Function estimateMessageTokens

src/services/compact/microCompact.ts:164–205

(messages: Message[])

 * Pads estimate by 4/3 to be conservative since we're approximating
 */
export function estimateMessageTokens(messages: Message[]): number {
  let totalTokens = 0

  for (const message of messages) {
    if (message.type !== 'user' && message.type !== 'assistant') {
      continue
    }

    if (!Array.isArray(message.message.content)) {
      continue
    }

    for (const block of message.message.content) {
      if (block.type === 'text') {
        totalTokens += roughTokenCountEstimation(block.text)
      } else if (block.type === 'tool_result') {
        totalTokens += calculateToolResultTokens(block)
      } else if (block.type === 'image' || block.type === 'document') {
        totalTokens += IMAGE_MAX_TOKEN_SIZE
      } else if (block.type === 'thinking') {
        // Match roughTokenCountEstimationForBlock: count only the thinking
        // text, not the JSON wrapper or signature (signature is metadata,
        // not model-tokenized content).
        totalTokens += roughTokenCountEstimation(block.thinking)
      } else if (block.type === 'redacted_thinking') {
        totalTokens += roughTokenCountEstimation(block.data)
      } else if (block.type === 'tool_use') {
        // Match roughTokenCountEstimationForBlock: count name + input,
        // not the JSON wrapper or id field.
        totalTokens += roughTokenCountEstimation(
          block.name + jsonStringify(block.input ?? {}),
        )
      } else {
        // server_tool_use, web_search_tool_result, etc.
        totalTokens += roughTokenCountEstimation(jsonStringify(block))
      }
    }
  }

  // Pad estimate by 4/3 to be conservative since we're approximating
  return Math.ceil(totalTokens * (4 / 3))
}

export type PendingCacheEdits = {
  trigger: 'auto'