Design multi-step workflows on a visual canvas. When your assistant books an appointment, sends a confirmation, or escalates to a human — that's a workflow. Drag, connect, test. No code.
Invoke via webhooks, cron schedules, or direct AI voice actions.
Drag, connect, and branch steps without writing any JSON or YAML.
Drop LLM nodes directly inside your workflows to summarize and reason.
Every workflow starts with a trigger. Generate a webhook URL and hand it to your CRM, your booking system, or any external service. Set a cron expression for recurring jobs like daily reports. Or let your voice assistant invoke the workflow as a tool — collecting input parameters from the caller before it runs.
Eleven action types on one palette. LLM calls for reasoning. Conditional branches for decision logic. Human approval for sensitive steps. Email and SMS for customer communication. API requests and scripts for custom logic. For-each loops for batch processing. Drag them in any order.
The test runner connects via WebSocket and streams execution progress in real time. Each action node shows its status — pending, running, success, or failed. Click any step to inspect its full request payload, response body, and the variables available at that point in the flow. Debug fast, ship confident.
Browse past executions. Filter by success or failure. Click into any run to see the full execution timeline — which steps passed, which failed, and how long each took. Re-run any past execution with the same inputs to verify fixes. No dark holes. No guessing.
Webhook URLs, cron schedules, or direct assistant calls. Your workflow fires the moment the right event happens.
Pull actions from the palette onto a visual editor. Arrange steps, add branches, and connect them in seconds. No YAML.
Branch on conditions. Loop through lists. Wait for human approval. Add delays between steps. Your workflow adapts.
Send emails and SMS directly from any step. Confirm appointments. Deliver receipts. Follow up after a call.
Pull from Google Calendar. Push to HubSpot. Post to Slack. Your workflow talks to the tools your team already uses.
“Summarize the clinical intake call logs and extract keywords relative to patient complaints.”
Drop an LLM node anywhere in the flow. Classify intent. Summarize conversations. Generate responses. The model works for you.
Test your workflow live with real inputs. Watch each step execute in real time via WebSocket. Inspect payload request data.
Browse past runs. Filter by success or failure. Re-run with the same inputs. Debug what went wrong — and what went right.
| Execution ID | Trigger source | Duration | Status |
|---|---|---|---|
| exec_9d1a3b | Webhook (Clinic Reschedule) | 2.4 seconds | Success |
| exec_8e4c1f | Assistant (Book Appointment) | 3.1 seconds | Success |
| exec_3f9e0b | Schedule (Daily Report) | 1.8 seconds | Failed (Timeout) |
Pick how your workflow starts. A webhook URL lets external services kick it off. A cron schedule runs it daily, hourly, or every minute. An assistant trigger lets your voice agent call it as a tool.
Drag actions from the palette onto the canvas. Start with an LLM call to understand what the customer wants. Branch with a conditional. Send an email. Post to Slack. Insert a delay.
Open the test runner. Fill in input values. Hit run. Watch each step light up in the execution timeline — green for success, red for failure. When everything looks right, publish.
Build a workflow once. It runs every time — whether triggered by a webhook, a schedule, or your AI assistant. No code. No servers. No maintenance.