About Fl stdio
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Key features of the Fl stdio that are enabled through these drivers include:
- FL FLowstone (formerly FL SynthMaker) - FL Studio
- FL Mobile 3 FL Beta - FL Studio
- Fl ssiggaspreiseDr. Fuchs Fl ssiggas Fl ssiggastanks
- FL - Adey FL - SupplyHouse.com
- fl-studio-crack fl-studio-download fl-studio-cracked fl-studio
- GitHub - SulfurHal/Fl-Studio-21: fl-studio-crack fl-studio-download fl
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LangChain MCP AdaptersThis library provides a lightweight wrapper that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph.Features🛠️ Convert MCP tools into LangChain tools that can be used with LangGraph agents📦 A client implementation that allows you to connect to multiple MCP servers and load tools from themInstallationpip install langchain-mcp-adaptersQuickstartHere is a simple example of using the MCP tools with a LangGraph agent.">pip install langchain-mcp-adapters langgraph langchain-openaiexport OPENAI_API_KEY=your_api_key>ServerFirst, let's create an MCP server that can add and multiply numbers. int: """Add two numbers""" return a + b@mcp.tool()def multiply(a: int, b: int) -> int: """Multiply two numbers""" return a * bif __name__ == "__main__": mcp.run(transport="stdio")"># math_server.pyfrom mcp.server.fastmcp import FastMCPmcp = FastMCP("Math")@mcp.tool()def add(a: int, b: int) -> int: """Add two numbers""" return a + b@mcp.tool()def multiply(a: int, b: int) -> int: """Multiply two numbers""" return a * bif __name__ == "__main__": mcp.run(transport="stdio")Client# Create server parameters for stdio connectionfrom mcp import ClientSession, StdioServerParametersfrom mcp.client.stdio import stdio_clientfrom langchain_mcp_adapters.tools import load_mcp_toolsfrom langgraph.prebuilt import create_react_agentfrom langchain_openai import ChatOpenAImodel = ChatOpenAI(model="gpt-4o")server_params = StdioServerParameters( command="python", # Make sure to update to the full absolute path to your math_server.py file args=["/path/to/math_server.py"],)async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: # Initialize the connection await session.initialize() # Get tools tools = await load_mcp_tools(session) # Create and run the agent agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})Multiple MCP ServersThe library also allows you to connect to multiple MCP servers and load tools from them:Server str: """Get weather for location.""" return "It's always sunny in New York"if __name__ == "__main__": mcp.run(transport="sse")"># math_server.py...# weather_server.pyfrom typing import Listfrom mcp.server.fastmcp import FastMCPmcp = FastMCP("Weather")@mcp.tool()async def get_weather(location: str) -> str: """Get weather for location.""" return "It's always sunny in New York"if __name__ == "__main__": mcp.run(transport="sse")Clientfrom langchain_mcp_adapters.client import MultiServerMCPClientfrom langgraph.prebuilt import create_react_agentfrom langchain_openai import ChatOpenAImodel = ChatOpenAI(model="gpt-4o")async with MultiServerMCPClient( { "math": { "command": "python", # Make sure to update to the full absolute path to your math_server.py file "args": ["/path/to/math_server.py"], "transport": "stdio", }, "weather": { # make sure you start your weather server on port 8000 "url": " "transport": "sse", } }) as client:
