A low-latency workflow where inputs are dynamically routed to the most appropriate LLM instance or configuration, optimizing efficiency and specialization.

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Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents.agent import Agent
from praisonaiagents.task import Task
from praisonaiagents.agents import PraisonAIAgents
import time

def get_time_check():
    current_time = int(time.time())
    result = "even" if current_time % 2 == 0 else "odd"
    print(f"Time check: {current_time} is {result}")
    return result

# Create specialized agents
router = Agent(
    name="Router",
    role="Input Router",
    goal="Evaluate input and determine routing path",
    instructions="Analyze input and decide whether to proceed or exit",
    tools=[get_time_check]
)

processor1 = Agent(
    name="Processor 1",
    role="Secondary Processor",
    goal="Process valid inputs that passed initial check",
    instructions="Process data that passed the routing check"
)

processor2 = Agent(
    name="Processor 2",
    role="Final Processor",
    goal="Perform final processing on validated data",
    instructions="Generate final output for processed data"
)

# Create tasks with routing logic
routing_task = Task(
    name="initial_routing",
    description="check the time and return according to what is returned",
    expected_output="pass or fail based on what is returned",
    agent=router,
    is_start=True,
    task_type="decision",
    condition={
        "pass": ["process_valid"],
        "fail": ["process_invalid"]
    }
)

processing_task = Task(
    name="process_valid",
    description="Process validated input",
    expected_output="Processed data ready for final step",
    agent=processor1,
)

final_task = Task(
    name="process_invalid",
    description="Generate final output",
    expected_output="Final processed result",
    agent=processor2
)

# Create and run workflow
workflow = PraisonAIAgents(
    agents=[router, processor1, processor2],
    tasks=[routing_task, processing_task, final_task],
    process="workflow",
    verbose=True
)

print("\nStarting Routing Workflow...")
print("=" * 50)

results = workflow.start()

print("\nWorkflow Results:")
print("=" * 50)
for task_id, result in results["task_results"].items():
    if result:
        task_name = result.description
        print(f"\nTask: {task_name}")
        print(f"Result: {result.raw}")
        print("-" * 50)
4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
  • Basic understanding of Python

​
Understanding Agentic Routing

What is Agentic Routing?

Agentic routing enables:

  • Dynamic decision-making in workflows
  • Conditional task execution paths
  • Automated process branching
  • Intelligent workflow management

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Features

Dynamic Routing

Route tasks based on real-time decisions and conditions.

Conditional Logic

Implement complex branching logic in workflows.

Task Management

Handle task dependencies and execution order.

Process Control

Control workflow execution with detailed monitoring.

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Configuration Options

# Create a router agent
router = Agent(
    name="Router",
    role="Input Router",
    goal="Evaluate input and determine routing path",
    instructions="Analyze input and decide whether to proceed or exit",
    tools=[get_time_check],  # Custom tools for routing decisions
    verbose=True  # Enable detailed logging
)

# Task with routing configuration
routing_task = Task(
    name="initial_routing",
    description="Route based on conditions",
    expected_output="Routing decision",
    agent=router,
    is_start=True,
    task_type="decision",
    condition={
        "pass": ["next_task"],
        "fail": ["alternate_task"]
    }
)

​
Troubleshooting

Routing Issues

If routing doesn’t work as expected:

  • Verify condition mappings
  • Check task dependencies
  • Enable verbose mode for debugging

Workflow Flow

If workflow is unclear:

  • Review task connections
  • Verify agent configurations
  • Check routing conditions

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Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

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For optimal results, ensure your routing conditions are well-defined and your task dependencies are properly configured.

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