About Langchain MCP Integration
Langchain MCP Integration is an MCP server published by lambdaworks in the Developer Tools category: langChain integration with MCP tools.
The repository has 1 stars and 0 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/lambdaworks/langchain-mcp-integrationThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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- ✓README presentThe repository ships documentation.
- ✓License declaredUsable under a stated license.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- !No dynamic code executionSource scan found eval or shell execution. Review how tool inputs reach it before granting credentials.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
Releases
README
LangChain MCP Integration

Overview
The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. There are plenty of open source MCP servers available, each one offering different tools for an LLM to use, but they're not compatible with LangChain's tool representation out of the box. This library provides a thin wrapper around the interaction with MCP servers, ensuring they're compatible with LangChain.
Installation
npm i @lambdaworks/langchain-mcp-integration
Usage
An example of how this library allows you to use MCP tools with a prebuilt ReAct agent from LangGraph:
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { HumanMessage } from "@langchain/core/messages";
import { MCPServerToolkit } from "@lambdaworks/langchain-mcp-integration";
const client = new Client({
name: "example-client",
version: "1.0.0"
});
const transport = new StdioClientTransport({
command: "uvx",
args: ["mcp-server-fetch", "--ignore-robots-txt"]
});
let toolkit = new MCPServerToolkit();
await toolkit.addTools({ client, transport });
const agentModel = new ChatOpenAI({ temperature: 0 });
const agent = createReactAgent({
llm: agentModel,
tools: toolkit.getTools()
});
const agentFinalState = await agent.invoke({
messages: [new HumanMessage("Summarize the information from this URL: https://rs.linkedin.com/company/lambdaworksio")]
});
console.log(
agentFinalState.messages[agentFinalState.messages.length - 1].content
);
You can also add tools from multiple MCP servers:
toolkit.addTools({ client: client1, transport: transport1 }, {client: client2, transport: transport2 });
Each client and transport pair are used to specify the way you'll connect to a specific MCP server.
README fetched from the repository 7 days ago. The original is authoritative.