Deploying Spurs as APIs

One-Click Deployment

PySpur makes it incredibly easy to deploy your workflows as production-ready APIs with a single click.

Open your workflow

Navigate to any workflow you’ve created and want to deploy.

Click Deploy

Click the “Deploy” button in the top navigation bar to open the deployment modal.

Choose deployment options

In the modal that appears, you can configure:

API call type: Choose between blocking (synchronous) or non-blocking (asynchronous) calls
Programming language: Select your preferred language for the code example

For example, in Python:

Or in TypeScript:

Copy the code

Copy the generated code example to integrate with your application.

API Call Types

PySpur supports two types of API calls when deploying your workflows:

Blocking (Synchronous)

Use blocking calls when:

You need immediate results
The workflow completes quickly
You want to process the response in the same request

The API will wait for the workflow to complete before returning a response.

# Endpoint structure
POST /api/wf/{workflow_id}/run/?run_type=blocking

Non-Blocking (Asynchronous)

Use non-blocking calls when:

Workflows may take longer to complete
You want to decouple request and response
You need better scalability for long-running tasks

The API will immediately return a run ID, and you can check the status later.

# Start endpoint
POST /api/wf/{workflow_id}/start_run/?run_type=non_blocking

# Status check endpoint
GET /api/runs/{run_id}/status/

Code Examples

The deployment modal provides ready-to-use code examples in various programming languages:

import requests

# For blocking calls
url = 'https://your-pyspur-instance.com/api/wf/{workflow_id}/run/?run_type=blocking'
data = {
    "initial_inputs": {
        "InputNode_1": {
            "input_field_1": "example_value",
            "input_field_2": 123
        }
    }
}

response = requests.post(url, json=data)
print(response.status_code)
print(response.json())

For non-blocking calls:

# Step 1: Start the workflow
url = 'https://your-pyspur-instance.com/api/wf/{workflow_id}/start_run/?run_type=non_blocking'
response = requests.post(url, json=data)
run_id = response.json()['id']

# Step 2: Check status later
status_url = f'https://your-pyspur-instance.com/api/runs/{run_id}/status/'
status_response = requests.get(status_url)
print(status_response.json())

import requests

# For blocking calls
url = 'https://your-pyspur-instance.com/api/wf/{workflow_id}/run/?run_type=blocking'
data = {
    "initial_inputs": {
        "InputNode_1": {
            "input_field_1": "example_value",
            "input_field_2": 123
        }
    }
}

response = requests.post(url, json=data)
print(response.status_code)
print(response.json())

For non-blocking calls:

# Step 1: Start the workflow
url = 'https://your-pyspur-instance.com/api/wf/{workflow_id}/start_run/?run_type=non_blocking'
response = requests.post(url, json=data)
run_id = response.json()['id']

# Step 2: Check status later
status_url = f'https://your-pyspur-instance.com/api/runs/{run_id}/status/'
status_response = requests.get(status_url)
print(status_response.json())

// For blocking calls
fetch('https://your-pyspur-instance.com/api/wf/{workflow_id}/run/?run_type=blocking', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    initial_inputs: {
      InputNode_1: {
        input_field_1: "example_value",
        input_field_2: 123
      }
    }
  })
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error('Error:', error));

# For blocking calls
curl -X POST 'https://your-pyspur-instance.com/api/wf/{workflow_id}/run/?run_type=blocking' \
  -H "Content-Type: application/json" \
  -d '{
    "initial_inputs": {
      "InputNode_1": {
        "input_field_1": "example_value",
        "input_field_2": 123
      }
    }
  }'

Advanced Deployment Options

Batch Processing

Run your workflow over a dataset with the batch processing API

POST /api/wf/{workflow_id}/start_batch_run/

Provide a dataset ID and mini-batch size to process large datasets efficiently.

Cancellation

Cancel in-progress workflows when needed

POST /api/cancel_workflow/{run_id}/

This is useful for stopping long-running or paused workflows.

Run Control

PySpur provides full control over your deployed workflows with APIs for:

Listing all runs of a workflow
Retrieving run status
Handling human-in-the-loop interventions

Security Considerations

When deploying workflows as APIs, consider:

API Authentication: Add appropriate authentication to your PySpur instance
Input Validation: Ensure workflows validate inputs properly
Error Handling: Implement robust error handling in your client code

Next Steps

Add Authentication

Learn how to secure your deployed APIs

Monitor Workflows

Track usage and performance of your deployed Spurs

Advanced Configuration

Explore additional deployment configuration options

Get Started

Chatbots

Tools

RAG

Evals

API Reference

Deploying Spurs as APIs

One-Click Deployment

API Call Types

Code Examples

Advanced Deployment Options

Batch Processing

Cancellation

Run Control

Security Considerations

Next Steps

Add Authentication

Monitor Workflows

Advanced Configuration

Get Started

Chatbots

Tools

RAG

Evals

API Reference

​One-Click Deployment

​API Call Types

​Code Examples

​Advanced Deployment Options

Batch Processing

Cancellation

Run Control

​Security Considerations

​Next Steps

Add Authentication

Monitor Workflows

Advanced Configuration

One-Click Deployment

API Call Types

Code Examples

Advanced Deployment Options

Security Considerations

Next Steps