Connectto OpenRouter MCP Server

Seamlessly integrate your CrewAI multi-agent systems with OpenRouter using Klavis AI's comprehensive MCP server connection guide.

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OpenRouter

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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 CrewAI to this MCP server

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Agents & Tools

Set up your CrewAI agents with the MCP server tools and configure authentication settings for collaborative workflows.

3

Deploy Your Crew

Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.

CrewAI + KlavisAI Integration Snippets

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_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,
)

with MCPServerAdapter(openrouter_mcp_instance.server_params) as mcp_tools:
    # Create a OpenRouter Analysis Agent
    openrouter_agent = Agent(
        role="OpenRouter Analyst",
        goal="Research and analyze openrouter to extract comprehensive insights",
        backstory="You are an expert at analyzing openrouter and creating professional summaries.",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    analysis_task = Task(
        description=f"Research and analyze openrouter data. Extract relevant information and create a comprehensive summary with key points and main takeaways.",
        expected_output="Complete analysis with structured summary, key insights, and main takeaways",
        agent=openrouter_agent,
        markdown=True
    )
    
    # Create and execute the crew
    openrouter_crew = Crew(
        agents=[openrouter_agent],
        tasks=[analysis_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = openrouter_crew.kickoff()

Frequently Asked Questions

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