AI Orchestration API

Running AI work across several models usually means writing integration code for each one and hand-rolling the data hand-off between steps. An orchestration API removes that middle layer: you describe the whole pipeline once, and one call runs it end to end. On Wireflow the pipeline is a graph you can see, not a hidden prompt chain.

The flow behind this page is a short chain: a Nano Banana Lite image node feeds a Seedance 2.0 video node, so a still becomes motion, and a Compose Video step assembles the result. You build it on a drag-and-drop canvas, publish it, and send one authenticated POST to its execute endpoint. The response is a 201 with an executionId, and you poll a separate endpoint until the run reaches COMPLETED or FAILED. The same pattern powers an image to video pipeline or a content generation API.

There is no separate SDK to wire up. When you publish a graph, that graph is the API. One authenticated POST to its execute endpoint starts the run and returns an executionId, and you poll the execution endpoint until the run reads COMPLETED or FAILED. The response carries each node's status, output URLs, timing, and credit cost, so you can see exactly which step produced what. Send an Idempotency-Key header on retries and an identical key replays the original result instead of running the pipeline twice.

The same published flow is also a hosted MCP tool, so an agent can list it and run it with typed inputs, and a webhook trigger lets a form or CI job start a run without holding a connection open. The models themselves are nodes: unplug Nano Banana Lite and drop in another image model, or swap the video node, and the endpoint stays the same. See the Wireflow MCP connector or build the graph in the workflow builder.

Wireflow is the generation and routing layer, not the reasoning brain. It runs the pipeline you designed and returns the results; it does not decide which models your app needs, write the prompts, or refine on its own between runs. The graph does exactly what its wires say, which is the point of an orchestration API but also its limit. There are no offline or local runs, no custom Python nodes, and no local model checkpoints; every execution happens on hosted compute.

It earns its place when a multi-model pipeline is worth publishing once and calling many times: chain an image model into a video model, add an upscaler, and hand the whole thing to an app or an agent as one endpoint. Building and wiring the graph is free, and each execution spends credits, so a high-volume batch is a real budgeting decision. If you only ever need a single model call, a direct provider API is simpler than orchestration.