Documentation Index
Fetch the complete documentation index at: https://docs.cascadeflow.ai/llms.txt
Use this file to discover all available pages before exploring further.
TypeScript API Reference
cascadeflow provides three TypeScript packages for different integration patterns.
Quick Start
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('Summarize this document');
console.log(result.content);
console.log(`Model: ${result.modelUsed}, Cost: $${result.totalCost}`);
Packages
| Package | Purpose | Docs |
|---|
@cascadeflow/core | CascadeAgent with speculative execution and cost tracking | Reference |
@cascadeflow/vercel-ai | Vercel AI SDK middleware with streaming and tool loops | Reference |
@cascadeflow/langchain | LangChain withCascade() wrapper and model discovery | Reference |
Install
# Core package
npm install @cascadeflow/core
# Vercel AI SDK integration
npm install @cascadeflow/vercel-ai
# LangChain integration
npm install @cascadeflow/langchain @langchain/core @langchain/openai
Core Concepts
Speculative execution — the cheaper model (drafter) runs first. If its response passes quality validation, cascadeflow returns it without calling the expensive model. If not, the verifier model runs as fallback.
Quality validation — configurable confidence threshold, minimum token count, and optional semantic validation determine whether a draft is accepted.
Cost tracking — every response includes totalCost and savingsPercentage so you can measure optimization impact.
Integration Patterns
Standalone Agent
Use @cascadeflow/core directly for full control:
const result = await agent.run('What is TypeScript?');
// result.modelUsed → 'gpt-4o-mini' (if draft accepted)
// result.savingsPercentage → 94
Vercel AI SDK
Use @cascadeflow/vercel-ai for Next.js and Edge deployments:
import { createChatHandler } from '@cascadeflow/vercel-ai';
const handler = createChatHandler(agent, {
protocol: 'data',
maxSteps: 5,
});
LangChain
Use @cascadeflow/langchain for LCEL chains and pipelines:
import { withCascade } from '@cascadeflow/langchain';
const cascade = withCascade({
drafter: new ChatOpenAI({ model: 'gpt-4o-mini' }),
verifier: new ChatAnthropic({ model: 'claude-sonnet-4' }),
});
const chain = prompt.pipe(cascade).pipe(new StringOutputParser());
Runtime Support
All packages work in Node.js, Browser, and Edge Functions (Vercel Edge, Cloudflare Workers).
