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121 lines
4.4 KiB
121 lines
4.4 KiB
import asyncio
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from mcp import ClientSession
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from mcp.client.streamable_http import (
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streamablehttp_client as http_client,
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)
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async def create_agent_via_mcp(session, agent_name, system_prompt, model_name, task):
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"""Create and use an agent through MCP using streamable HTTP."""
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print(f"🔧 Creating agent '{agent_name}' with task: {task}")
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try:
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arguments = {
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"agent_name": agent_name,
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"system_prompt": system_prompt,
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"model_name": model_name,
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"task": task
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}
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result = await session.call_tool(
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name="create_agent",
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arguments=arguments
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)
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# Result Handling
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output = None
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if hasattr(result, 'content') and result.content:
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if isinstance(result.content, list):
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for content_item in result.content:
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if hasattr(content_item, 'text'):
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print(content_item.text)
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output = content_item.text
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else:
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print(content_item)
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output = content_item
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else:
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print(result.content)
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output = result.content
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else:
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print("No content returned from agent")
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return output
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except Exception as e:
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print(f"Tool call failed: {e}")
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import traceback
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traceback.print_exc()
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raise
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async def main():
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print("🔧 Starting MCP client connection...")
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try:
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async with http_client("http://localhost:8000/mcp") as (read, write, _):
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async with ClientSession(read, write) as session:
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try:
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await session.initialize()
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print("Session initialized successfully!")
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except Exception as e:
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print(f"Session initialization failed: {e}")
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raise
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# List available tools
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print("Listing available tools...")
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try:
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tools = await session.list_tools()
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print(f"📋 Available tools: {[tool.name for tool in tools.tools]}")
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except Exception as e:
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print(f"Failed to list tools: {e}")
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raise
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# Sequential Multi-Agent System
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# Agent 1: Tech Expert explains blockchain
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agent1_name = "tech_expert"
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agent1_prompt = "You are a technology expert. Provide clear explanations."
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agent1_model = "gpt-4"
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agent1_task = "Explain blockchain technology in simple terms"
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agent1_output = await create_agent_via_mcp(
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session,
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agent1_name,
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agent1_prompt,
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agent1_model,
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agent1_task
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)
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# Agent 2: Legal Expert analyzes the explanation from Agent 1
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agent2_name = "legal_expert"
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agent2_prompt = "You are a legal expert. Analyze the following explanation for legal implications."
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agent2_model = "gpt-4"
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agent2_task = f"Analyze the following explanation for legal implications:\n\n{agent1_output}"
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agent2_output = await create_agent_via_mcp(
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session,
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agent2_name,
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agent2_prompt,
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agent2_model,
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agent2_task
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)
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# Agent 3: Educator simplifies the legal analysis for students
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agent3_name = "educator"
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agent3_prompt = "You are an educator. Summarize the following legal analysis in simple terms for students."
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agent3_model = "gpt-4"
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agent3_task = f"Summarize the following legal analysis in simple terms for students:\n\n{agent2_output}"
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agent3_output = await create_agent_via_mcp(
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session,
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agent3_name,
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agent3_prompt,
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agent3_model,
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agent3_task
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)
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print("\n=== Final Output from Educator Agent ===")
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print(agent3_output)
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except Exception as e:
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print(f"Connection failed: {e}")
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import traceback
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traceback.print_exc()
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raise
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# Run the client
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if __name__ == "__main__":
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asyncio.run(main()) |