The --router flag enables intelligent model selection, automatically choosing the best model based on task complexity.
Quick Start
praisonai "Simple question" --router
Usage
Basic Router
praisonai "What is 2+2?" --router
🎯 Task complexity: simple
🤖 Selected model: gpt-4o-mini
╭─ Agent Info ─────────────────────────────────────────────────────────────────╮
│ 👤 Agent: DirectAgent │
│ Role: Assistant │
│ 🤖 Model: gpt-4o-mini (auto-selected) │
╰──────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────── Response ──────────────────────────────────╮
│ 2 + 2 equals 4. │
╰──────────────────────────────────────────────────────────────────────────────╯
Complex Task
praisonai "Analyze the implications of quantum computing on cryptography and provide a detailed technical assessment" --router
🎯 Task complexity: complex
🤖 Selected model: gpt-4-turbo
╭─ Agent Info ─────────────────────────────────────────────────────────────────╮
│ 👤 Agent: DirectAgent │
│ Role: Assistant │
│ 🤖 Model: gpt-4-turbo (auto-selected) │
╰──────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────── Response ──────────────────────────────────╮
│ # Quantum Computing Impact on Cryptography │
│ │
│ ## Executive Summary │
│ Quantum computing poses significant challenges to current cryptographic... │
│ │
│ ## Technical Analysis │
│ ### 1. Vulnerable Algorithms │
│ - RSA: Shor's algorithm can factor large numbers in polynomial time... │
│ [Detailed technical analysis continues...] │
╰──────────────────────────────────────────────────────────────────────────────╯
Specify Provider
# Prefer Anthropic models
praisonai "Complex analysis" --router --router-provider anthropic
# Prefer OpenAI models
praisonai "Simple task" --router --router-provider openai
🎯 Task complexity: complex
🏢 Provider preference: anthropic
🤖 Selected model: claude-3-opus-20240229
╭────────────────────────────────── Response ──────────────────────────────────╮
│ [Response from Claude 3 Opus] │
╰──────────────────────────────────────────────────────────────────────────────╯
Combine with Other Features
# Router with metrics (see cost savings)
praisonai "Quick question" --router --metrics
# Router with planning
praisonai "Complex project" --router --planning
# Router with guardrail
praisonai "Generate code" --router --guardrail "Follow best practices"
How It Works
- Prompt Analysis: The router analyzes your prompt
- Complexity Assessment: Determines task complexity (simple/medium/complex)
- Model Selection: Chooses appropriate model based on complexity
- Execution: Runs the task with the selected model
Complexity Levels
| Level | Indicators | Model Selection |
|---|
| Simple | Short questions, basic math, simple lookups | gpt-4o-mini, claude-3-haiku |
| Medium | Explanations, summaries, moderate analysis | gpt-4o, claude-3-sonnet |
| Complex | Deep analysis, code generation, research | gpt-4-turbo, claude-3-opus |
Model Selection Matrix
OpenAI Models
| Complexity | Model | Cost (per 1M tokens) |
|---|
| Simple | gpt-4o-mini | 0.15/0.60 |
| Medium | gpt-4o | 2.50/10.00 |
| Complex | gpt-4-turbo | 10.00/30.00 |
Anthropic Models
| Complexity | Model | Cost (per 1M tokens) |
|---|
| Simple | claude-3-haiku | 0.25/1.25 |
| Medium | claude-3-sonnet | 3.00/15.00 |
| Complex | claude-3-opus | 15.00/75.00 |
Cost Savings Example
Without router (always using gpt-4-turbo):
praisonai "What is Python?" --metrics
# Tokens: 150, Cost: $0.0045
praisonai "What is Python?" --router --metrics
# Tokens: 150, Cost: $0.0001
# Savings: 97%
🎯 Task complexity: simple
🤖 Selected model: gpt-4o-mini
💰 Estimated savings: 97% vs gpt-4-turbo
╭────────────────────────────────── Response ──────────────────────────────────╮
│ Python is a high-level, interpreted programming language known for its │
│ simple syntax and readability... │
╰──────────────────────────────────────────────────────────────────────────────╯
📊 Metrics:
┌─────────────────────┬──────────────┐
│ Metric │ Value │
├─────────────────────┼──────────────┤
│ Model │ gpt-4o-mini │
│ Total Tokens │ 150 │
│ Estimated Cost │ $0.0001 │
│ vs gpt-4-turbo │ $0.0045 │
│ Savings │ 97% │
└─────────────────────┴──────────────┘
Use Cases
Cost Optimization
# Let router choose the most cost-effective model
praisonai "Answer user questions" --router --metrics
Quality Assurance
# Ensure complex tasks get powerful models
praisonai "Write a comprehensive security audit" --router
Batch Processing
# Process many queries efficiently
for query in "${queries[@]}"; do
praisonai "$query" --router
done
Complexity Indicators
The router considers these factors:
Simple Tasks
- Short prompts (< 50 words)
- Basic questions
- Simple calculations
- Factual lookups
- Yes/no questions
Complex Tasks
- Long prompts (> 200 words)
- Technical analysis
- Code generation
- Multi-step reasoning
- Creative writing
- Research tasks
Best Practices
Use --router --metrics to see the cost savings from automatic model selection.
The router adds a small overhead for complexity analysis. For known simple tasks, you may prefer to specify the model directly with --llm.
Cost Monitoring
Use --metrics to track savings
Provider Preference
Use --router-provider for specific needs
Override When Needed
Use --llm to override router selection
Batch Operations
Router is ideal for varied batch tasks
