Connectto OpenRouter MCP Server
Seamlessly integrate your LangChain and AI applications with OpenRouter using Klavis AI's comprehensive MCP server connection guide.
OpenRouter
Access to multiple AI models through a unified API. Generate chat completions, compare model performance, manage usage and costs, get model recommendations, and analyze model capabilities across various providers like OpenAI, Anthropic, Meta, Google, and more
Available Tools:
- openrouter_list_models
- openrouter_search_models
- openrouter_get_model_pricing
- +11 more tools
Quick Setup Guide
Follow these steps to connect LangChain to this MCP server
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Connections
Add the MCP server to LangChain and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
LangChain + KlavisAI Integration Snippets
import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"))
openrouter_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.OPENROUTER,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mcp_client = MultiServerMCPClient({
"openrouter": {
"transport": "streamable_http",
"url": openrouter_mcp_instance.server_url
}
})
tools = asyncio.run(mcp_client.get_tools())
agent = create_react_agent(
model=llm,
tools=tools,
)
response = asyncio.run(agent.ainvoke({
"messages": [{"role": "user", "content": "Your query here"}]
}))Frequently Asked Questions
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