AI Workflow Builder
Design multi-step AI workflows with a visual flow diagram.
About the AI Workflow Builder
The AI Workflow Builder is a visual designer for multi-step LLM pipelines. Every step is a card holding a name, a prompt template with {{variable}} placeholders, a model selected from sixteen options across OpenAI, Anthropic, Google, Meta, Mistral, Cohere, and DeepSeek, and a temperature slider from 0 to 2. Steps run top to bottom, and a live flow diagram renders each as a numbered node with its model and T= badge, joined by arrows so the execution order is unmistakable. A Variables across workflow card aggregates every placeholder detected in the pipeline into a single checklist of runtime inputs. The tool is explicit that it designs rather than executes: no LLM API is ever called, and the finished artifact is a versioned JSON payload with an exportedAt timestamp that you can copy or download and feed into your own runner. Edits persist automatically in localStorage, and import/export lets the design travel between machines. For anyone planning a multi-step AI workflow — extraction, classification, generation — it turns a vague idea into a concrete, reviewable spec.
AI Workflow Builder examples
Click Add step twice. Step 1: rename to "Extract entities", prompt "Extract named entities from {{article}} and return them as JSON.", model GPT-4o mini, temperature 0.10. Step 2: rename to "Summarise", prompt "Summarise the entity list into a 3-bullet brief for {{audience}}.", keep GPT-4o and the 0.7 default.Stats: Steps 2 · Distinct models 2 · Variables 2. Each step card lists its own variables (article on step 1; audience on step 2), the Variables across workflow card shows {{article}} and {{audience}} once each, and the Flow diagram draws two numbered nodes joined by an arrow, with GPT-4o mini / T=0.10 and GPT-4o / T=0.70 badges.With those two steps in place, click Export JSON.
{
"version": 1,
"exportedAt": "<ISO timestamp>",
"steps": [
{
"id": "s-<generated>",
"name": "Extract entities",
"prompt": "Extract named entities from {{article}} and return them as JSON.",
"model": "gpt-4o-mini",
"temperature": 0.1
},
{
"id": "s-<generated>",
"name": "Summarise",
"prompt": "Summarise the entity list into a 3-bullet brief for {{audience}}.",
"model": "gpt-4o",
"temperature": 0.7
}
]
}Import a JSON file — either this exported shape or a bare array of steps.
Valid steps (a prompt string and a model string are required) replace the current design and a toast reports "Imported N step(s)". Missing ids are regenerated and a missing temperature falls back to 0.7.
How to use the AI Workflow Builder
- 1
Click Add step to create the first step card — it arrives named "Step 1" with the GPT-4o model and a 0.7 temperature.
- 2
Edit the step: rename it in the Step name field and write the prompt template in the Prompt template box, using {{variables}} for runtime inputs.
- 3
Choose a Model from the sixteen-provider dropdown and drag the Temperature slider (0–2) to match the step's purpose.
- 4
Reorder steps with the up/down arrows or remove them with the trash icon; the Flow diagram below redraws the sequence with arrows.
- 5
Check the Steps / Distinct models / Variables stats and the Variables across workflow card, then Export JSON (or Copy JSON) for your pipeline.
Common use cases
- Design a content pipeline — extract keywords, generate an outline, then draft the article, each as its own step.
- Plan a multi-model pipeline — cheap models (GPT-4o mini, Haiku) for extraction steps and expensive ones (GPT-4o, Opus) for synthesis.
- Define a data-analysis chain — classify, extract, and summarise in sequence with low temperatures for consistency.
- Sketch a support bot — intent detection, response drafting, and tone-check steps mapped visually before any code is written.
- Document a workflow for engineering — the exported JSON with version and timestamps doubles as a spec for implementation.
- Teach pipeline design — the flow diagram makes the concept of step order and variable flow concrete for learners.
Best practices
- Set temperature per step by purpose: use 0.0–0.3 for extraction and classification steps and 0.7–1.0 for generation — the slider's hint marks 0 as focused and 2 as creative.
- Keep model choice per step intentional: the designer tracks "Distinct models", so a pipeline that uses GPT-4o mini for extraction and GPT-4o for synthesis is a feature, not an accident.
- Use {{variables}} in every step template: the Variables overview card lists placeholders across the whole workflow, giving you a single checklist of runtime inputs before you wire the pipeline.
- Remember the tool designs but does not execute: no API calls are made, so prompts can reference any model in the list safely, but you must build your own runner from the exported JSON.
- Reorder with the arrows before exporting: step order in the JSON is execution order, and the flow diagram is your visual proof that the sequence reads top-to-bottom.
- Export after every design session: the payload includes a version field and exportedAt timestamp, which makes each ai-workflow.json a dated snapshot you can diff in source control.
Tips
- The flow diagram reuses the live step data, so rename a step and the diagram updates instantly — keep names short and action-oriented.
- Temperature is per step in the exported JSON, so a mixed pipeline can stay deterministic upstream and creative downstream.
- Import replaces the current design rather than merging — export first if the existing steps matter.
- Keep {{variable}} names consistent across steps: the Variables overview deduplicates, so the same placeholder appears once no matter how many steps use it.
Frequently asked questions
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