cascadeflow Quick Start Guide (TypeScript)

cascadeflow Quick Start Guide (TypeScript)

Get started with cascadeflow in TypeScript/JavaScript in 5 minutes. This guide walks you through the basics of intelligent model cascading.

📚 Table of Contents

• What is cascadeflow?
• Installation
• Your First Cascade
• How It Works
• Understanding Costs
• Configuration Options
• Best Practices
• Troubleshooting
• Next Steps

What is cascadeflow?

cascadeflow is an intelligent model router that saves you 40-60% on AI costs by automatically using cheaper models when possible and only escalating to expensive models when needed.

The Problem

Using GPT-4o for everything is expensive:

10,000 queries/month × $0.005/query = $50/month

But using GPT-4o-mini for everything sacrifices quality.

The Solution

cascadeflow tries the cheap model first, checks quality, and only uses the expensive model if needed:

Simple query → GPT-4o-mini ✅ (draft accepted) → Cost: $0.0004
Complex query → GPT-4o-mini ❌ (draft rejected) → GPT-4o ✅ → Cost: $0.006

Result: 40-60% savings while maintaining quality!

Installation

Step 1: Install cascadeflow

npm install @cascadeflow/core

Step 2: Install Provider SDKs

Install the SDK for your chosen provider:

# OpenAI (most common)
npm install openai

# Anthropic
npm install @anthropic-ai/sdk

# Groq
npm install groq-sdk

Step 3: Set Up API Key

# OpenAI
export OPENAI_API_KEY="sk-..."

# Or add to your .env file
echo "OPENAI_API_KEY=sk-..." >> .env

Step 4: Verify Installation

node -e "import('@cascadeflow/core').then(m => console.log('✅ Installed'))"

Your First Cascade

Create a file called my-first-cascade.ts:

import { CascadeAgent, ModelConfig } from '@cascadeflow/core';

async function main() {
  // Configure cascade with two tiers
  const agent = new CascadeAgent({
    models: [
      // Tier 1: Cheap model (tries first)
      {
        name: 'gpt-4o-mini',
        provider: 'openai',
        cost: 0.000375,  // $0.375 per 1M tokens (blended)
      },

      // Tier 2: Expensive model (only if needed)
      {
        name: 'gpt-4o',
        provider: 'openai',
        cost: 0.00625,  // $6.25 per 1M tokens (blended)
      },
    ],
  });
  // Quality validation uses default cascade-optimized config (0.7 threshold)
  // See "Configuration Options" section below to customize

  // Try a simple query
  const result = await agent.run('What color is the sky?');

  console.log(`Response: ${result.content}`);
  console.log(`Model used: ${result.modelUsed}`);
  console.log(`Cost: $${result.totalCost.toFixed(6)}`);
  console.log(`Draft accepted: ${result.draftAccepted}`);
}

main().catch(console.error);

Run it:

npx tsx my-first-cascade.ts

Expected output:

Response: The sky is typically blue during the day.
Model used: gpt-4o-mini
Cost: $0.000081
Draft accepted: true

What happened?

1. Query sent to GPT-4o-mini (cheap)
2. Response passed quality check
3. GPT-4o was NOT called (saved money!)

How It Works

The Cascade Process

┌─────────────────┐
│  Your Query     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Complexity     │ ─────► Simple/Moderate/Complex
│  Detection      │
└────────┬────────┘
         │
         ▼
   ┌─────────────┐
   │ Direct to   │ ───► Very simple → GPT-4o-mini only
   │ GPT-4o-mini?│ ───► Very complex → GPT-4o directly
   └──────┬──────┘
          │ Maybe cascade
          ▼
┌─────────────────┐
│ GPT-4o-mini     │ ────► Generate response
│ Draft           │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Quality Check   │ ────► Confidence > threshold?
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
    ▼         ▼
  PASS      FAIL
    │         │
    │    ┌────────────────┐
    │    │  GPT-4o Verify │
    │    └────────┬───────┘
    │             │
    └─────────────┘
         │
         ▼
   ┌──────────────┐
   │  Final       │
   │  Response    │
   └──────────────┘

Key Concepts

1. Draft Model

• Purpose: Try to answer with cheap model
• Cost: Low (~$0.000375 per 1K tokens)
• Speed: Fast
• Quality: Good for simple queries

2. Verifier Model

• Purpose: Verify draft or handle complex queries
• Cost: Higher (~$0.00625 per 1K tokens)
• Speed: Slower
• Quality: Best quality

3. Quality Check

• Checks: Confidence score, alignment, coherence
• Threshold: Configurable (default: 0.7)
• Result: Pass → use draft, Fail → use verifier

4. Draft Accepted vs Rejected

Draft Accepted ✅

• Cheap model response is good enough
• Verifier is NOT called
• Cost = cheap model only
• This is where you save money!

Draft Rejected ❌

• Cheap model response not good enough
• BOTH models are called
• Cost = cheap + expensive
• Quality is ensured

Understanding Costs

Token-Based Pricing

cascadeflow uses actual token-based pricing, not flat rates:

// Your query
const query = "What is TypeScript?";  // ~4 tokens

// Model's response
const response = "TypeScript is a programming language...";  // ~50 tokens

// Total tokens
const total = 4 (input) + 50 (output) = 54 tokens

// Cost calculation (GPT-4o-mini example)
const inputCost  = (4 / 1000) × $0.00015 = $0.0000006
const outputCost = (50 / 1000) × $0.0006 = $0.000030
const totalCost  = $0.0000306

Cost Breakdown by Scenario

Scenario 1: Draft Accepted (Best Case)

Query → GPT-4o-mini ✅ (accepted)

Costs:
  GPT-4o-mini: $0.000031
  GPT-4o:      $0.000000 (not called)
  ─────────────────
  Total:       $0.000031

Savings: ~95% vs GPT-4o only

Scenario 2: Draft Rejected (Worst Case)

Query → GPT-4o-mini ❌ (rejected) → GPT-4o ✅

Costs:
  GPT-4o-mini: $0.000031
  GPT-4o:      $0.000650
  ─────────────────
  Total:       $0.000681

Savings: -5% vs GPT-4o only (paid extra for GPT-4o-mini)

Scenario 3: Direct Routing

Query → GPT-4o directly (complex query)

Costs:
  GPT-4o-mini: $0.000000 (not called)
  GPT-4o:      $0.000650
  ─────────────────
  Total:       $0.000650

Savings: 0% (same as GPT-4o only)

Expected Savings

Your savings depend on your query mix:

Rule of thumb: The more simple queries, the more you save!

Configuration Options

Model Configuration

import { ModelConfig } from '@cascadeflow/core';

const modelConfig: ModelConfig = {
  name: 'gpt-4o-mini',               // Model name
  provider: 'openai',                // Provider (openai, anthropic, groq, ollama)
  cost: 0.000375,                    // Cost per 1K tokens (blended estimate)
  apiKey: process.env.OPENAI_API_KEY // Optional: override default API key
};

Agent Configuration

const agent = new CascadeAgent({
  models: [tier1, tier2],            // List of models (ordered by cost)
  verbose: true,                     // Enable logging
  enableCascade: true,               // Enable cascade system
});

Quality Configuration

Quality validation is controlled via the quality option on the agent:

import { CascadeAgent } from '@cascadeflow/core';

// Option 1: Use default (recommended for cascading)
const agent = new CascadeAgent({
  models: [...],
  // Default quality config automatically applied (0.7 threshold)
});

// Option 2: Customize quality settings
const agent = new CascadeAgent({
  models: [...],
  quality: {
    threshold: 0.7,                  // Confidence threshold (0.0-1.0)
    requireMinimumTokens: 10,        // Minimum response length
  },
});

// Option 3: Enable semantic validation with ML
const agent = new CascadeAgent({
  models: [...],
  quality: {
    threshold: 0.40,                 // Traditional confidence threshold
    requireMinimumTokens: 5,
    useSemanticValidation: true,     // Enable ML-based validation
    semanticThreshold: 0.5,          // 50% minimum similarity
  },
});

Quality Threshold Trade-offs:

• Higher threshold (0.8+) → Better quality, fewer drafts accepted, lower savings
• Medium threshold (0.7) → Balanced quality and savings (recommended)
• Lower threshold (0.6-) → More drafts accepted, higher savings, occasional quality issues

Best Practices

1. Choose the Right Models

Good Combinations:

• GPT-4o-mini → GPT-4o (balanced, recommended)
• Claude Haiku → GPT-4o (cross-provider)
• Llama 3.1 8B (Groq) → GPT-4o (maximum savings)

Avoid:

• Similar-tier models (GPT-4o-mini → GPT-3.5 Turbo)
• Reverse ordering (GPT-4o → GPT-4o-mini)

2. Tune Quality Thresholds

Start with default (0.7) and adjust based on your needs:

// Track acceptance rates
const results: boolean[] = [];
for (const query of yourQueries) {
  const result = await agent.run(query);
  results.push(result.draftAccepted || false);
}

const acceptanceRate = results.filter(Boolean).length / results.length;
console.log(`Draft acceptance rate: ${(acceptanceRate * 100).toFixed(1)}%`);

If acceptance rate is:

• < 30% → Lower threshold (0.6) or use better draft model
• 30-70% → Perfect! (balanced)

• 70% → Can raise threshold (0.75) for better quality

3. Monitor Costs

// Track costs over time
let totalCost = 0;
for (const query of yourQueries) {
  const result = await agent.run(query);
  totalCost += result.totalCost;
}

console.log(`Total cost: $${totalCost.toFixed(6)}`);
console.log(`Average per query: $${(totalCost / yourQueries.length).toFixed(6)}`);

4. Handle Failures Gracefully

try {
  const result = await agent.run(query);
  console.log(result.content);
} catch (error) {
  console.error('Error:', error);
  // Fallback logic here
}

5. Use Appropriate Max Tokens

// Short responses (save cost)
const result = await agent.run(query, { maxTokens: 50 });

// Medium responses (balanced)
const result = await agent.run(query, { maxTokens: 150 });

// Long responses (quality)
const result = await agent.run(query, { maxTokens: 500 });

Troubleshooting

Issue: All Queries Go to Expensive Model

Symptoms:

• Draft acceptance rate < 10%
• Costs almost same as GPT-4 only

Solutions:

1. Lower quality threshold:

   const agent = new CascadeAgent({
     models: [...],
     quality: { threshold: 0.6 }
   });
   

2. Use better draft model: Try GPT-4o-mini (already recommended)
3. Check query complexity: Ensure you have simple queries in your mix

Issue: Poor Quality Responses

Symptoms:

• Draft acceptance rate > 80%
• Responses are incorrect or low quality

Solutions:

1. Raise quality threshold:

   const agent = new CascadeAgent({
     models: [...],
     quality: { threshold: 0.75 }
   });
   

2. Use better verifier model: Try GPT-4o instead of GPT-4
3. Enable verbose mode to see quality scores: verbose: true

Issue: High Latency

Symptoms:

• Responses take too long
• Users complaining about wait times

Solutions:

1. Use faster models: Groq Llama for draft, GPT-4o-mini for verifier
2. Reduce max_tokens: maxTokens: 100
3. Skip cascade for time-critical queries

Issue: Costs Higher Than Expected

Symptoms:

• Savings < 30%
• Many drafts rejected

Possible Causes:

1. Query mix too complex (mostly hard queries)
2. Quality threshold too high (rejecting good drafts)
3. Token estimates inaccurate

Solutions:

1. Analyze your query complexity distribution
2. Lower quality threshold slightly
3. Use cheaper draft model (Groq Llama, Ollama)

Issue: TypeScript Type Errors

Symptoms:

• Compilation errors about missing types
• IDE not showing autocomplete

Solutions:

1. Ensure TypeScript version 4.5+:

   npm install -D typescript@latest
   

2. Check tsconfig.json includes:

   {
     "compilerOptions": {
       "moduleResolution": "node16",
       "module": "ES2022"
     }
   }
   

Next Steps

1. Run the Basic Example

cd packages/core/examples/nodejs
npx tsx basic-usage.ts

2. Customize for Your Use Case

• Modify models
• Adjust thresholds
• Add your queries

3. Read Advanced Guides

• Multi-Provider Setup
• Custom Validation
• Browser/Edge Deployment

4. Explore More Examples

• Tool Calling: tool-calling.ts
• Cost Tracking: cost-tracking.ts
• Multi-Provider: multi-provider.ts
• Reasoning Models: reasoning-models.ts
• Semantic Quality: semantic-quality.ts
• Production Patterns: production-patterns.ts

5. Deploy to Production

• Set up monitoring
• Configure logging
• Implement fallbacks
• Track costs

6. Join the Community

• ⭐ Star the GitHub repo
• 💬 Join Discussions
• 🐛 Report issues
• 🤝 Contribute examples

Quick Reference

Common Commands

# Install
npm install @cascadeflow/core openai

# Run example
npx tsx my-cascade.ts

# Check types
npx tsc --noEmit

# Run with watch mode
npx tsx watch my-cascade.ts

Code Snippets

Basic Usage:

import { CascadeAgent } from '@cascadeflow/core';

const agent = new CascadeAgent({
  models: [
    { name: 'gpt-4o-mini', provider: 'openai', cost: 0.000375 },
    { name: 'gpt-4o', provider: 'openai', cost: 0.00625 },
  ],
});

const result = await agent.run('Your query here');

Check Result:

console.log(`Response: ${result.content}`);
console.log(`Model: ${result.modelUsed}`);
console.log(`Cost: $${result.totalCost.toFixed(6)}`);
console.log(`Draft accepted: ${result.draftAccepted}`);

Track Costs:

const total = results.reduce((sum, r) => sum + r.totalCost, 0);
console.log(`Total: $${total.toFixed(6)}`);

With Tools:

const result = await agent.run(query, {
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_weather',
        description: 'Get weather for a location',
        parameters: {
          type: 'object',
          properties: {
            location: { type: 'string' }
          }
        }
      }
    }
  ]
});

TypeScript-Specific Features

Full Type Safety

import { CascadeAgent, ModelConfig, CascadeResult } from '@cascadeflow/core';

const models: ModelConfig[] = [
  { name: 'gpt-4o-mini', provider: 'openai', cost: 0.000375 },
  { name: 'gpt-4o', provider: 'openai', cost: 0.00625 },
];

const agent = new CascadeAgent({ models });
const result: CascadeResult = await agent.run('Hello');

// IDE autocomplete for all properties
result.content;
result.modelUsed;
result.totalCost;
result.draftAccepted;
result.cascaded;

Async/Await

All operations are async - use await or .then():

// Using await
const result = await agent.run(query);

// Using .then()
agent.run(query).then(result => {
  console.log(result.content);
});

Universal Runtime Support

Works in Node.js, browser, and edge runtimes:

// Node.js
import { CascadeAgent } from '@cascadeflow/core';

// Browser/Edge
import { CascadeAgent } from '@cascadeflow/core';
// Same code works everywhere!

Support

Need help?

• 💬 Ask in Discussions
• 🐛 Report a bug
• 📧 Email hello@lemony.ai

Happy Cascading! 🌊