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68 lines
1.8 KiB
68 lines
1.8 KiB
import asyncio
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from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms.utils.visualizer import (
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SwarmVisualizationRich,
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SwarmMetadata,
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) # Replace with your actual module name
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# Create two example agents
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agent1 = Agent(
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agent_name="Financial-Analysis-Agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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output_type="string",
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streaming_on=False,
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)
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# Create a second dummy agent for demonstration
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agent2 = Agent(
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agent_name="Stock-Advisor-Agent",
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system_prompt="Provide stock market insights and investment advice.",
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model_name="gpt-4o-mini",
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="stock_agent.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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output_type="string",
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streaming_on=False,
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)
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# Create swarm metadata
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metadata = SwarmMetadata(
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name="Financial Swarm",
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description="A swarm of agents focused on financial analysis and stock market advice.",
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version="1.0",
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author="Your Name",
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primary_objective="Provide comprehensive financial and investment analysis.",
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)
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# Instantiate the visualizer with a list of agents
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visualizer = SwarmVisualizationRich(
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swarm_metadata=metadata,
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agents=[agent1, agent2],
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update_resources=True,
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refresh_rate=0.1,
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)
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# Start the visualization
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asyncio.run(visualizer.start())
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