cascadeflow
Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.
- 3.9k estrellas
- 885 forks
- 12 issues abiertos
- Lenguaje: Python
- Licencia: MIT
- actualizado
Instalación
git clone https://github.com/lemony-ai/cascadeflow && cp cascadeflow/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
Nota editorial
CascadeFlow is an in-process runtime intelligence layer for AI agents that handles model selection, cost control, latency optimization, and policy enforcement inside the agent execution loop rather than at the HTTP boundary. It integrates with Claude through the Anthropic API and works alongside frameworks including LangChain, CrewAI, PydanticAI, Google ADK, n8n, Vercel AI SDK, and OpenAI Agents SDK, available as both a Python package and a TypeScript npm module. The core mechanism is model cascading: routing each agent step or tool call to the most appropriate and cost-effective model based on task complexity, token budgets, quality thresholds, and business KPIs accumulated across the run. A standout benchmark result from the README shows 93% cost reduction on GSM8K math tasks while retaining 96% of GPT-5 quality. The library adds under 5ms overhead per call and supports per-tool-call budget gating and runtime stop, continue, or escalate decisions. It targets developers and teams running multi-step Claude-based agents who need transparent cost accounting and adaptive model routing without an external proxy.
1 items en este repositorio
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`, `simulate`), `withCascade`/`CascadeFlow`; picking drafter+verifier pairs; per-step budget/compliance/KPI enforcement; quality validation; complexity pre-routing; tool execution and multi-turn agent loops; presets; decision traces; or wiring cascadeflow into LangChain, OpenAI Agents, CrewAI, PydanticAI, Google ADK, n8n, or Vercel AI SDK. Also when a user mentions "cascade", "drafter/verifier", "runtime intelligence", "in-process harness", "cost-optimized agent", "agent loop with cost control", is in the lemony-ai/cascadeflow repo, or found a bug in cascadeflow/integrations needing an upstream fix/PR.
Resumen de Subagents
Lo que la gente pregunta sobre cascadeflow
¿Qué es lemony-ai/cascadeflow?
lemony-ai/cascadeflow es subagents para el ecosistema de Claude AI. Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop. Tiene 3.9k estrellas en GitHub y su última actualización registrada es del 2026-09-25.
¿Cómo se instala cascadeflow?
Puedes instalar cascadeflow clonando el repositorio (https://github.com/lemony-ai/cascadeflow) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar lemony-ai/cascadeflow?
Nuestro agente de seguridad ha analizado lemony-ai/cascadeflow y le ha asignado un Trust Score de 97/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene lemony-ai/cascadeflow?
lemony-ai/cascadeflow es mantenido por lemony-ai. La última actividad registrada en GitHub es del 2026-09-25, con 12 issues abiertos.
¿Hay alternativas a cascadeflow?
Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.