LLM integration

MCPAgent runs through LangChain chat models. You can either pass a "provider/model" string and let the agent create the model, or pass an existing LangChain chat model instance. The model must support tool calling. Structured output and streaming are optional, but they are required for the corresponding MCPAgent features to work well.

Use simplified mode

Simplified mode supports openai, anthropic, google, and groq provider strings. Install the matching LangChain package and set the provider API key in the environment.

Provider string	Package	API key
`openai/model`	`@langchain/openai`	`OPENAI_API_KEY`
`anthropic/model`	`@langchain/anthropic`	`ANTHROPIC_API_KEY`
`google/model`	`@langchain/google-genai`	`GOOGLE_API_KEY` or `GOOGLE_GENERATIVE_AI_API_KEY`
`groq/model`	`@langchain/groq`	`GROQ_API_KEY`

import { MCPAgent } from "@mcp-use/agent";

const agent = new MCPAgent({
  llm: "openai/gpt-4o",
  llmConfig: {
    temperature: 0.2,
    maxTokens: 1000,
  },
  mcpServers: {
    filesystem: {
      command: "npx",
      args: ["-y", "@modelcontextprotocol/server-filesystem", "./"],
    },
  },
});

const result = await agent.run({
  prompt: "Summarize the files in this directory.",
});

console.log(result);
await agent.close();

Pass llmConfig.apiKey when you cannot use environment variables.

const agent = new MCPAgent({
  llm: "anthropic/claude-sonnet-4-6",
  llmConfig: {
    apiKey: process.env.ANTHROPIC_API_KEY,
    temperature: 0,
  },
  mcpServers,
});

Use explicit mode

Explicit mode accepts any LangChain-compatible chat model. Use it for custom model setup, provider-specific options, callbacks, or models not supported by simplified mode.

OpenAI

import { ChatOpenAI } from "@langchain/openai";
import { MCPAgent } from "@mcp-use/agent";
import { MCPClient } from "@mcp-use/client";

const client = new MCPClient({ mcpServers });
const llm = new ChatOpenAI({
  model: "gpt-4o",
  temperature: 0.7,
  apiKey: process.env.OPENAI_API_KEY,
});

const agent = new MCPAgent({ llm, client });

Anthropic

import { ChatAnthropic } from "@langchain/anthropic";
import { MCPAgent } from "@mcp-use/agent";
import { MCPClient } from "@mcp-use/client";

const client = new MCPClient({ mcpServers });
const llm = new ChatAnthropic({
  model: "claude-sonnet-4-6",
  temperature: 0.7,
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const agent = new MCPAgent({ llm, client });

Google Gemini

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import { MCPAgent } from "@mcp-use/agent";
import { MCPClient } from "@mcp-use/client";

const client = new MCPClient({ mcpServers });
const llm = new ChatGoogleGenerativeAI({
  model: "gemini-pro",
  temperature: 0.7,
  apiKey: process.env.GOOGLE_API_KEY,
});

const agent = new MCPAgent({ llm, client });

Groq

import { ChatGroq } from "@langchain/groq";
import { MCPAgent } from "@mcp-use/agent";
import { MCPClient } from "@mcp-use/client";

const client = new MCPClient({ mcpServers });
const llm = new ChatGroq({
  model: "llama-3.1-70b-versatile",
  temperature: 0.7,
  apiKey: process.env.GROQ_API_KEY,
});

const agent = new MCPAgent({ llm, client });

Verify provider support

Before wiring a model into a production workflow, run one prompt that requires a tool call. If the model cannot call tools, the agent will not be able to use MCP tools even if the client connection succeeds. For more providers, use a LangChain chat model that supports tool calling and pass it through explicit mode. See the LangChain JavaScript chat model integrations.

Next steps

MCPAgent API Reference

Get Started

MCP Server

MCP Apps

MCP Client

MCP Agent

Tooling

LLM integration

Use simplified mode

Use explicit mode

OpenAI

Anthropic

Google Gemini

Groq

Verify provider support

Next steps

​Use simplified mode

​Use explicit mode

​OpenAI

​Anthropic

​Google Gemini

​Groq

​Verify provider support

​Next steps

Use simplified mode

Use explicit mode

OpenAI

Anthropic

Google Gemini

Groq

Verify provider support

Next steps