The --metrics flag displays token usage and cost information after agent execution.
Quick Start
praisonai "Analyze this data" --metrics
Usage
Basic Metrics
praisonai "Explain quantum computing" --metrics
Metrics enabled - will display token usage and costs
╭─ Agent Info ─────────────────────────────────────────────────────────────────╮
│ 👤 Agent: DirectAgent │
│ Role: Assistant │
╰──────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────── Response ──────────────────────────────────╮
│ Quantum computing is a type of computation that harnesses quantum mechanical │
│ phenomena like superposition and entanglement... │
╰──────────────────────────────────────────────────────────────────────────────╯
📊 Metrics:
┌─────────────────────┬──────────────┐
│ Metric │ Value │
├─────────────────────┼──────────────┤
│ Model │ gpt-4o-mini │
│ Prompt Tokens │ 45 │
│ Completion Tokens │ 312 │
│ Total Tokens │ 357 │
│ Estimated Cost │ $0.0021 │
└─────────────────────┴──────────────┘
Combine with Other Features
# Metrics with planning mode
praisonai "Complex analysis task" --metrics --planning
# Metrics with guardrail (shows combined usage)
praisonai "Generate code" --metrics --guardrail "Include tests"
# Metrics with router (shows selected model)
praisonai "Simple question" --metrics --router
Metrics Displayed
| Metric | Description |
|---|
| Model | The LLM model used for the task |
| Prompt Tokens | Tokens in the input/prompt |
| Completion Tokens | Tokens in the response |
| Total Tokens | Sum of prompt + completion tokens |
| Estimated Cost | Approximate cost based on model pricing |
Use Cases
Cost Monitoring
Track costs across different prompts:
# Short prompt
praisonai "What is 2+2?" --metrics
# Expected: ~50 tokens, ~$0.0001
# Long prompt
praisonai "Write a detailed analysis of AI trends in 2025" --metrics
# Expected: ~2000 tokens, ~$0.012
Model Comparison
Compare token usage across models:
# GPT-4o-mini (cheaper)
praisonai "Explain AI" --metrics --llm openai/gpt-4o-mini
# GPT-4o (more capable)
praisonai "Explain AI" --metrics --llm openai/gpt-4o
# Claude (different pricing)
praisonai "Explain AI" --metrics --llm anthropic/claude-3-haiku-20240307
Planning Mode Metrics
See total tokens across all planning steps:
praisonai "Research and write a report" --metrics --planning
📊 Metrics (Planning Mode):
┌─────────────────────┬──────────────┐
│ Metric │ Value │
├─────────────────────┼──────────────┤
│ Planning Tokens │ 523 │
│ Execution Tokens │ 1,847 │
│ Total Tokens │ 2,370 │
│ Estimated Cost │ $0.0142 │
└─────────────────────┴──────────────┘
Cost Estimation
Cost estimates are approximate and based on publicly available pricing. Actual costs may vary based on your API plan.
Typical Costs by Model
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|
| gpt-4o-mini | $0.15 | $0.60 |
| gpt-4o | $2.50 | $10.00 |
| claude-3-haiku | $0.25 | $1.25 |
| claude-3-sonnet | $3.00 | $15.00 |
Best Practices
Use --metrics during development to optimize prompts and reduce costs before production deployment.
Optimize Prompts
Monitor token counts to identify verbose prompts that can be shortened
Choose Right Model
Use metrics to compare cost/quality tradeoffs between models
Budget Tracking
Track cumulative costs across multiple runs for budget planning
Debug Issues
High token counts may indicate prompt issues or infinite loops
