Metrics

Track token usage and cost metrics for agent executions

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

Expected Output:

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

Expected Output:

📊 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

For cross-session aggregate reporting — rolling up token and cost totals by day, model, or project — see Usage.

Related

• Usage
• Telemetry CLI
• Router CLI
• Models