Approximate client-side tokenization and context-budget visualization for agent design.
Approximation only. Real LLM tokenizers use BPE/byte-level subword encoding trained on corpus statistics. Counts here split on word boundaries + punctuation, then estimate ~4 chars/token for long runs. Use provider APIs (tiktoken, Anthropic tokenizer) for production budgets.