Chain AI Models API

Chaining AI models is rarely the hard part. Passing model A's output into model B's input, handling the async waits, retrying the flaky provider, and then wrapping the whole sequence in a REST service your app can call is where the days go. Most approaches make you own all of it: an SDK gives you the calls but leaves you the orchestration, a YAML pipeline config still needs a runtime and a server, and a model aggregator hands you one key for many models but not a single endpoint that runs a multi-step chain.

Wireflow removes the wrapper. You wire the chain as a graph on a canvas, so the connection between two models is a line, not a function you write. When you publish, the graph becomes one REST endpoint and one MCP tool with typed inputs. That is the same pipeline your team can run by hand on the canvas and that an external service or agent can call over HTTP, which is the property behind a headless AI workflow platform: one call in, a finished asset out.

Open the flow linked here and you are looking at a two-model chain with nothing hidden behind an SDK.

• Product Prompt holds the input. A Text Input node with one line describing the asset you want. This is the typed input the endpoint accepts.
• Prompt Expander does the language work. A Run any LLM node on Claude Sonnet 4.6, system-prompted to expand that one line into a detailed image prompt covering subject, composition, lighting, palette, and finish. Its text output is wired straight into the next node.
• Image Generator renders the asset. Nano Banana Lite takes the expanded prompt and returns a PNG in sub-2s latency across 14 aspect ratios. Its output is the endpoint's response.

That is two models chained: an LLM call feeds an image call, and publishing the graph exposes the pair as one endpoint. To make it a longer media pipeline you add nodes, not code: wire the image into a Crystal Upscaler for a sharper master, or into a video model to animate it, or add an ElevenLabs step for a voiceover. Each new node extends the same endpoint. The trade to be honest about: this is media orchestration, so if your chain is pure text tool-calling with no generation step, a code-first framework may suit you better than a canvas. That is the same reasoning behind building multi-model AI workflows on a canvas instead of a hand-written pipeline: the graph is the source of truth, and improving one node improves every call.

Wireflow chains AI generation models, so it fits text-to-image, image-to-video, upscale, and voice pipelines. It is not a classical machine-learning platform: if you are chaining LSTM models, regression steps, or your own trained checkpoints, this is the wrong tool. It is also not the reasoning brain. The Run any LLM node runs a hosted model inside the chain, but the product logic, the strategy, and the decisions about what to build still come from your code or your agent.

Execution is hosted and metered: building the chain on the canvas is free, but every run of the endpoint costs credits, so an agent hammering the URL unattended is a spend decision you should cap deliberately. And there is no offline mode, no custom Python nodes, and no bringing your own GPUs; you trade that control for never running a server. If your requirement is a pure-code chain with no visual layer, compare the field first: the roundup of the best AI APIs for developers shows where a publish-a-canvas model wins and where a framework does.