Token Limit Agent

The Token Limit Agent monitors and enforces token usage constraints for AI language model interactions. It helps manage costs, prevent abuse, and optimize resource utilization by restricting token consumption within predefined limits.

Token Limit Agent interface and configuration

Resource Notice: Token limits should be set based on your application's specific requirements and usage patterns. Monitor token usage regularly and adjust limits as needed to balance cost control with user experience.

Component Inputs

Input Text: The text content to be processed and measured for token count
Example: A user query or conversation history to be sent to an AI model
Token Limit: The maximum number of tokens allowed for processing
Example: 4096 (standard for many language models)

Component Outputs

Processed Text: The input text, potentially truncated to meet token limits
May include warning markers if truncation occurred
Safety Status: Indicator of whether token limits were respected
Values: Safe (within limits), Warning (near limit), Unsafe (limit exceeded)
Token Count: The actual token count of the processed input
Provides visibility into token usage for monitoring and optimization
Risk Score: Numerical representation of token limit risk
Scale: 0.0 (well within limits) to 1.0 (at or exceeding limits)

How It Works

The Token Limit Agent employs tokenizers compatible with various AI language models to accurately count tokens in text. When input exceeds the specified limit, the agent can apply different strategies to reduce token count while preserving the most relevant content.

Truncation Strategies

Simple Truncation: Removes content from the beginning or end of the text
Smart Truncation: Preserves key information while removing less important content
Conversation Trimming: Removes older messages in chat histories to maintain context
Compression: Summarizes content to reduce token count while preserving meaning

Use Cases

Cost Control: Prevent excessive token usage that could lead to high API costs
Resource Allocation: Distribute token usage fairly across users in multi-user systems
Performance Optimization: Ensure inputs don't exceed model context windows
Abuse Prevention: Block attempts to consume excessive resources through large inputs
Usage Analytics: Monitor and track token consumption for reporting and optimization

Implementation Example

const tokenLimiter = new TokenLimitAgent({
  limit: 4096,
  tokenizer: "gpt-3.5-turbo",
  truncationStrategy: "smart"
});

// A long document or conversation history
const inputText = "This is a very long conversation between a +
user and an AI assistant...";
const result = tokenLimiter.process(inputText);

// Output:
// {
//   processedText: "This is a very long conversation between... +
// [truncated to fit token limit]",
//   safetyStatus: "Safe",
//   tokenCount: 3982,
//   riskScore: 0.45,
//   truncated: true,
//   truncationDetails: {
//     originalTokens: 6240,
//     removedTokens: 2258,
//     preservedPercentage: 63.8
//   }
// }

Model-Specific Token Limits

Model	Token Limit	Notes
GPT-3.5-Turbo	4,096	Standard version
GPT-3.5-Turbo-16k	16,384	Extended context version
GPT-4	8,192	Base version
GPT-4-32k	32,768	Extended context version
Claude 2	100,000	Anthropic's model

Useful Resources

Best Practices

Set token limits with a buffer below the actual model maximum to account for response tokens
Implement tiered limits based on user roles or subscription levels
Consider different truncation strategies based on content type
Provide feedback to users when their input has been truncated
Monitor token usage patterns to identify optimization opportunities

Documentation