# lemony-ai/cascadeflow

Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.

Repository: https://github.com/lemony-ai/cascadeflow
Canonical: https://ross.abutalabs.com/products/cascadeflow
Homepage: https://cascadeflow.ai
Language: Python
License: MIT
License Family: permissive
Topics: agent, ai, anthropic, api, budgets, claude, cost-optimization, gpt, huggingface, llm, n8n, ollama, openai, python, together-ai, typescript, vllm, cost-transparency, model-cascading, google-adk
Last push: 2026-08-06T19:29:00+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 77, longevity 22
- inputs: {"age_days": 313, "days_push": 27, "days_rel": 153, "gap_med": 7, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3980, forks 912 (observed 2026-08-28T04:08:31.678750+00:00)

## What it is
Cascadeflow is an open-source agent runtime intelligence layer (Python and TypeScript SDKs) that sits inside the AI agent loop to observe, score, and enforce decisions on every model call, tool call, and sub-agent handoff. It optimizes cost, latency, quality, budget, compliance, and energy in real time with sub-5ms overhead, supporting 17+ LLM providers and frameworks like LangChain, OpenAI Agents SDK, CrewAI, Vercel AI, and n8n.

## Use cases
- reduce LLM costs in agent workflows by cascading to cheaper models
- enforce spending budgets on AI agent runs
- route model calls based on cost, latency, and quality tradeoffs
- add policy and compliance enforcement to agent execution
- audit and track every step of an AI agent loop
- switch models mid-run when predicted cost exceeds thresholds
- block unsafe tool calls in autonomous agents

## When to choose
- you run multi-step agents and need per-step cost/quality governance
- you want model cascading to cut LLM spend without losing quality
- you need in-process enforcement with minimal latency overhead instead of an external proxy
- you use LangChain, OpenAI Agents SDK, CrewAI, or n8n and want drop-in runtime controls

## When to avoid
- you only need simple request-level model routing without agent context
- your stack has no supported SDK or agent framework integration
- you need a hosted managed gateway rather than an in-process library
- your project is not LLM/agent-based

## Facets
- artifact type: library
- maturity: active
- function: agent-framework, llm-inference, monitoring, rate-limiting, middleware
- domain: large-language-models, developer-tools, artificial-intelligence
- platform: python, cross-platform, cli
- tags: model-cascading, cost-optimization, budget-enforcement, agent-runtime, llm-routing, policy-enforcement, langchain, openai, anthropic, typescript-sdk, ai-agents, nodejs

## Member repositories
- lemony-ai/cascadeflow (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:31.678750+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:24:17.180999+00:00, confidence not recorded.
  - readme: https://github.com/lemony-ai/cascadeflow (fetched 2026-08-28T04:08:31.678750+00:00, sha d49a84e8bed4)
  - homepage: https://cascadeflow.ai (fetched 2026-08-29T09:17:38.776726+00:00, sha 6fb3e5b29946)
  - site_page: https://docs.cascadeflow.ai (fetched 2026-08-29T09:17:38.786062+00:00, sha 69379877732a)
  - registry_pypi: https://pypi.org/pypi/cascadeflow/json (fetched 2026-08-29T09:17:38.787991+00:00, sha 759684842be7)
- Data as of 2026-08-30T08:39:29.467469+00:00.