Context window utilization is monitored or bounded

Multi-turn chat applications that accumulate conversation history without any token management will eventually exceed the model's context window — at which point the API returns a 400 error and the conversation becomes unusable. Beyond hard failure, unbounded context accumulation drives token costs up linearly with conversation length. Users in long sessions pay a disproportionately high cost per message as the prompt grows. ISO 25010:2011 performance-efficiency requires that software resource consumption is proportional to task requirements. A sliding window or summarization strategy prevents both the failure mode and the cost spiral.

Add conversation history trimming before each API call in src/app/api/chat/route.ts:

const MAX_CHARS = 400_000  // ~100k tokens at ~4 chars/token; adjust per model

function trimHistory(messages: Message[]): Message[] {
  const system = messages.filter(m => m.role === 'system')
  let history = messages.filter(m => m.role !== 'system')
  while (history.join('').length > MAX_CHARS && history.length > 2) {
    history = history.slice(2)  // Drop oldest user+assistant turn pair
  }
  return [...system, ...history]
}

Also catch the API's token-limit error explicitly and return a recoverable error state rather than a 500.