This guide explains how to save flows from the marketplace and customize them according to your specific requirements. 🛠️✨

​
Saving a Flow

When you find a useful flow in the Marketplace, you can save it locally for customization:

Python
from mira_sdk import MiraClient

# Initialize client
client = MiraClient(config={"API_KEY": "YOUR_API_KEY"})

# Get the flow you want to customize
flow = client.flow.get("author/flow_name")

# Save to local YAML file
flow.save("/path/to/flow.yaml")

​
Customizing Your Flow

After saving the flow locally, you can modify the YAML file according to your needs. Here are the key areas you can customize:

​
Modifying Metadata

Update the flow’s basic information to reflect your ownership and purpose:

.yaml
metadata:
  name: "your-customized-flow-name"          # Rename flow 
  description: "Your modified description"   # Flow description
  author: "your-username"                    # Your Mira Flows username
  tags: [your, custom, tags]                 # Discovery keywords
  private: true                              # Access control setting (true/false)

​
Adjusting Inputs

Modify input parameters to match your requirements:

.yaml
inputs:
  custom_input:
    type: string                                  # Input type
    description: "Your custom input description"  # Input description
    required: true                                # Required field
    example: "Your example value"                 # Example value

​
Updating the Prompt

Customize the prompt to achieve your desired output:

.yaml
prompt: |
  Your customized instructions...
  You can use {custom_input} as a placeholder

​
Modifying Model Settings

Change the model configuration if needed:

.yaml
model:
  provider: "provider-name"                      # AI service provider
  name: "your-preferred-model"                   # Specific model identifier

​
Testing Your Customized Flow

Before deployment, test your modifications to ensure everything works correctly:

Python
from mira_sdk import Flow

# Load your modified flow
modified_flow = Flow(source="/path/to/flow.yaml")

# Test with sample inputs
test_input = {"custom_input": "test value"}
response = client.flow.test(modified_flow, test_input)

​
Deploying Your Custom Flow

Once you’re satisfied with the modifications:

Python
# Deploy as your own flow
client.flow.deploy(modified_flow)

For more detailed information about customization options and best practices, please refer to our complete documentation 📖🔗.

This guide explains how to save flows from the marketplace and customize them according to your specific requirements. 🛠️✨

​
Saving a Flow

When you find a useful flow in the Marketplace, you can save it locally for customization:

Python
from mira_sdk import MiraClient

# Initialize client
client = MiraClient(config={"API_KEY": "YOUR_API_KEY"})

# Get the flow you want to customize
flow = client.flow.get("author/flow_name")

# Save to local YAML file
flow.save("/path/to/flow.yaml")

​
Customizing Your Flow

After saving the flow locally, you can modify the YAML file according to your needs. Here are the key areas you can customize:

​
Modifying Metadata

Update the flow’s basic information to reflect your ownership and purpose:

.yaml
metadata:
  name: "your-customized-flow-name"          # Rename flow 
  description: "Your modified description"   # Flow description
  author: "your-username"                    # Your Mira Flows username
  tags: [your, custom, tags]                 # Discovery keywords
  private: true                              # Access control setting (true/false)

​
Adjusting Inputs

Modify input parameters to match your requirements:

.yaml
inputs:
  custom_input:
    type: string                                  # Input type
    description: "Your custom input description"  # Input description
    required: true                                # Required field
    example: "Your example value"                 # Example value

​
Updating the Prompt

Customize the prompt to achieve your desired output:

.yaml
prompt: |
  Your customized instructions...
  You can use {custom_input} as a placeholder

​
Modifying Model Settings

Change the model configuration if needed:

.yaml
model:
  provider: "provider-name"                      # AI service provider
  name: "your-preferred-model"                   # Specific model identifier

​
Testing Your Customized Flow

Before deployment, test your modifications to ensure everything works correctly:

Python
from mira_sdk import Flow

# Load your modified flow
modified_flow = Flow(source="/path/to/flow.yaml")

# Test with sample inputs
test_input = {"custom_input": "test value"}
response = client.flow.test(modified_flow, test_input)

​
Deploying Your Custom Flow

Once you’re satisfied with the modifications:

Python
# Deploy as your own flow
client.flow.deploy(modified_flow)

For more detailed information about customization options and best practices, please refer to our complete documentation 📖🔗.