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
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
"""
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Agentic Parallel Workflow Example
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Demonstrates running multiple agents concurrently and
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combining their results with an aggregator agent.
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"""
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from praisonaiagents import Agent, Workflow
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from praisonaiagents.workflows import parallel
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# Create parallel research agents
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market_researcher = Agent(
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name="MarketResearcher",
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role="Market Research Analyst",
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goal="Research market trends and opportunities",
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instructions="Analyze market trends. Provide concise market insights."
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)
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competitor_researcher = Agent(
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name="CompetitorResearcher",
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role="Competitive Intelligence Analyst",
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goal="Research competitor strategies",
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instructions="Analyze competitors. Provide key competitive insights."
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)
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customer_researcher = Agent(
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name="CustomerResearcher",
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role="Customer Research Analyst",
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goal="Research customer needs and behaviors",
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instructions="Analyze customer segments. Provide customer insights."
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)
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# Create aggregator agent
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aggregator = Agent(
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name="Aggregator",
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role="Research Synthesizer",
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goal="Synthesize research findings",
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instructions="Combine all research findings into a comprehensive summary."
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)
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# Create workflow with parallel execution
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workflow = Workflow(
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name="Parallel Research Pipeline",
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steps=[
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parallel([market_researcher, competitor_researcher, customer_researcher]),
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aggregator
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]
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)
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if __name__ == "__main__":
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print("=== Testing Agentic Parallel Workflow ===\n")
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# Run workflow - all researchers work in parallel, then aggregator summarizes
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result = workflow.start("Research the AI industry", verbose=True)
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print(f"\nFinal Summary:\n{result['output']}")
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