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The homepage does not describe a trial for Studio. It does state that the cascadeflow core is MIT licensed and free to run, which lets teams evaluate the underlying runtime before any commercial commitment.
Learn moreRuntime intelligence that steers every AI agent step toward your policies

Lemony builds cascadeflow, an MIT-licensed runtime that sits inside AI agent execution, scores every model and tool call against cost, latency, quality, budget, compliance and energy, and steers each step accordingly. A managed Studio edition adds visual policy builders and self-optimizing routing.
Lemony Inc. is the New York and Zurich based company behind cascadeflow, a runtime intelligence layer for AI agents. Rather than sitting at the edge of an agent's traffic like a proxy or a model router, cascadeflow runs inside the agent's execution loop. It observes model calls, tool calls and sub-agent handoffs, scores each step across cost, latency, quality, budget, compliance and energy, and then enforces the appropriate action in process. The company claims this scoring adds under 5ms of overhead. The product ships in two forms. The core cascadeflow runtime is MIT licensed and installable with pip, designed to be dropped into an existing agent script in a few lines of code. On top sits cascadeflow Studio, a managed and self-optimizing edition that adds a visual interface, a CLI, and Policy and Domain builders for setting up and adjusting how agents behave, including custom scoring dimensions beyond the six built in. Studio also supports domain-aware cascading, which classifies each query and routes it to a specialist model cascade instead of a single large general model. The tool is aimed at engineering and platform teams running autonomous agents in production, particularly those where inference spend, response latency or compliance review have become operational problems. In practice a team installs the open-source runtime, points it at an agent, and watches it score and reroute steps; upgrading to Studio moves that tuning into a managed control plane with visual policy editing. Where alternatives stop at request-level routing, cascadeflow's pitch is visibility into the decisions themselves. The tradeoff is commitment: it is infrastructure for teams already building agents, not a drop-in tool for casual users, and the strongest capabilities sit behind the managed tier.
The homepage does not describe a trial for Studio. It does state that the cascadeflow core is MIT licensed and free to run, which lets teams evaluate the underlying runtime before any commercial commitment.
Learn moreNo public price list appears on the homepage. The core cascadeflow runtime is open source under the MIT license. cascadeflow Studio is the managed, self-optimizing edition, and the homepage directs interested teams to book a demo rather than quoting tiers or rates.
Learn moreSupport runs through a contact email and a demo booking link for Studio and enterprise walkthroughs. The open-source runtime points to its GitHub repository and separate cascadeflow documentation.
Step-level scoring across cost, latency, quality, budget, compliance and energy; in-process enforcement of actions such as model switching; sub-5ms overhead; observation of model calls, tool calls and sub-agent handoffs; domain-aware cascading to specialist model cascades; Policy Builder and Domain Builder in Studio; custom policies and scoring dimensions; visual UI plus CLI.
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