Connect to OpenRouter to access multiple AI models, compare model performance, and manage usage across different AI providers through AI agents.
API
UI
Open Source
1
Install the SDKs (optional)
pip install klavis
2
Create a Strata MCP Server with OpenRouter
from klavis import Klavisfrom klavis.types import McpServerNameklavis_client = Klavis(api_key="YOUR_API_KEY")# Create a Strata MCP server with OpenRouterresponse = klavis_client.mcp_server.create_strata_server( servers=[McpServerName.OPENROUTER], user_id="user123")
userId specifies whose connected accounts and data you are accessing in Klavis. It should be a unique id for yourself, your team, or your organization.
# Set the OpenRouter API key for your instanceresponse = klavis_client.mcp_server.set_instance_auth( instance_id=openrouter_server.instance_id, auth_data={ "api_key": "YOUR_OPENROUTER_API_KEY" })
🎉 Your OpenRouter MCP Server is ready! You can now use your MCP server URL with any MCP-compatible client to access multiple AI models.
# Pull the Docker imagedocker pull ghcr.io/klavis-ai/openrouter-mcp-server:latest# Run with OpenRouter API keydocker run -p 5000:5000 \ -e AUTH_DATA='{"api_key":"your_openrouter_api_key"}' \ ghcr.io/klavis-ai/openrouter-mcp-server:latest
With our progressive discovery approach, Klavis System is capable of enabling all tools for OpenRouter. Please use the get_tools API for more details. If you find any tool that is missing, please reach out to contact@klavis.ai.
