Readit NewsHi HN,
I'm launching cascadeflow – an open-source tool for AI model cascading that can reduce your AI provider costs by 30-65% with just 3 lines of code.
The core insight: After a year of working with small language models and domain-specific models (especially on edge devices), I found that 80% of queries can be handled by cheaper, smaller models. Only the complex 20% actually need flagship models.
How it works: 1. Route queries to a cheap "drafter" model first 2. Validate the response quality 3. If quality passes, return it (fast + cheap) 4. If not, escalate to an expensive "verifier" model
We're seeing 40-85% cost savings in production workflows, with 70-80% of queries never touching the expensive model.
Available for Python and TypeScript, with integrations for n8n and LiteLLM. MIT licensed.
GitHub: https://github.com/lemony-ai/cascadeflow
This is Day 2 of our release sprint. Would love to hear your feedback, especially if you're dealing with high AI API costs or running models on resource-constrained environments.
After analyzing hundreds of production agent workflows, we discovered something: 40-70% of agent tool calls and text prompts don't need expensive flagship models. Yet most implementations route everything through their selected flagship model.
Here's what that looks like in practice:
A customer support agent handling 1,000 queries/day: - Current cost: ~$225/month - Actual need: 60% could use smaller or domain specific models (faster, cheaper) - Wasted spend: $135/month per agent
A data analysis agent making 5,000 tool calls/day: - Current cost: ~$1,125/month - Actual need: 70% are simple operations - Wasted spend: $787/month
Multiply this across multiple agents, and you're looking at hundreds in unnecessary costs per month.
The root cause? Agent frameworks don't differentiate between "check database status" and "analyze complex business logic" - they treat every call the same.
The Solution: Intelligent Model Cascading
We built CascadeFlow's LangChain integration as a drop-in replacement that:
1. Tries fast, cheap models first 2. Validates response quality automatically 3. Escalates to flagship models only when needed 4. Tracks costs per query in real-time
The integration is dead simple - it works exactly like any LangChain chat model. No architecture changes. Just swap your chat model for CascadeFlow.
What you get: - Full LCEL chain support - Streaming and tool calling - LangSmith tracing out of the box - 40-85% cost reduction - 2-10x faster responses for simple queries - Zero quality loss
Real production results from teams already using it.
Open source, MIT licensed. Takes 5 minutes to integrate.