Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.
cascadeflow is an in-process runtime for AI agents that makes model and execution decisions during agent loops, including model selection, budget checks, and stop, continue, or escalation actions. It is used to manage cost, latency, quality, policy, compliance, and energy across agent frameworks, while the catalogue skills integrate those decisions into supported agent workflows.
Skill Codex
OpenClaw-native domain cascading. Use when users need cost/latency reduction via cascading, domain-aware model assignment, OpenClaw-native event handling, and command setup including /model cflow and optional /cascade stats commands.
Skill Claude CodeCodex
Use when building, extending, or debugging AI agents with cascadeflow (agent runtime intelligence layer) — installing cascadeflow (Python) or @cascadeflow/core/@cascadeflow/langchain (TypeScript); using CascadeAgent, ModelConfig, harness APIs (cascadeflow.init, cascadeflow.run, @agent from cascadeflow.harness…