curl -X POST "https://api.danubeai.com/v1/workflows/wf_abc123/execute" \
-H "danube-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
}
}'
from danube import DanubeClient
with DanubeClient(api_key="YOUR_API_KEY") as client:
execution = client.workflows.execute(
"wf_abc123",
inputs={"city": "San Francisco", "channel": "#weather-alerts"}
)
print(f"Status: {execution.status}")
for step in execution.step_results:
print(f" Step {step.step_number}: {step.status}")
{
"id": "exec_xyz789",
"workflow_id": "wf_abc123",
"user_id": "user_456",
"status": "success",
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
},
"step_results": [
{
"step_number": 1,
"tool_id": "tool_weather_001",
"tool_name": "Weather - Get Forecast",
"status": "success",
"result": {
"forecast": "Partly cloudy, 18C, 10% chance of rain"
},
"error": null,
"execution_time_ms": 340
},
{
"step_number": 2,
"tool_id": "tool_slack_001",
"tool_name": "Slack - Send Message",
"status": "success",
"result": {
"message_ts": "1708100000.000100"
},
"error": null,
"execution_time_ms": 210
}
],
"error": null,
"execution_time_ms": 550,
"started_at": "2026-02-16T12:00:00Z",
"completed_at": "2026-02-16T12:00:01Z",
"created_at": "2026-02-16T12:00:00Z"
}
Workflows
Execute Workflow
Run a workflow with the provided inputs
POST
/
v1
/
workflows
/
{workflow_id}
/
execute
curl -X POST "https://api.danubeai.com/v1/workflows/wf_abc123/execute" \
-H "danube-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
}
}'
from danube import DanubeClient
with DanubeClient(api_key="YOUR_API_KEY") as client:
execution = client.workflows.execute(
"wf_abc123",
inputs={"city": "San Francisco", "channel": "#weather-alerts"}
)
print(f"Status: {execution.status}")
for step in execution.step_results:
print(f" Step {step.step_number}: {step.status}")
{
"id": "exec_xyz789",
"workflow_id": "wf_abc123",
"user_id": "user_456",
"status": "success",
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
},
"step_results": [
{
"step_number": 1,
"tool_id": "tool_weather_001",
"tool_name": "Weather - Get Forecast",
"status": "success",
"result": {
"forecast": "Partly cloudy, 18C, 10% chance of rain"
},
"error": null,
"execution_time_ms": 340
},
{
"step_number": 2,
"tool_id": "tool_slack_001",
"tool_name": "Slack - Send Message",
"status": "success",
"result": {
"message_ts": "1708100000.000100"
},
"error": null,
"execution_time_ms": 210
}
],
"error": null,
"execution_time_ms": 550,
"started_at": "2026-02-16T12:00:00Z",
"completed_at": "2026-02-16T12:00:01Z",
"created_at": "2026-02-16T12:00:00Z"
}
Overview
Executes a workflow by running each step sequentially. Each step calls a Danube tool and template variables are resolved with results from previous steps. Returns the full execution result including all step outcomes. Auth: Accepts either JWT or API key (danube-api-key header). Public workflows can be executed by any authenticated user; private workflows require ownership.
Path Parameters
string
required
The workflow UUID to execute
Body Parameters
object
default:{}
Input values referenced by
{{inputs.field}} templates in the workflow stepsstring
Your own idempotency key for this run. Sending the same value again returns the existing execution instead of starting another.
Response
string
Execution ID (use to poll status or retrieve results later)
string
The workflow that was executed
string
Execution status:
pending, running, success, or failedobject
The inputs that were provided
array
string
Overall error message if the workflow failed
integer
Total execution time in milliseconds
string
ISO 8601 timestamp when execution began
string
ISO 8601 timestamp when execution finished
string
For
execution_mode: "instructions", the hosted model’s final answer; null for template workflowsstring
manual, or schedule for a scheduled runinteger
On the free plan, hosted-AI workflow runs left after this one; null when the cap does not apply
Example
curl -X POST "https://api.danubeai.com/v1/workflows/wf_abc123/execute" \
-H "danube-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
}
}'
from danube import DanubeClient
with DanubeClient(api_key="YOUR_API_KEY") as client:
execution = client.workflows.execute(
"wf_abc123",
inputs={"city": "San Francisco", "channel": "#weather-alerts"}
)
print(f"Status: {execution.status}")
for step in execution.step_results:
print(f" Step {step.step_number}: {step.status}")
{
"id": "exec_xyz789",
"workflow_id": "wf_abc123",
"user_id": "user_456",
"status": "success",
"inputs": {
"city": "San Francisco",
"channel": "#weather-alerts"
},
"step_results": [
{
"step_number": 1,
"tool_id": "tool_weather_001",
"tool_name": "Weather - Get Forecast",
"status": "success",
"result": {
"forecast": "Partly cloudy, 18C, 10% chance of rain"
},
"error": null,
"execution_time_ms": 340
},
{
"step_number": 2,
"tool_id": "tool_slack_001",
"tool_name": "Slack - Send Message",
"status": "success",
"result": {
"message_ts": "1708100000.000100"
},
"error": null,
"execution_time_ms": 210
}
],
"error": null,
"execution_time_ms": 550,
"started_at": "2026-02-16T12:00:00Z",
"completed_at": "2026-02-16T12:00:01Z",
"created_at": "2026-02-16T12:00:00Z"
}
MCP Tool
This endpoint is also available as theexecute_workflow MCP tool:
result = await mcp.call_tool("execute_workflow", {
"workflow_id": "wf_abc123",
"inputs": {"city": "San Francisco"}
})