mirror of
https://github.com/MervinPraison/PraisonAI.git
synced 2026-11-07 12:47:55 -10:00
Mervin Praison
6168401a69
- Replace WorkflowManager examples with Workflow class - Emphasize Agent objects as workflow steps - Streamline pattern examples (route, parallel, loop, repeat) with agent-based usage - Remove complex WorkflowStep configuration examples - Consolidate workflow features into clearer, more concise examples - Remove workflow comparison table and memory config examples - Focus on core workflow patterns with practical agent examples
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
"""
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Agentic Evaluator-Optimizer (Repeat) Workflow Example
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Demonstrates the evaluator-optimizer pattern where an agent
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generates content and another evaluates it, repeating until approved.
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"""
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from praisonaiagents import Agent, Workflow
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from praisonaiagents.workflows import repeat
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# Create generator agent
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generator = Agent(
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name="Generator",
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role="Content Generator",
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goal="Generate high-quality content",
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instructions="Generate content based on the topic. Improve based on feedback if provided."
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)
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# Create evaluator agent
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evaluator = Agent(
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name="Evaluator",
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role="Content Evaluator",
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goal="Evaluate and provide feedback on content",
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instructions="Evaluate the content quality. If good, respond with 'APPROVED'. Otherwise provide specific feedback for improvement."
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)
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# Evaluation function to check if content is approved
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def is_approved(ctx) -> bool:
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return "approved" in ctx.previous_result.lower()
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# Create workflow with evaluator-optimizer pattern
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workflow = Workflow(
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name="Evaluator-Optimizer Pipeline",
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steps=[
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generator, # First, generate content
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repeat(
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evaluator, # Evaluate and provide feedback
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until=is_approved,
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max_iterations=3
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)
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]
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)
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if __name__ == "__main__":
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print("=== Testing Agentic Evaluator-Optimizer Workflow ===\n")
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# Run optimization workflow
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result = workflow.start(
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"Write a compelling product description for an AI assistant",
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verbose=True
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)
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print(f"\nFinal Result:\n{result['output']}")
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