Open Studio, click Create Flow, and Start/End nodes appear automatically. Before adding anything else:
Once validated, Studio keeps the configuration in sync and pipes those variables throughout the flow—no manual wiring or guesswork.
Drag the blocks you need from the palette. For a story-generation pipeline:
Studio’s detail panel keeps everything in context—model parameters, prompt editor, tool configuration, pre/post-process code, and even multi-LLM settings if you want parallel generations. Typing { inside a prompt surfaces every available state variable instantly.
Open the Code Panel to inspect the exact YAML/JSON Studio is generating. This is the same artifact written to tasks/examples/, so what you see is what gets committed.
After the run, download outputs, compare against prior executions, get metadata of latency and usage details.
SyGra Studio can also execute existing workflow in the tasks. For example, in the tasks/examples/glaive_code_assistant/ workflow — it ingests the glaiveai/glaive-code-assistant-v2 dataset, drafts answers, critiques them, and loops until the critique returns “NO MORE FEEDBACK.”
SyGra Studio turns synthetic data workflows into a visual, user friendly experience. Configure once, build with confidence, run with full observability, generate the data without ever leaving the canvas.
SyGra Studio looks like a powerful platform for synthetic data generation workflows! For teams working with complex data pipelines like this, clear system documentation is essential. net/ ) to document our ML infrastructure—you describe your system architecture in plain English and it generates diagrams that you can refine conversationally. It's been really helpful for onboarding new team members and creating design docs that actually stay current with our evolving architecture.
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