Token
A token is the basic unit of text that large language models (LLMs) process. Instead of working character-by-character or word-by-word, LLMs operate on tokens — chunks of text that are determined by the model's tokenizer.
What Is a Token?
Tokens don't map cleanly to words or characters. They're determined by frequency patterns in training data:
- → Common words are often a single token (
the,agent,task) - → Rare words are split into multiple tokens (
tokenization→token+ization) - → Code and symbols often tokenize differently than prose
- → Roughly 1 token ≈ 4 characters ≈ 0.75 words in English
Why Tokens Matter for Agents
Tokens matter in three key ways:
1. Context Window Size
Every LLM has a maximum context window measured in tokens. Claude's models support up to 200K tokens. Everything the model sees — system prompt, conversation history, tool results, injected data — counts against this limit.
2. API Cost
Most LLM providers charge per token for both input (prompt) and output (completion). Long-running agentic workflows with many tool calls can accumulate significant token costs.
3. Latency
More tokens to process = more time to generate a response. Agents with very long context windows will have higher latency than those with trimmed, focused prompts.
Token Usage in AgentRQ
AgentRQ helps minimize token waste by externalizing agent-human communication. Instead of maintaining long conversation histories inside the model's context, agents use reply and getTaskMessages MCP tools to communicate, keeping the agent's context window lean.
Token Estimation
| Content | Approximate Tokens |
|---|---|
| This glossary page | ~600 tokens |
| A 500-line Go file | ~2,000 tokens |
| A 10-page PDF | ~5,000 tokens |
| Claude's max context | 200,000 tokens |