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Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Workflow Routing

Route workflows to different paths based on the output of a decision step. This pattern is also known as Agentic Routing or Conditional Branching.

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Quick Start

from praisonaiagents import AgentFlow, WorkflowContext, StepResult
from praisonaiagents import route

# Decision maker - determines which route to take
def classify_request(ctx: WorkflowContext) -> StepResult:
    input_lower = ctx.input.lower()
    if "urgent" in input_lower:
        return StepResult(output="priority: high")
    elif "question" in input_lower:
        return StepResult(output="priority: support")
    return StepResult(output="priority: normal")

# Route handlers
def handle_high(ctx: WorkflowContext) -> StepResult:
    return StepResult(output="🚨 Escalating to senior team!")

def handle_support(ctx: WorkflowContext) -> StepResult:
    return StepResult(output="💬 Routing to help desk")

def handle_normal(ctx: WorkflowContext) -> StepResult:
    return StepResult(output="📋 Added to queue")

# Create workflow with routing
workflow = AgentFlow(steps=[
    classify_request,
    route({
        "high": [handle_high],
        "support": [handle_support],
        "normal": [handle_normal],
        "default": [handle_normal]
    })
])

result = workflow.start("This is URGENT!", )
print(result["output"])  # "🚨 Escalating to senior team!"

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API Reference

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route()

route(
    routes: Dict[str, List],      # Key: pattern to match, Value: steps to execute
    default: Optional[List] = None # Fallback steps if no match
) -> Route

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Parameters

ParameterTypeDescription
routesDict[str, List]Dictionary mapping patterns to step lists
defaultOptional[List]Steps to execute if no pattern matches

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How Matching Works

The router checks if any route key (case-insensitive) is contained in the previous step’s output: 
# If previous output is "priority: high"
route({
    "high": [...],    # ✅ Matches - "high" is in "priority: high"
    "low": [...],     # ❌ No match
})

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Examples

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Multi-Step Routes

Each route can contain multiple steps: 
workflow = AgentFlow(steps=[
    classifier,
    route({
        "approve": [
            validate_approval,
            send_notification,
            update_database
        ],
        "reject": [
            log_rejection,
            notify_requester
        ]
    })
])

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Chained Routing

Routes can be chained for complex decision trees: 
workflow = AgentFlow(steps=[
    initial_classifier,
    route({
        "category_a": [
            sub_classifier_a,
            route({
                "sub_a1": [handler_a1],
                "sub_a2": [handler_a2]
            })
        ],
        "category_b": [handler_b]
    })
])

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With Agents

Routes can include Agent instances: 
from praisonaiagents import Agent

researcher = Agent(name="Researcher", role="Research topics")
writer = Agent(name="Writer", role="Write content")

workflow = AgentFlow(steps=[
    topic_classifier,
    route({
        "technical": [researcher, writer],
        "creative": [writer]
    })
])

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Use Cases

Use CaseDescription
Request TriageRoute support tickets to appropriate teams
Content ModerationRoute content based on classification
Approval WorkflowsDifferent paths for approved/rejected items
A/B TestingRoute to different processing pipelines
Error HandlingRoute based on success/failure status

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Best Practices

  1. Always include a default - Handle unexpected outputs gracefully
  2. Use clear patterns - Make route keys unambiguous
  3. Keep routes focused - Each route should have a clear purpose
  4. Log decisions - Track which routes are taken for debugging

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See Also