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LLM Integration Guide

mcp_use supports integration with any Language Learning Model (LLM) that is compatible with LangChain. This guide covers how to use different LLM providers with mcp_use and emphasizes the flexibility to use any LangChain-supported model.
Key Requirement: Your chosen LLM must support tool calling (also known as function calling) to work with MCP tools. Most modern LLMs support this feature.

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Universal LLM Support

mcp_use leverages LangChain’s architecture to support any LLM that implements the LangChain interface. This means you can use virtually any model from any provider, including:

OpenAI

GPT-4, GPT-4o, GPT-3.5 Turbo

Anthropic

Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku

Google

Gemini Pro, Gemini Flash, PaLM

Open Source

Llama, Mistral, CodeLlama via various providers

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OpenAI

from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

# Initialize OpenAI model
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.7,
    api_key="your-api-key"  # Or set OPENAI_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)

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Anthropic Claude

from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient

# Initialize Claude model
llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.7,
    api_key="your-api-key"  # Or set ANTHROPIC_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)

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Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from mcp_use import MCPAgent, MCPClient

# Initialize Gemini model
llm = ChatGoogleGenerativeAI(
    model="gemini-pro",
    temperature=0.7,
    google_api_key="your-api-key"  # Or set GOOGLE_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)

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Groq (Fast Inference)

from langchain_groq import ChatGroq
from mcp_use import MCPAgent, MCPClient

# Initialize Groq model
llm = ChatGroq(
    model="llama-3.1-70b-versatile",
    temperature=0.7,
    api_key="your-api-key"  # Or set GROQ_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)

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Local Models with Ollama

from langchain_ollama import ChatOllama
from mcp_use import MCPAgent, MCPClient

# Initialize local Ollama model
llm = ChatOllama(
    model="llama3.1:8b",
    base_url="http://localhost:11434",  # Default Ollama URL
    temperature=0.7
)

# Create agent
agent = MCPAgent(llm=llm, client=client)

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Model Requirements

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Tool Calling Support

For MCP tools to work properly, your chosen model must support tool calling. Most modern LLMs support this: ✅ Supported Models:
  • OpenAI: GPT-4, GPT-4o, GPT-3.5 Turbo
  • Anthropic: Claude 3+ series
  • Google: Gemini Pro, Gemini Flash
  • Groq: Llama 3.1, Mixtral models
  • Most recent open-source models
❌ Not Supported:
  • Basic completion models without tool calling
  • Very old model versions
  • Models without function calling capabilities

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Checking Tool Support

You can verify if a model supports tools: 
# Check if the model supports tool calling
if hasattr(llm, 'bind_tools') or hasattr(llm, 'with_tools'):
    print("✅ Model supports tool calling")
else:
    print("❌ Model may not support tool calling")

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Model Configuration Tips

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Temperature Settings

Different tasks benefit from different temperature settings: 
# For precise, deterministic tasks
llm = ChatOpenAI(model="gpt-4o", temperature=0.1)

# For creative tasks
llm = ChatOpenAI(model="gpt-4o", temperature=0.8)

# Balanced approach (recommended)
llm = ChatOpenAI(model="gpt-4o", temperature=0.7)

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Model-Specific Parameters

Each provider has unique parameters you can configure: 
# OpenAI with custom parameters
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.7,
    max_tokens=4000,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.1
)

# Anthropic with custom parameters
llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.7,
    max_tokens=4000,
    top_p=0.9
)

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Cost Optimization

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Choosing Cost-Effective Models

Consider your use case when selecting models:
Use CaseRecommended ModelsReason
Development/TestingGPT-3.5 Turbo, Claude HaikuLower cost, good performance
Production/ComplexGPT-4o, Claude SonnetBest performance
High VolumeGroq modelsFast inference, competitive pricing
Privacy/LocalOllama modelsNo API costs, data stays local

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Token Management

# Set reasonable token limits
llm = ChatOpenAI(
    model="gpt-4o",
    max_tokens=2000,  # Limit response length
    temperature=0.7
)

# Monitor usage in your application
agent = MCPAgent(
    llm=llm,
    client=client,
    max_steps=10  # Limit agent steps to control costs
)

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Environment Setup

Always use environment variables for API keys: 
# .env file
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AI...
GROQ_API_KEY=gsk_...

from dotenv import load_dotenv
load_dotenv()  # Load environment variables

# Now LangChain will automatically use the keys
llm = ChatOpenAI(model="gpt-4o")  # No need to pass api_key

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Advanced Integration

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Custom Model Wrappers

You can create custom wrappers for specialized models: 
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage

class CustomModelWrapper(BaseChatModel):
    """Custom wrapper for your model"""

    def _generate(self, messages, stop=None, **kwargs):
        # Your custom model implementation
        pass

    def _llm_type(self):
        return "custom_model"

# Use with mcp_use
llm = CustomModelWrapper()
agent = MCPAgent(llm=llm, client=client)

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Model Switching

Switch between models dynamically: 
def get_model_for_task(task_type: str):
    if task_type == "complex_reasoning":
        return ChatOpenAI(model="gpt-4o", temperature=0.1)
    elif task_type == "creative":
        return ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.8)
    else:
        return ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Use different models for different tasks
llm = get_model_for_task("complex_reasoning")
agent = MCPAgent(llm=llm, client=client)

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Troubleshooting

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Common Issues

  1. “Model doesn’t support tools”: Ensure your model supports function calling
  2. API key errors: Check environment variables and API key validity
  3. Rate limiting: Implement retry logic or use different models
  4. Token limits: Adjust max_tokens or use models with larger context windows

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Debug Model Behavior

# Enable verbose logging to see model interactions
agent = MCPAgent(
    llm=llm,
    client=client,
    verbose=True  # Shows detailed model interactions
)
For more LLM providers and detailed integration examples, visit the LangChain Chat Models documentation.