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64 lines
1.8 KiB
64 lines
1.8 KiB
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from swarms import Agent
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from swarms.tools.mcp_integration import MCPServerSseParams
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from swarms.prompts.agent_prompts import FINANCE_AGENT_PROMPT, MATH_AGENT_PROMPT
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def main():
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# Configure MCP server connections
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math_server = MCPServerSseParams(
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url="http://0.0.0.0:8000/mcp",
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headers={"Content-Type": "application/json"},
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timeout=5.0,
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sse_read_timeout=30.0
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)
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stock_server = MCPServerSseParams(
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url="http://0.0.0.0:8001/mcp",
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headers={"Content-Type": "application/json"},
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timeout=5.0,
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sse_read_timeout=30.0
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)
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# Initialize math agent
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math_agent = Agent(
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agent_name="Math Agent",
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agent_description="Specialized agent for mathematical computations",
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system_prompt=MATH_AGENT_PROMPT,
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max_loops=1,
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mcp_servers=[math_server],
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streaming_on=True,
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model_name="gpt-4o-mini"
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)
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# Initialize stock agent
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stock_agent = Agent(
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agent_name="Stock Agent",
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agent_description="Specialized agent for stock analysis",
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system_prompt=FINANCE_AGENT_PROMPT,
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max_loops=1,
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mcp_servers=[stock_server],
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streaming_on=True,
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model_name="gpt-4o-mini"
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)
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print("\nMulti-Agent System Initialized")
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print("\nAvailable operations:")
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print("Math Agent: add, multiply, divide")
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print("Stock Agent: get stock price, calculate moving average")
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while True:
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query = input("\nEnter your query (or 'exit' to quit): ")
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if query.lower() == 'exit':
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break
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# Process with both agents
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math_result = math_agent.run(query)
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stock_result = stock_agent.run(query)
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print("\nMath Agent Response:", math_result)
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print("Stock Agent Response:", stock_result)
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if __name__ == "__main__":
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main()
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