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claude[bot]andMervin Praison
9f7993ac40
- simple_input.py: Basic user input example - medium_complexity_input.py: Multiple inputs with tools and context - advanced_dynamic_input.py: Class-based system with conditional logic - environment_variable_input.py: Combining env vars with user input - streamlit_ui_input.py: Web-based UI for dynamic input These examples demonstrate various approaches to handling dynamic user input in PraisonAI agents. Closes #66 Co-authored-by: Mervin Praison <[email protected]>
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""
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Medium Complexity Input Example for PraisonAI
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This example shows how to use multiple user inputs to create a more sophisticated
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agent system with tools, multiple agents, and context passing between tasks.
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"""
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from praisonaiagents import Agent, Task, PraisonAIAgents
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from praisonaiagents.tools import duckduckgo
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# Get multiple inputs
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topic = input("Enter search topic: ")
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num_results = input("How many results? (default: 5): ") or "5"
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# Create search agent with tools
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search_agent = Agent(
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name="WebSearcher",
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role="Search Specialist",
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goal=f"Find {num_results} relevant results about {topic}",
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backstory="Expert in internet research and data collection",
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tools=[duckduckgo],
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self_reflect=False
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)
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# Create analysis agent
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analysis_agent = Agent(
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name="Analyzer",
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role="Data Analyst",
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goal="Analyze and summarize search results",
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backstory="Expert in data synthesis and reporting"
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)
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# Create tasks with context passing
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search_task = Task(
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description=f"Search for '{topic}' and find top {num_results} results",
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expected_output=f"List of {num_results} relevant results with sources",
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agent=search_agent,
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name="search_task"
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)
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analyze_task = Task(
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description="Analyze the search results and create a summary report",
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expected_output="Comprehensive summary with key insights",
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agent=analysis_agent,
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context=[search_task], # Receives results from search_task
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name="analysis_task"
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)
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# Run sequential process
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agents = PraisonAIAgents(
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agents=[search_agent, analysis_agent],
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tasks=[search_task, analyze_task],
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process="sequential"
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)
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agents.start() |