Execute Workflow

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="dk_...") 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"
}

POST

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="dk_...") 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

workflow_id

string

required

The workflow UUID to execute

Body Parameters

inputs

object

Input values referenced by {{inputs.field}} templates in the workflow steps

Response

string

Execution ID (use to poll status or retrieve results later)

workflow_id

string

The workflow that was executed

status

string

Execution status: pending, running, success, or failed

inputs

object

The inputs that were provided

step_results

array

Results from each step

Show Step result object

step_number

integer

Step position

tool_id

string

Tool that was executed

tool_name

string

Tool name

status

string

success or failed

result

any

Tool output (varies by tool)

error

string

Error message if step failed

execution_time_ms

integer

Step execution time in milliseconds

error

string

Overall error message if the workflow failed

execution_time_ms

integer

Total execution time in milliseconds

started_at

string

ISO 8601 timestamp when execution began

completed_at

string

ISO 8601 timestamp when execution finished

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="dk_...") 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 the execute_workflow MCP tool:

result = await mcp.call_tool("execute_workflow", {
    "workflow_id": "wf_abc123",
    "inputs": {"city": "San Francisco"}
})

Delete Workflow Get Workflow Execution

​Overview

​Path Parameters

​Body Parameters

​Response

​Example

​MCP Tool

Overview

Path Parameters

Body Parameters

Response

Example

MCP Tool