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Set token limits so your Rust agents stay within budget and never generate unexpectedly long responses.

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Quick Start

1

Set Output Token Limit

use praisonai::Agent;

let agent = Agent::new()
    .name("Assistant")
    .instructions("You are concise")
    .max_tokens(500)  // Limit output to 500 tokens
    .build()?;

let response = agent.chat("Explain quantum computing").await?;
println!("{}", response);
2

Track Token Usage

use praisonai::Agent;

let agent = Agent::new()
    .name("Assistant")
    .instructions("You are helpful")
    .build()?;

let response = agent.chat("Hello").await?;

// Check usage after the call
let usage = agent.token_usage();
println!("Input tokens:  {}", usage.input_tokens);
println!("Output tokens: {}", usage.output_tokens);
println!("Total tokens:  {}", usage.total_tokens);
3

Set Context Window Limit

use praisonai::Agent;

let agent = Agent::new()
    .name("Assistant")
    .instructions("You summarize long documents")
    .max_tokens(1000)      // Output limit
    .max_context(8192)     // Context window limit
    .build()?;

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How It Works

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Configuration Options

OptionTypeDefaultDescription
max_tokensu32provider defaultMaximum output tokens per request
max_contextu32provider defaultMaximum context window size

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Common Patterns

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Budget-Aware Loops

Stop processing when cumulative cost gets too high: 
use praisonai::Agent;

let agent = Agent::new()
    .name("Analyst")
    .instructions("Analyze text")
    .max_tokens(200)
    .build()?;

let questions = vec!["Q1", "Q2", "Q3"];
let token_limit = 1000u32;

for question in &questions {
    let usage = agent.token_usage();
    if usage.total_tokens >= token_limit {
        println!("Token budget exhausted after {} tokens", usage.total_tokens);
        break;
    }
    let answer = agent.chat(question).await?;
    println!("{}", answer);
}

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Verbose Usage Reporting

use praisonai::Agent;

let agent = Agent::new()
    .name("Reporter")
    .instructions("Generate reports")
    .max_tokens(800)
    .verbose(true)  // Logs token usage after each request
    .build()?;

agent.chat("Write a weekly summary").await?;

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Best Practices

Always set max_tokens when you know the expected output length. For short answers use 200, for summaries 500–1000, for detailed analysis 2000+. This prevents runaway generation and unexpected costs.
Call agent.token_usage() after each request to track total spend. In loops or multi-turn conversations, check the running total and stop when approaching your budget.
Set max_context slightly below the model’s documented limit to leave headroom. Running at 100% of the context window can cause errors with some providers.
Enable .verbose(true) during development to see token counts logged automatically after each request. Disable in production to reduce noise.

Budget

Dollar-based spending limits

Context Management

Context window strategies