  }).format(tokens)\n}\n\n/**\n * Formats a numeric value as a localized currency string.\n *\n * Uses the built-in `Intl.NumberFormat` API to format the given price\n * in the specified currency. Defaults to USD if no currency code is provided.\n *\n * @example\n * ```ts\n * formatPrice(0.032) // \"$0.03\"\n * formatPrice(1.2, \"EUR\") // \"€1.20\"\n * ```\n *\n * @param price - The numeric value to format as a currency.\n * @param currency - The ISO 4217 currency code (defaults to `\"USD\"`).\n * @returns A localized currency string representing the formatted price.\n */\nexport function formatPrice(price: number, currency: string = \"USD\") {\n  return new Intl.NumberFormat(\"en-US\", {\n    style: \"currency\",\n    currency,\n  }).format(price)\n}\n","import type { TokenUsageError } from \"./types\"\n\nexport class BaseError extends Error {\n  status: number\n  info: unknown\n\n  constructor(status: number, info: unknown, message?: string) {\n    super(message ?? \"An error occurred\")\n    this.name = \"BaseError\"\n    this.status = status\n    this.info = info\n  }\n\n  toJSON(): TokenUsageError {\n    return {\n      name: this.name,\n      message: this.message,\n      status: this.status,\n      info: this.info,\n    }\n  }\n}\n\nexport class FetchError extends BaseError {\n  constructor(status: number, info: unknown, message?: string) {\n    super(\n      status,\n      info,\n      message ?? \"Network request failed or returned an unexpected status. Check `status` and `info` for details.\",\n    )\n    this.name = \"FetchError\"\n  }\n}\n\nexport class ModelNotFoundError extends BaseError {\n  constructor(info: unknown) {\n    super(\n      404,\n      info,\n      \"Model not found in catalog. Verify the model ID/provider, or inspect the catalog at https://models.dev.\",\n    )\n    this.name = \"ModelNotFoundError\"\n  }\n}\n\nexport class MissingMetadataError extends BaseError {\n  constructor(info: unknown) {\n    super(\n      422,\n      info,\n      \"Message metadata is missing or invalid. Expected { totalUsage: LanguageModelUsage, canonicalSlug: string }. Extra fields are allowed.\",\n    )\n    this.name = \"MissingMetadataError\"\n  }\n}\n\nexport class CostComputationError extends BaseError {\n  constructor(info: unknown) {\n    super(\n      404,\n      info,\n      \"Cost computation failed: pricing is missing for one or more models. Visit https://models.dev to see the catalog\",\n    )\n    this.name = \"CostComputationError\"\n  }\n}\n\nexport class UnknownError extends BaseError {\n  constructor() {\n    super(500, undefined, \"An unknown error occured\")\n    this.name = \"UnknownError\"\n  }\n}\n","import type { UIMessage } from \"ai\"\nimport type { TokenUsageMetadata } from \"../../shared-types\"\nimport { BaseError, UnknownError } from \"../errors\"\nimport type { NormalizedTokenUsage, TokenAccountingPolicy, TokenUsageError } from \"../types\"\n\nexport function toTokenUsageError(err: unknown) {\n  return err instanceof BaseError ? err.toJSON() : new UnknownError().toJSON()\n}\n\nexport function resultError(error: TokenUsageError) {\n  return {\n    data: undefined,\n    isLoading: false,\n    error,\n  }\n}\n\nexport function resultLoading() {\n  return {\n    data: undefined,\n    isLoading: true,\n    error: null,\n  }\n}\n\nexport function resultSuccess<T>(data: T) {\n  return {\n    data,\n    isLoading: false,\n    error: null,\n  }\n}\n\nconst DEFAULT_POLICY: TokenAccountingPolicy = { reasoningBakedIn: false }\nconst PROVIDER_POLICY: Record<string, TokenAccountingPolicy> = {\n  openai: { reasoningBakedIn: true },\n  google: { reasoningBakedIn: false },\n  anthropic: { reasoningBakedIn: false },\n}\n\nfunction getPolicy(providerId: string) {\n  return PROVIDER_POLICY[providerId] ?? DEFAULT_POLICY\n}\n\nexport function normalizeTokenUsage(message: UIMessage, stripEmptyReasoning: boolean = false): NormalizedTokenUsage {\n  const { totalUsage, canonicalSlug } = message.metadata as TokenUsageMetadata\n  const { providerId } = parseCanonicalSlug(canonicalSlug)\n\n  const policy = getPolicy(providerId)\n\n  const input = totalUsage.inputTokens ?? 0\n  const cachedInput = totalUsage.cachedInputTokens ?? 0\n  const output = totalUsage.outputTokens ?? 0\n  const reasoning = totalUsage.reasoningTokens ?? 0\n\n  const reasoningPart = message.parts.find((p) => p.type === \"reasoning\")\n  const hasEmptyReasoningPart = reasoningPart && reasoningPart.text.trim() === \"\"\n\n  // When computing the context window, strip reasoning tokens if the reasoning part is empty\n  const shouldZeroReasoning = hasEmptyReasoningPart && stripEmptyReasoning\n\n  return {\n    input,\n    output: policy.reasoningBakedIn ? Math.max(0, output - reasoning) : output,\n    reasoning: shouldZeroReasoning ? 0 : reasoning,\n    cachedInput,\n  }\n}\n\nexport function parseCanonicalSlug(slug: string): { providerId: string; modelId: string } {\n  const [providerId = \"\", modelId = \"\"] = slug.split(\"/\", 2)\n  return { providerId, modelId }\n}\n\nexport function hasInvalidTokenUsageMetadata(message: UIMessage | undefined): boolean {\n  if (!message) return false\n\n  const meta = message.metadata\n\n  // Must exist and be a non-array object\n  if (meta == null || typeof meta !== \"object\" || Array.isArray(meta)) return true\n\n  const m = meta as Record<string, unknown>\n\n  // Required fields\n  const hasCanonicalSlug = typeof m.canonicalSlug === \"string\"\n  const hasTotalUsage = typeof m.totalUsage === \"object\" && m.totalUsage !== null && !Array.isArray(m.totalUsage)\n\n  return !(hasCanonicalSlug && hasTotalUsage)\n}\n","\"use client\"\n\nimport useSWR from \"swr\"\nimport { FetchError } from \"../errors\"\nimport type { Provider } from \"../types\"\n\nasync function fetchModels<T>(url: string) {\n  const res = await fetch(url)\n\n  if (!res.ok) {\n    throw new FetchError(res.status, await res.json())\n  }\n\n  return res.json() as Promise<T>\n}\n\nexport function useModels() {\n  const { data, isLoading, error } = useSWR<Record<string, Provider>, FetchError>(\"/__models.dev\", fetchModels)\n\n  return {\n    data,\n    isLoading,\n    error,\n  }\n}\n","\"use client\"\n\nimport { CostComputationError, ModelNotFoundError } from \"../errors\"\nimport type { ModelDetails, Result } from \"../types\"\nimport { parseCanonicalSlug, resultError, resultLoading, resultSuccess } from \"./helpers\"\nimport { useModels } from \"./use-models\"\n\n/**\n * React hook that retrieves **model details**, such as pricing and token limits.\n *\n * The hook derives model information directly from the model registry based on\n * the provided {@link canonicalSlug}. It returns structured data including\n * per-token pricing (input, output, reasoning, cached input) and context/output\n * token limits.\n *\n * Internally, the hook leverages SWR and follows the SWR-style return pattern with\n * `data`, `isLoading`, and `error` states for consistent asynchronous handling.\n *\n * @param params - The parameters for the hook.\n * @param params.canonicalSlug - The canonical model identifier, composed of provider and model ID\n * (e.g. `\"openai/gpt-5\"`).\n *\n * @returns A {@link Result} object containing:\n * - `data`: The resolved {@link ModelDetails}, including pricing and limits.\n * - `isLoading`: Whether model data is still being loaded.\n * - `error`: A {@link TokenUsageError} if an error occurred.\n */\nexport function useModelDetails({ canonicalSlug }: { canonicalSlug: string }): Result<ModelDetails> {\n  const { data: models, isLoading, error } = useModels()\n\n  if (isLoading) return resultLoading()\n  if (error) return resultError(error.toJSON())\n\n  const { providerId, modelId } = parseCanonicalSlug(canonicalSlug)\n\n  const model = models?.[providerId]?.models[modelId]\n  if (!model) {\n    return resultError(new ModelNotFoundError({ providerId, modelId }).toJSON())\n  }\n  if (!model.cost) {\n    return resultError(new CostComputationError({ providerId, modelId }))\n  }\n\n  const cost = model.cost\n  const limit = model.limit\n\n  const modelDetails: ModelDetails = {\n    canonicalSlug,\n    pricing: {\n      input: cost.input,\n      output: cost.output,\n      reasoning: cost.reasoning ?? cost.output,\n      cachedInput: cost.cache_read ?? cost.input,\n    },\n    limit,\n  }\n\n  return resultSuccess<ModelDetails>(modelDetails)\n}\n","\"use client\"\n\nimport type { UIMessage } from \"ai\"\nimport { useMemo } from \"react\"\nimport { MissingMetadataError, ModelNotFoundError } from \"../errors\"\nimport type { Context, Model, Result } from \"../types\"\nimport {\n  hasInvalidTokenUsageMetadata,\n  normalizeTokenUsage,\n  parseCanonicalSlug,\n  resultError,\n  resultLoading,\n  resultSuccess,\n  toTokenUsageError,\n} from \"./helpers\"\nimport { useModels } from \"./use-models\"\n\nfunction findLast<T>(arr: readonly T[], pred: (x: T) => boolean): T | undefined {\n  for (let i = arr.length - 1; i >= 0; i--) {\n    const v = arr[i]\n    if (v === undefined) continue\n    if (pred(v)) return v\n  }\n  return undefined\n}\n\nfunction computeContext(message: UIMessage | undefined, model: Model): Context {\n  if (message && hasInvalidTokenUsageMetadata(message)) {\n    throw new MissingMetadataError({ message, metadata: message.metadata })\n  }\n\n  const breakdown = message ? normalizeTokenUsage(message, true) : { input: 0, output: 0, reasoning: 0, cachedInput: 0 }\n\n  const used = Object.values(breakdown).reduce((sum, v) => sum + v, 0)\n  const limit = model.limit.context\n  const remaining = Math.max(0, limit - used)\n  const fractionUsed = limit > 0 ? used / limit : 0\n  const percentageUsed = fractionUsed * 100\n  const isExceeded = used > limit\n\n  return {\n    breakdown,\n    used,\n    limit,\n    remaining,\n    fractionUsed,\n    percentageUsed,\n    isExceeded,\n  }\n}\n\n/**\n * React hook that provides insight into how much of the model’s context window is remaining.\n *\n * The hook derives usage information directly from the message metadata and the model’s defined\n * context window. It returns pre-computed values such as used and remaining tokens, percentage used,\n * and whether the context limit has been exceeded.\n *\n * Internally, the hook leverages SWR and follows the SWR-style return pattern with\n * `data`, `isLoading`, and `error` states for consistent asynchronous handling.\n *\n * @param params - The parameters for the hook.\n * @param params.messages - The messages in the chat, typically returned from `useChat`.\n * @param params.canonicalSlug - The canonical model identifier, composed of provider and model ID (e.g. `\"openai/gpt-5\"`).\n *\n * @returns A {@link Result} object containing:\n * - `data`: The computed {@link Context} with usage metrics.\n * - `isLoading`: Whether model data is still being loaded.\n * - `error`: A {@link TokenUsageError} if an error occurred.\n */\nexport function useTokenContext({\n  messages,\n  canonicalSlug,\n}: {\n  messages: readonly UIMessage[]\n  canonicalSlug: string\n}): Result<Context> {\n  const { data: models, isLoading, error } = useModels()\n\n  const mostRecentAssistantMessage = useMemo(\n    () => findLast(messages, (m) => m.role === \"assistant\" && m.metadata !== undefined),\n    [messages],\n  )\n\n  if (isLoading) return resultLoading()\n  if (error) return resultError(error.toJSON())\n\n  const { providerId, modelId } = parseCanonicalSlug(canonicalSlug)\n\n  const model = models?.[providerId]?.models[modelId]\n  if (!model) {\n    return resultError(new ModelNotFoundError({ modelId, providerId }).toJSON())\n  }\n\n  try {\n    return resultSuccess<Context>(computeContext(mostRecentAssistantMessage, model))\n  } catch (err) {\n    return resultError(toTokenUsageError(err))\n  }\n}\n","\"use client\"\n\nimport type { UIMessage } from \"ai\"\nimport { useMemo } from \"react\"\nimport type { TokenUsageMetadata } from \"../../shared-types\"\nimport { CostComputationError, MissingMetadataError, ModelNotFoundError } from \"../errors\"\nimport type { Cost, CostBreakdown, ModelResolver, Result } from \"../types\"\nimport {\n  hasInvalidTokenUsageMetadata,\n  normalizeTokenUsage,\n  parseCanonicalSlug,\n  resultError,\n  resultLoading,\n  resultSuccess,\n  toTokenUsageError,\n} from \"./helpers\"\nimport { useModels } from \"./use-models\"\n\nfunction computeCost(messages: readonly UIMessage[], resolveModel: ModelResolver): Cost {\n  const breakdown: CostBreakdown = {\n    input: { amount: 0, cost: 0 },\n    output: { amount: 0, cost: 0 },\n    reasoning: { amount: 0, cost: 0 },\n    cachedInput: { amount: 0, cost: 0 },\n  }\n\n  messages.forEach((m) => {\n    if (hasInvalidTokenUsageMetadata(m)) {\n      throw new MissingMetadataError({ message: m, metadata: m.metadata })\n    }\n\n    const { canonicalSlug } = m.metadata as TokenUsageMetadata\n    const { providerId, modelId } = parseCanonicalSlug(canonicalSlug)\n\n    const model = resolveModel({ providerId, modelId })\n\n    if (!model) {\n      throw new ModelNotFoundError({ providerId, modelId })\n    }\n    if (!model.cost) {\n      throw new CostComputationError({ providerId, modelId })\n    }\n\n    const tokens = normalizeTokenUsage(m)\n    const cost = model.cost\n\n    breakdown.input.amount += tokens.input\n    breakdown.input.cost += (tokens.input / 1_000_000) * cost.input\n\n    breakdown.output.amount += tokens.output\n    breakdown.output.cost += (tokens.output / 1_000_000) * cost.output\n\n    breakdown.reasoning.amount += tokens.reasoning\n    breakdown.reasoning.cost += (tokens.reasoning / 1_000_000) * (cost.reasoning ?? cost.output)\n\n    breakdown.cachedInput.amount += tokens.cachedInput\n    breakdown.cachedInput.cost += (tokens.cachedInput / 1_000_000) * (cost.cache_read ?? cost.input)\n  })\n\n  const total = Object.values(breakdown).reduce((sum, v) => sum + v.cost, 0)\n\n  return { breakdown, total, currency: \"USD\" }\n}\n\n/**\n * React hook that computes the **monetary cost** of token usage for assistant messages.\n *\n * The hook derives cost directly from message metadata and the model’s pricing\n * information. It returns pre-computed values such as the total cost, a detailed\n * cost breakdown per token type (input, output, reasoning, cached input), and the\n * currency.\n *\n * Internally, the hook leverages SWR and follows the SWR-style return pattern with\n * `data`, `isLoading`, and `error` states for consistent asynchronous handling.\n *\n * @param params - The parameters for the hook. Exactly one of the following must be provided:\n * - `messages`: All chat messages (typically returned from `useChat`).\n * - `message`: A single assistant message.\n *\n * @returns A {@link Result} object containing:\n * - `data`: The computed {@link Cost} with `breakdown`, `total`, and `currency`.\n * - `isLoading`: Whether model pricing data is still being loaded.\n * - `error`: A {@link TokenUsageError} if an error occurred.\n */\nexport function useTokenCost({ messages }: { messages: readonly UIMessage[] }): Result<Cost>\nexport function useTokenCost({ message }: { message: UIMessage }): Result<Cost>\n\nexport function useTokenCost(params: { messages: readonly UIMessage[] } | { message: UIMessage }): Result<Cost> {\n  const { data: models, isLoading, error } = useModels()\n\n  const messages: readonly UIMessage[] = \"messages\" in params ? params.messages : [params.message]\n  const assistantMessages = useMemo(\n    () => messages.filter((m) => m.role === \"assistant\" && m.metadata !== undefined),\n    [messages],\n  )\n\n  if (isLoading) return resultLoading()\n  if (error) return resultError(error.toJSON())\n\n  const resolveModel: ModelResolver = ({ providerId, modelId }) => models?.[providerId]?.models?.[modelId]\n\n  try {\n    return resultSuccess<Cost>(computeCost(assistantMessages, resolveModel))\n  } catch (err) {\n    return resultError(toTokenUsageError(err))\n  }\n}\n"]}