LangChain Integration

pip install langchain langchain-openai

import json
import urllib.request
from typing import Any, Dict
from langchain.tools import BaseTool
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

class AlloyMCPClient:
    """Simple client to communicate with Alloy MCP Server"""
    def __init__(self, server_url: str, server_id: str, access_token: str):
        self.base_url = f"{server_url}/mcp/{server_id}/{access_token}"

    def call_tool(self, name: str, arguments: Dict[str, Any]) -> Any:
        """Execute an MCP tool and return the result"""
        payload = {
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {"name": name, "arguments": arguments},
            "id": 1
        }

        req = urllib.request.Request(
            self.base_url,
            data=json.dumps(payload).encode('utf-8'),
            headers={'Content-Type': 'application/json', 'Accept': 'application/json, text/event-stream'}
        )

        try:
            with urllib.request.urlopen(req) as response:
                # Parse the event-stream response
                for line in response.read().decode('utf-8').split('\n'):
                    if line.startswith('data: '):
                        return json.loads(line[6:]).get('result', {})
        except Exception as e:
            print(f"Error calling MCP tool: {e}")
            return {}

class MCPTool(BaseTool):
    """Wrapper to use MCP tools in LangChain"""
    def __init__(self, name: str, description: str, mcp_client: AlloyMCPClient):
        self.mcp_client = mcp_client
        super().__init__(name=name, description=description)

    def _run(self, **kwargs) -> str:
        result = self.mcp_client.call_tool(self.name, kwargs)
        return json.dumps(result, indent=2)

# Setup
mcp = AlloyMCPClient(
    server_url="https://mcp.runalloy.com",
    server_id="your-server-id",
    access_token="your-token"
)

# Define available tools
tools = [
    MCPTool("list_connectors_alloy", "List all available platform integrations", mcp),
    MCPTool("execute_action_alloy", "Execute an action on a connected platform", mcp)
]

# Create the agent
llm = ChatOpenAI(model="gpt-4", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that manages platform integrations. "
               "You can list available connectors and execute actions on them."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

agent = create_openai_functions_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Use it!
response = executor.invoke({
    "input": "What Slack actions are available?"
})
print(response["output"])