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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 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 import Agent, Workflow

# Create agents for each step in the chain
researcher = Agent(
    name="Researcher",
    role="Research Analyst",
    goal="Research and gather information",
    instructions="Research the given topic thoroughly. Provide factual information."
)

analyst = Agent(
    name="Analyst",
    role="Data Analyst",
    goal="Analyze research findings",
    instructions="Analyze the research provided. Extract key insights and patterns."
)

writer = Agent(
    name="Writer",
    role="Content Writer",
    goal="Write clear content",
    instructions="Based on the analysis, write a clear and engaging summary."
)

editor = Agent(
    name="Editor",
    role="Content Editor",
    goal="Polish and refine content",
    instructions="Edit and polish the content. Ensure clarity and quality."
)

# Create sequential workflow (prompt chaining)
# Each agent's output becomes the next agent's input
workflow = Workflow(
    steps=[researcher, analyst, writer, editor],
    on_step_complete=lambda name, r: print(f"✅ {name} completed")
)

# Run the workflow
result = workflow.start("What are the benefits of renewable energy?")
print(f"\nFinal Result: {result['output'][:500]}...")
4

Start Workflow

Type this in your terminal to run your workflow:
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

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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

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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.

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Configuration Options

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Early Exit from Chain

Use stop_workflow=True to exit the chain early: 
def validate_step(ctx: WorkflowContext) -> StepResult:
    if not is_valid(ctx.input):
        return StepResult(output="Invalid", stop_workflow=True)  # Exit chain
    return StepResult(output="Valid")

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Conditional Branching

Use route() for conditional paths: 
from praisonaiagents.workflows import route

workflow = Workflow(
    steps=[
        classifier_step,
        route({
            "success": [process_step, finalize_step],
            "failure": [error_handler],
            "default": [fallback_step]
        })
    ]
)

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Conditional Step Execution

Use should_run to conditionally skip steps: 
from praisonaiagents import WorkflowStep

def should_process(ctx: WorkflowContext) -> bool:
    return ctx.variables.get("validated", False)

workflow = Workflow(steps=[
    validate_step,
    WorkflowStep(
        name="process",
        handler=process_step,
        should_run=should_process  # Only runs if validated
    )
])

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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

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Next Steps

AutoAgents

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Mini Agents

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