Use model cascading and budget-aware routing to balance cost and response quality.
Detailed Introduction
CascadeFlow, developed by Lemony, is an open-source framework focused on model cascading strategies that intelligently route requests across multiple Large Language Models (LLM) to reduce serving costs while preserving response quality. It provides a policy engine, budget and cost transparency, and adapters for multiple backends, enabling teams to compose low-cost and high-quality models into cooperative pipelines suitable for large-scale, cost-sensitive inference workloads.
Main Features
- Policy-based model cascading and routing: choose models based on confidence, budget, or custom rules.
- Cost and budget controls: real-time budget constraints, cost metrics and visualization for optimization and auditing.
- Multiple backend adapters: support for OpenAI, Anthropic, Hugging Face, VLLM and others.
- Developer-friendly: Python SDK, example workflows and documentation for easy integration.
Use Cases
- Cost-sensitive online Q&A and support systems that reduce high-cost model calls under heavy load.
- Hybrid deployments that combine local lightweight models with cloud-hosted high-quality models for compliance or latency requirements.
- Automated pipelines where requests are split and assigned to cooperating agents based on rules and metrics.
Technical Features
- Policy engine: configurable rules based on confidence thresholds, budgets and latency goals.
- Plugin adapters: connect different LLM providers and retrieval components via adapters.
- Observability: built-in call metrics, cost analytics and event logs for tuning and auditing.