Flows Ai

Flows AI occupies a narrow, deliberate slot: it is the thin orchestration layer over the Vercel AI SDK, not a platform around it. The design is easy to hold in your head. A flow is { agent, input, name }, input can be a string, a nested flow, or an array of flows, and an agent is simply an async function called with ({ input, context }). That means the learning curve is closer to a utility than a framework — the docs' first example is two agents in a sequence, and the custom agents guide shows a repeatAgent that calls Promise.all over nested flow results in about three lines. The built-in control flow agents cover the patterns most teams actually need: sequence chains steps so each output feeds the next input, parallel runs tasks concurrently, for each iterates over inputs, evaluator grades LLM output, best of all picks the strongest result, and one of handles branching. Because the agent helper takes the same props as the Vercel AI SDK's generateText, wiring a model is a few lines and model choice stays with you — the documentation shows the openai provider with gpt-4o, and nothing binds you to a single vendor. The absence of a model of its own is the point, not a gap. Where it fits: teams that want sequencing, parallelism, and evaluation expressed in code with full type inference instead of a YAML file or a drag-and-drop canvas, and developers who would rather own their runtime than hand it to a vendor. Where it does not: non-developers, anyone expecting a visual workflow builder, and teams that need hosted run history, dashboards, scheduling, or observability such as caching, logging, and tracing out of the box — the provided documentation does not mention those. It is also not a place to look for a large catalog of pre-built third-party integrations. Treat it as a library dependency you reason about like any other, and budget engineering time for the operational layer it deliberately leaves to you.