sandbox-tokens

name	sandbox-tokens
description	Count, encode, and decode LLM tokens in a Code node or CodeAct action, with js-tiktoken running on the host

Token counting in the sandbox

Specifier: @nodetool-ai/sandbox-tokens. Import it at the top of the body.

One encoding's BPE ranks are several megabytes, well past the 1 MB cap on a compiled guest module, so this pack is a host module: the import resolves to a generated facade over NodeTool's own implementation. The ranks load once per process per encoding.

This is what the removed nodetool.text.CountTokens node did.

count — how many tokens a text costs

import { count } from "@nodetool-ai/sandbox-tokens";

const tokens = await count(inputs.text, "cl100k_base");
return { tokens, fitsContext: tokens < 8000 };

Encodings: cl100k_base (default — GPT-4, GPT-3.5), o200k_base (GPT-4o), p50k_base, r50k_base. Empty text is 0 without loading anything.

encode / decode — the token ids themselves

import { encode, decode } from "@nodetool-ai/sandbox-tokens";

const ids = await encode(inputs.text);
// Truncate on a token boundary rather than a character one.
return { head: await decode(ids.slice(0, 500)) };

Gotchas

• All three are async.
• 5 MB per call, the shared host-module text cap.
• tiktoken is OpenAI's tokenizer. Anthropic, Gemini, and local models count differently — treat the number as an estimate for anything else.
• Round-tripping is not free. decode(encode(t)) allocates the ranks and two passes; use count when you only need the number.