Configuration System - Rilo Documentation

Rilo’s configuration system allows you to customize workflow behavior through three main mechanisms: config fields, runtime inputs, and instructions.

Overview

Each workflow block can be configured with:

Config Fields: Static values that remain constant across executions
Runtime Inputs: Dynamic values provided by users at each execution
Instructions: Natural language descriptions of block logic

Config fields are stored permanently in the database and used for every workflow execution. Runtime inputs are provided fresh each time a workflow runs.

Config Fields

Config fields are static configuration values that are set once and used for all workflow executions.

When to Use Config Fields

Use config fields for values that:

✅ Don’t change between executions
✅ Are set during workflow creation
✅ Apply to all workflow runs

Examples:

Email addresses for notifications
API endpoints or service URLs
Threshold values (e.g., “minimum 50 upvotes”)
File references (templates, reference documents)
Tool-specific settings

Config Field Types

Rilo supports multiple field types:

Type	Description	Example
`string`	Text values	Email addresses, IDs, names
`number`	Numeric values	Thresholds, counts, limits
`boolean`	True/false flags	Enable/disable options
`list`	Arrays of values	Subreddits, recipients, URLs
`json`	Complex nested objects	Complex configurations
`datetime`	Date/time values	Schedules, deadlines
`file`	File references	Templates, reference documents

Example: Config Fields

{
  "config_fields": [
    {
      "name": "notification_email",
      "value": "[email protected]",
      "field_type": "string",
      "description": "Email address for notifications"
    },
    {
      "name": "min_upvotes",
      "value": 50,
      "field_type": "number",
      "description": "Minimum upvotes threshold"
    },
    {
      "name": "subreddits",
      "value": ["r/python", "r/programming"],
      "field_type": "list",
      "description": "Subreddits to monitor"
    }
  ]
}

Config fields are stored as JSON in the database. Complex objects should use the json field type.

Runtime Inputs

Runtime inputs are dynamic values that users provide each time a workflow executes.

When to Use Runtime Inputs

Use runtime inputs for values that:

✅ Change between executions
✅ Are provided by users at runtime
✅ Vary per workflow run

Examples:

Search queries
File uploads
Date ranges
User selections

Runtime Input Configuration

Runtime inputs are configured in Start blocks:

{
  "block_type": "start",
  "config_fields": [
    {
      "name": "search_query",
      "value": null,
      "field_type": "string",
      "description": "Search query to use for this execution"
    },
    {
      "name": "input_file",
      "value": null,
      "field_type": "file",
      "description": "File to process in this execution"
    }
  ]
}

Runtime inputs with value: null indicate that the user must provide a value at each execution. If a value is provided, it becomes the default.

Instructions

Instructions are natural language descriptions of what a block should do.

Purpose of Instructions

Instructions serve multiple purposes:

Documentation: Explain what the block does
Code Generation: Guide the AI agent in generating code
Verification: Help verify block configuration

Writing Good Instructions

Be Specific

Instead of “Process data”, write “Filter posts with 50+ upvotes from the last 24 hours”.

Include Context

Mention relevant details: “Send email to [email protected] with daily summary”.

State What, Not How

Focus on the goal, not implementation: “Generate 5 quotes” not “Call LLM API with prompt”.

Keep It Concise

Instructions should be 2-3 lines max. Use config fields for detailed settings.

Example: Instructions

Start Block:

Daily at 9 AM. User provides a search query for each run.

Code Block:

- Scrape Reddit posts matching the search query
- Filter posts with 50+ upvotes
- Return top 10 posts

HITL Block:

Review scraped posts. User can approve or reject each post before sending email.

Configuration Best Practices

Config Fields vs Runtime Inputs

Use Config Fields For
Use Runtime Inputs For

Email addresses
API endpoints
Thresholds and limits
File templates
Tool settings
Default values

File Configuration

Files can be used in both config fields and runtime inputs: Config Field (Permanent):

{
  "name": "template_file",
  "value": {
    "type": "file",
    "filename": "template.xlsx",
    "s3_location": {...}
  },
  "field_type": "file",
  "description": "Template file for document generation"
}

Runtime Input (Per Execution):

{
  "name": "input_data",
  "value": null,
  "field_type": "file",
  "description": "Data file to process"
}

Files uploaded as config fields are stored permanently. Files uploaded as runtime inputs are provided fresh each execution.

Configuration Workflow

1. Create Workflow

Describe your workflow in natural language:

"Scrape Reddit posts daily and email summary to [email protected]"

2. AI Agent Creates Blocks

The agent automatically:

Creates blocks with appropriate types
Sets up config fields based on your description
Generates instructions for each block

3. Verify Configuration

Review and adjust:

Config fields: Add or modify static values
Runtime inputs: Configure what users provide
Instructions: Refine block descriptions

4. Execute

Config fields are used automatically
Runtime inputs prompt users for values
Instructions guide code generation

Advanced Configuration

JSON Config Fields

For complex configurations, use the json field type:

{
  "name": "filter_config",
  "value": {
    "min_upvotes": 50,
    "max_age_days": 7,
    "subreddits": ["r/python", "r/programming"],
    "keywords": ["tutorial", "guide"]
  },
  "field_type": "json",
  "description": "Complex filtering configuration"
}

Conditional Configuration

Some blocks support conditional logic based on config fields:

# In block code
min_upvotes = config.get("min_upvotes", 10)
if min_upvotes > 50:
    # Use stricter filtering
    ...

Troubleshooting

Config field not being used

Verify the field name matches what the code expects
Check that the field type is correct
Ensure the value is set (not null for required fields)

Runtime input not prompting

Verify the Start block has the config field
Check that value is null (not a default value)
Ensure the workflow is in “verified” state

Instructions unclear

Be more specific about what the block should do
Include relevant context and constraints
Reference config fields by name when relevant

Code Generation - How config fields are used in generated code
Graph Mode - Visual configuration in the graph editor
Human-in-the-Loop - Configuring review blocks

Configuration is a key part of workflow creation. Take time to set up config fields and runtime inputs correctly for the best workflow experience.

​Overview

​Config Fields

​When to Use Config Fields

​Config Field Types

​Example: Config Fields

​Runtime Inputs

​When to Use Runtime Inputs

​Runtime Input Configuration

​Instructions

​Purpose of Instructions

​Writing Good Instructions

​Example: Instructions

​Configuration Best Practices

​Config Fields vs Runtime Inputs

​File Configuration

​Configuration Workflow

​1. Create Workflow

​2. AI Agent Creates Blocks

​3. Verify Configuration

​4. Execute

​Advanced Configuration

​JSON Config Fields

​Conditional Configuration

​Troubleshooting

​Related Features

Overview

Config Fields

When to Use Config Fields

Config Field Types

Example: Config Fields

Runtime Inputs

When to Use Runtime Inputs

Runtime Input Configuration

Instructions

Purpose of Instructions

Writing Good Instructions

Example: Instructions

Configuration Best Practices

Config Fields vs Runtime Inputs

File Configuration

Configuration Workflow

1. Create Workflow

2. AI Agent Creates Blocks

3. Verify Configuration

4. Execute

Advanced Configuration

JSON Config Fields

Conditional Configuration

Troubleshooting

Related Features