Install
$ agentstack add mcp-lambdaworks-langchain-mcp-integration ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
LangChain MCP Integration [](https://www.npmjs.com/package/@lambdaworks/langchain-mcp-integration) [](https://opensource.org/licenses/Apache-2.0)
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.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: lambdaworks
- Source: lambdaworks/langchain-mcp-integration
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.