The Router block uses AI to intelligently route workflows based on content analysis. Unlike Condition blocks that use simple rules, Routers understand context and intent. Each route you define creates a separate output port, allowing you to connect different paths to different downstream blocks.
Router vs Condition
Use Router when:
- AI-powered content analysis is needed
- Working with unstructured or varying content
- Intent-based routing is required (e.g., "route support tickets to departments")
Use Condition when:
- Simple rule-based decisions are sufficient
- Working with structured data or numeric comparisons
- Fast, deterministic routing is needed
Configuration Options
Context
The context that the Router will analyze to make routing decisions. This is the input data that gets evaluated against your route descriptions. It can be:
- A direct user query or input
- Output from a previous block
- A system-generated message
- Any text content that needs intelligent routing
Routes
Define the possible paths that the Router can take. Each route consists of:
- Route Title: A name for the route (e.g., "Sales", "Support", "Technical")
- Route Description: A clear description of when this route should be selected (e.g., "Route here when the query is about pricing, purchasing, or sales inquiries")
Each route you add creates a separate output port on the Router block. Connect each port to the appropriate downstream block for that route.
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: Ollama or VLLM compatible models
Use models with strong reasoning capabilities like GPT-4o or Claude 3.7 Sonnet for best results.
API Key
Your API key for the selected LLM provider. This is securely stored and used for authentication.
Outputs
<router.context>: The context that was analyzed<router.selectedRoute>: The ID of the selected route<router.selected_path>: Details of the chosen destination block<router.tokens>: Token usage statistics<router.cost>: Estimated routing cost<router.model>: Model used for decision-making
Example Use Cases
Customer Support Triage - Route tickets to specialized departments
Input (Ticket) → Router
├── [Sales Route] → Agent (Sales Team)
├── [Technical Route] → Agent (Engineering)
└── [Billing Route] → Agent (Finance)Content Classification - Classify and route user-generated content
Input (Feedback) → Router
├── [Product Feedback] → Workflow (Product Team)
└── [Bug Report] → Workflow (Technical Team)Lead Qualification - Route leads based on qualification criteria
Input (Lead) → Router
├── [Enterprise] → Agent (Enterprise Sales)
└── [Self-serve] → Workflow (Automated Onboarding)Error Handling
When the Router cannot determine an appropriate route for the given context, it will route to the error path instead of arbitrarily selecting a route. This happens when:
- The context doesn't clearly match any of the defined route descriptions
- The AI determines that none of the available routes are appropriate
Best Practices
- Write clear route descriptions: Each route description should clearly explain when that route should be selected. Be specific about the criteria.
- Make routes mutually exclusive: When possible, ensure route descriptions don't overlap to prevent ambiguous routing decisions.
- Connect an error path: Handle cases where no route matches by connecting an error handler for graceful fallback behavior.
- Use descriptive route titles: Route titles appear in the workflow canvas, so make them meaningful for readability.
- 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 route descriptions based on actual usage patterns.
- Choose appropriate models: Use models with strong reasoning capabilities for complex routing decisions.
