// THE COMPANY BEHIND CASCADEFLOW

Lemony

RUNTIME INTELLIGENCE FOR AI AGENTS

We build cascadeflow — the layer that sees every step an agent takes and steers it toward your policies and goals. Open source at the core, the full self-optimizing version in Studio.

// Partners & Investors

IBM watsonx
JetBrains
PwC
True Ventures
Alumni Ventures

// Two products, one runtime

Run it yourself. Or let it run itself.

cascadeflow starts as an MIT-licensed runtime any team can run in three lines of code — and grows into Studio: the full, managed, self-optimizing version that tunes your agents for you.

cascadeflowOpen Source · MIT

A high-performance runtime that scores every agent step across cost, latency, quality, budget, compliance, and energy — then enforces the right action in-process, with sub-5ms overhead.

bash — cascadeflow
$ pip install cascadeflow
$ cascadeflow run agent.py
[cascadeflow] scoring step · route → specialist
[cascadeflow] action=switch_model · saved 87%
[cascadeflow] overhead 3.9ms · quality 96%
3.2k+
GitHub stars
26.7k+
Downloads
90%
Cost saved
cascadeflow StudioManaged · UI + CLI

The full, self-optimizing cascadeflow — a managed runtime that tunes your agents automatically. Drive it from a visual UI or the CLI, and use the builders to set up, optimize, and adjust exactly how every agent behaves.

cascadeflow Studio dashboard
  • Fully managed, self-optimizing runtime that tunes your agents for you
  • Visual UI and CLI to run, steer, and manage other agents
  • Policy Builder and Domain Builder to set up, optimize, and adjust live
  • Custom policies & scoring dimensions beyond the six built-in
  • Domain-aware cascading routed to specialist model cascades

// The company

We build the intelligence layer for agentic AI.

Lemony Inc. is the company behind cascadeflow. As AI agents take on real work, the hard problem is no longer calling a model — it is governing thousands of autonomous decisions across cost, latency, quality, compliance, and energy.

cascadeflow sits inside agent execution and sees every step an agent takes — model calls, tool calls, and sub-agent handoffs — then steers each one toward your policies and goals. That is a vantage point proxies and model routers never get.

We ship the runtime as an open-source core so any team can adopt it, and cascadeflow Studio — the full, managed, self-optimizing version, with visual builders to set up, optimize, and adjust how every agent behaves.

Headquarters
New York, NY
Presence
New York, USA · Switzerland
Model
Open-core + Studio
Backed by
True Ventures · Alumni Ventures

// Get in touch

Talk to the team behind cascadeflow.

Questions about the open-source runtime, cascadeflow Studio, or partnering with Lemony? Reach out — we read every message.

447 Broadway, 2nd Floor #2114New York, NY 10013United States

// FAQ

Questions, answered.

Lemony Inc. is the company behind cascadeflow. Founded in 2023 and based in New York with a presence in Zurich, Switzerland, Lemony builds the runtime intelligence layer for agentic AI — the software that governs the decisions AI agents make as they do real work.

cascadeflow is an agent runtime intelligence layer that sits inside agent execution. It sees every step an agent takes — model calls, tool calls, and sub-agent handoffs — and steers each decision toward your policies and goals across cost, latency, quality, compliance, and energy.

Proxies and model routers only see requests going in and out. cascadeflow runs inside the agent's execution and sees the actual decisions being made, so it can inject your policies and KPIs in real time and steer each step — a vantage point request-level tools never get.

Yes. The cascadeflow core is open source under the MIT license and scores every step across six built-in dimensions. cascadeflow Studio is the full, managed, self-optimizing version, with visual Policy and Domain builders and a UI plus CLI to set up, optimize, and adjust how every agent behaves — including custom policies and scoring dimensions beyond the built-in six.

cascadeflow can cut inference costs by up to 90% with sub-5ms overhead, while retaining roughly 96% of frontier-model quality. It does this in part through domain-aware cascading — classifying each query and routing it to a specialist model cascade, where smaller specialized models often beat large general ones.

Start with the open-source core on GitHub (github.com/lemony-ai/cascadeflow) and the documentation at docs.cascadeflow.ai. For the managed Studio experience or an enterprise walkthrough, book a demo with the team.