Visual Workflow AI
Blocks

Router

Route workflow execution based on specific conditions or logic

The Router block uses AI to intelligently decide which path your workflow should take next. Unlike Condition blocks that use simple rules, Router blocks can understand context and make smart routing decisions based on content analysis.

Overview

The Router block enables you to:

Intelligent content routing: Use AI to understand intent and context

Dynamic path selection: Route workflows based on unstructured content analysis

Context-aware decisions: Make smart routing choices beyond simple rules

Multi-path management: Handle complex workflows with multiple potential destinations

Router vs Condition Blocks

How It Works

The Router block:

Analyze content: Uses an LLM to understand input content and context

Evaluate targets: Compares content against available destination blocks

Select destination: Identifies the most appropriate path based on intent

Route execution: Directs workflow to the selected block

Configuration Options

Content/Prompt

The content or prompt that the Router will analyze to make routing decisions. This can be:

  • A direct user query or input
  • Output from a previous block
  • A system-generated message

Target Blocks

The possible destination blocks that the Router can select from. The Router will automatically detect connected blocks, but you can also:

  • Customize the descriptions of target blocks to improve routing accuracy
  • Specify routing criteria for each target block
  • Exclude certain blocks from being considered as routing targets

Model Selection

Choose an AI model to power the routing decision:

OpenAI: GPT-4o, o1, o3, o4-mini, gpt-4.1
Anthropic: Claude 3.7 Sonnet
Google: Gemini 2.5 Pro, Gemini 2.0 Flash
Other Providers: Groq, Cerebras, xAI, DeepSeek
Local Models: Any model running on Ollama

Recommendation: Use models with strong reasoning capabilities like GPT-4o or Claude 3.7 Sonnet for more accurate routing decisions.

API Key

Your API key for the selected LLM provider. This is securely stored and used for authentication.

Accessing Results

After a router makes a decision, you can access its outputs:

  • <router.content>: Summary of the routing decision made
  • <router.selected_path>: Details of the chosen destination block
  • <router.tokens>: Token usage statistics from the LLM
  • <router.model>: The model used for decision-making

Advanced Features

Custom Routing Criteria

Define specific criteria for each target block:

// Example routing descriptions
Target Block 1: "Technical support issues, API problems, integration questions"
Target Block 2: "Billing inquiries, subscription changes, payment issues"
Target Block 3: "General questions, feedback, feature requests"

Multi-Model Routing

Use different models for different routing scenarios:

// Fast routing for simple cases
Model: GPT-4o-mini
Criteria: Simple, common routing patterns

// Complex routing for nuanced decisions
Model: Claude 3.7 Sonnet
Criteria: Complex content analysis required

Fallback Handling

Implement robust fallback mechanisms:

// Router configuration
Primary Targets: ["Support", "Sales", "Technical"]
Fallback Target: "General" // Default when no specific match
Confidence Threshold: 0.7 // Minimum confidence for routing

Inputs and Outputs

  • Content/Prompt: Text to analyze for routing decisions

  • Target Blocks: Connected blocks as potential destinations

  • Model: AI model for routing analysis

  • API Key: Authentication for selected LLM provider

  • router.content: Summary of routing decision

  • router.selected_path: Details of chosen destination

  • router.tokens: Token usage statistics

  • router.model: Model used for decision-making

  • Routing Decision: Primary path selection result

  • Decision Context: Analysis summary and reasoning

  • Access: Available in blocks after the router

Example Use Cases

Customer Support Triage

Scenario: Route support tickets to specialized departments

  1. User submits support request via form
  2. Router analyzes ticket content and context
  3. Technical issues → Engineering support agent
  4. Billing questions → Finance support agent

Content Classification

Scenario: Classify and route user-generated content

  1. User submits content or feedback
  2. Router analyzes content type and sentiment
  3. Feature requests → Product team workflow
  4. Bug reports → Technical support workflow

Lead Qualification

Scenario: Route leads based on qualification criteria

  1. Lead information captured from form
  2. Router analyzes company size, industry, and needs
  3. Enterprise leads → Sales team with custom pricing
  4. SMB leads → Self-service onboarding flow

Best Practices

  • Provide clear target descriptions: Help the Router understand when to select each destination with specific, detailed descriptions
  • Use specific routing criteria: Define clear conditions and examples for each path to improve accuracy
  • Implement fallback paths: Connect a default destination for when no specific path is appropriate
  • Test with diverse inputs: Ensure the Router handles various input types, edge cases, and unexpected content
  • Monitor routing performance: Review routing decisions regularly and refine criteria based on actual usage patterns
  • Choose appropriate models: Use models with strong reasoning capabilities for complex routing decisions