A workflow where the output of one LLM call becomes the input for the next. This sequential design allows for structured reasoning and step-by-step task completion.

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Quick Start

1

Install Package

First, install the Praison Labs 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 import Agent, Task, Praison LabsAgents
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 agents for each step in the chain
agent1 = Agent(
    name="Time Checker",
    role="Time checker",
    goal="Check if the time is even or odd",
    instructions="Check if the time is even or odd",
    tools=[get_time_check]
)

agent2 = Agent(
    name="Advanced Analyzer",
    role="Advanced data analyzer",
    goal="Perform in-depth analysis of processed data",
    instructions="Analyze the processed data in detail"
)

agent3 = Agent(
    name="Final Processor",
    role="Final data processor",
    goal="Generate final output based on analysis",
    instructions="Create final output based on analyzed data"
)

# Create tasks for each step
initial_task = Task(
    name="time_check",
    description="Getting time check and checking if it is even or odd",
    expected_output="Getting time check and checking if it is even or odd",
    agent=agent1,
    is_start=True,  # Mark as the starting task
    task_type="decision",  # This task will make a decision
    next_tasks=["advanced_analysis"],  # Next task if condition passes
    condition={
        "even": ["advanced_analysis"],  # If passes, go to advanced analysis
        "odd": ""  # If fails, exit the chain
    }
)

analysis_task = Task(
    name="advanced_analysis",
    description="Perform advanced analysis on the processed data",
    expected_output="Analyzed data ready for final processing",
    agent=agent2,
    next_tasks=["final_processing"]
)

final_task = Task(
    name="final_processing",
    description="Generate final output",
    expected_output="Final processed result",
    agent=agent3
)

# Create the workflow manager
workflow = Praison LabsAgents(
    agents=[agent1, agent2, agent3],
    tasks=[initial_task, analysis_task, final_task],
    process="workflow",  # Use workflow process type
    verbose=True
)

# Run the workflow
results = workflow.start()

# Print results
print("\nWorkflow Results:")
for task_id, result in results["task_results"].items():
    if result:
        print(f"Task {task_id}: {result.raw}")
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 Prompt Chaining

What is Prompt Chaining?

Prompt chaining enables:

  • Sequential execution of prompts
  • Data flow between agents
  • Conditional branching in workflows
  • Step-by-step processing of complex tasks

​
Features

Sequential Processing

Execute tasks in a defined sequence with data passing between steps.

Decision Points

Implement conditional logic to control workflow progression.

Data Flow

Pass data seamlessly between agents in the chain.

Process Control

Monitor and control the execution of each step in the chain.

​
Configuration Options to exit the chain 

# Task with chaining configuration
task = Task(
    name="time_check",
    description="Check time and make decision",
    expected_output="Time check result",
    agent=agent,
    is_start=True,
    task_type="decision",
    next_tasks=["next_step"],
    condition={
        "even": ["next_step"],
        "odd": ""
    }
)

task = Task(
    name="time_check",
    description="Check time and make decision",
    expected_output="Time check result",
    agent=agent,
    is_start=True,
    task_type="decision",
    next_tasks=["next_step"],
    condition={
        "even": ["next_step"],
        "odd": "exit"
    }
)

task = Task(
    name="time_check",
    description="Check time and make decision",
    expected_output="Time check result",
    agent=agent,
    is_start=True,
    task_type="decision",
    next_tasks=["next_step"],
    condition={
        "even": ["next_step"],
        "odd": ["exit"]
    }
)

task = Task(
    name="time_check",
    description="Check time and make decision",
    expected_output="Time check result",
    agent=agent,
    is_start=True,
    task_type="decision",
    next_tasks=["next_step"],
    condition={
        "even": ["next_step"],
        "odd": [""]
    }
)

​
Troubleshooting

Chain Issues

If chain execution fails:

  • Verify task connections
  • Check condition logic
  • Enable verbose mode for debugging

Data Flow

If data flow is incorrect:

  • Review task outputs
  • Check agent configurations
  • Verify task dependencies

​
Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

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For optimal results, ensure your chain is properly configured with clear task dependencies and conditions for branching logic.