How do I use multiple MCP servers (Mem0, ServiceNow) with Together AI?

Together AI supports connecting to multiple MCP servers simultaneously. You can configure each Mem0, ServiceNow server independently and use their tools together in your AI applications. This enables powerful multi-source AI systems where your models can access data from Mem0, ServiceNow and combine insights for comprehensive responses. Klavis AI simplifies multi-server management with centralized configuration and unified connection handling for Together AI applications.

What are the benefits of using Mem0 and ServiceNow together with Together AI?

Combining Mem0 and ServiceNow with your Together AI applications creates powerful synergies for advanced AI systems. Each service brings unique data sources and capabilities that complement each other, enabling comprehensive information retrieval, cross-platform data correlation, and enhanced AI responses with multi-source validation using Together AI's high-performance inference.

Do I need special packages for Together AI MCP server integration?

Yes, you'll need the Together AI SDK and any service-specific packages for the MCP servers you want to connect to. Klavis AI provides detailed package requirements and automated installation scripts for each supported MCP server, along with Together AI-specific configuration examples and troubleshooting guides to ensure smooth setup.

Can I use function calling with Together AI and MCP servers?

Absolutely! MCP servers are designed to work seamlessly with Together AI's function calling capabilities. You can use MCP server tools with any Together AI model that supports function calling, allowing your AI models to perform actions and retrieve data from external services as part of their reasoning and response generation process.

How do I handle authentication for MCP servers with Together AI?

Authentication for MCP servers with Together AI is handled through environment variables and configuration files. Each MCP server may require different authentication methods (API keys, OAuth tokens, etc.). Klavis AI handles the authentication flow automatically once you provide the necessary credentials, and provides secure credential management, encrypted storage, and pre-configured authentication templates for all supported services.

Which Together AI models are best for these MCP servers?

For these MCP servers, we recommend using Together AI models with strong function calling capabilities such as Llama 2 70B, Mixtral 8x7B, or Code Llama for coding tasks. These models provide excellent performance for tool use and can effectively leverage the capabilities of external MCP servers. Klavis AI provides model-specific optimization guides and performance benchmarks to help you choose the best Together AI model for your use case.

What types of AI applications benefit most from Together AI + MCP server integration?

AI applications that benefit most include: chatbots and virtual assistants needing real-time data access, automated workflow systems that interact with multiple platforms, content generation tools that need to pull data from various sources, and AI agents that perform complex multi-step tasks. Together AI's high-performance inference combined with MCP servers extends your applications beyond static responses to dynamic, real-time AI interactions.

How does MCP server integration affect Together AI performance?

MCP server integration adds minimal latency when properly configured, thanks to Together AI's optimized inference and efficient function calling. The impact is typically negligible compared to the enhanced capabilities gained. Best practices include caching frequently accessed data, using appropriate timeouts, and implementing fallback strategies for when external services are unavailable. Klavis AI provides performance monitoring tools and optimization recommendations to ensure your Together AI + MCP server applications run at peak efficiency.

Can I use streaming responses with Together AI and MCP servers?

Yes, Together AI's streaming capabilities work well with MCP server integrations. You can stream AI responses while making function calls to MCP servers, providing a smooth user experience. The streaming continues after function calls are completed, allowing for real-time updates and interactive AI experiences. Klavis AI provides examples and best practices for implementing streaming with MCP server function calls.

Why choose Klavis AI for Together AI MCP server connections?

Klavis AI provides the most comprehensive platform for Together AI MCP server integration with features like pre-built Together AI connectors, automated setup scripts, detailed documentation with Together AI-specific examples, secure credential management, and performance optimization guides. Our platform is specifically designed to make Together AI + MCP server integration as seamless as possible for developers building advanced AI applications. With Klavis AI's centralized management dashboard, you can monitor all your MCP server connections, track performance metrics, analyze function call usage, and troubleshoot issues from a single interface.

Connectto Mem0, ServiceNow MCP Servers

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Mem0

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Mem0 is an intelligent memory layer for AI applications that provides long-term memory storage and retrieval. Store code snippets, implementation details, and programming knowledge for seamless context retention across conversations

Available Tools:

mem0_add_memory
mem0_get_all_memories
mem0_search_memories
+2 more tools

ServiceNow

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ServiceNow is a cloud-based software platform that helps companies manage and automate digital workflows for their enterprise operations, particularly in IT, human resources, and customer service.

Quick Setup Guide

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Create Your Account

Configure Connections

Add your desired MCP servers to Together AI and configure authentication settings.

Test & Deploy

Verify your connections work correctly and start using your enhanced AI capabilities.

Together AI + KlavisAI Integration Snippets

View Documentation

import os
import json
from together import Together
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

# Initialize clients
together_client = Together(api_key=os.getenv("TOGETHER_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

mem0_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.MEM0,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

servicenow_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.SERVICENOW,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Get all MCP tools
mem0_tools = klavis_client.mcp_server.list_tools(
    server_url=mem0_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)
servicenow_tools = klavis_client.mcp_server.list_tools(
    server_url=servicenow_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)

# Combine all tools
all_tools = []
all_tools.extend(mem0_tools.tools)
all_tools.extend(servicenow_tools.tools)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant with access to multiple data sources."},
    {"role": "user", "content": user_message}
]

response = together_client.chat.completions.create(
    model="meta-llama/Llama-2-70b-chat-hf",
    messages=messages,
    tools=all_tools
)

Frequently Asked Questions

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ConnectTogether AIto Mem0, ServiceNow MCP Servers