How to integrate Textrazor MCP with LangChain

This guide walks you through connecting Textrazor to LangChain using the Composio tool router. By the end, you'll have a working Textrazor agent that can extract named entities from this news article, summarize key phrases in customer reviews, classify support tickets by topic automatically through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Textrazor account through Composio's Textrazor MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Textrazor is a natural language processing API for extracting meaning, entities, and relationships from text. It enables powerful content analysis and sentiment detection for smarter applications.

6 Tools

Introduction

This guide walks you through connecting Textrazor to LangChain using the Composio tool router. By the end, you'll have a working Textrazor agent that can extract named entities from this news article, summarize key phrases in customer reviews, classify support tickets by topic automatically through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Textrazor account through Composio's Textrazor MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Textrazor with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Textrazor project to Composio
  • Create a Tool Router MCP session for Textrazor
  • Initialize an MCP client and retrieve Textrazor tools
  • Build a LangChain agent that can interact with Textrazor
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Textrazor MCP server, and what's possible with it?

The Textrazor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Textrazor account. It provides structured and secure access to advanced natural language processing features, so your agent can extract entities, classify content, analyze grammar, and understand relationships within text—all automatically and at scale.

  • Entity and relationship extraction: Enable your agent to identify and classify people, places, organizations, and relationships from any text, powering intelligent content analysis and knowledge graph building.
  • Text classification and categorization: Automatically categorize documents, articles, or snippets using built-in or custom classifiers, making it easy to sort and organize large volumes of text data.
  • Grammatical and dependency analysis: Let your agent parse sentence structure, analyze grammatical relationships, and build dependency trees to support advanced linguistic understanding and text analytics.
  • Custom dictionary and classifier management: Allow the agent to create and update custom entity dictionaries and classifiers, tailoring analysis to specialized domains or business needs.
  • Phrase extraction and sentiment detection: Extract key phrases, multi-word expressions, and even detect logical entailments or word senses, enabling deeper insights from any written content.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Textrazor functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Textrazor tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['textrazor']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Textrazor tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Textrazor tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "textrazor-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Textrazor MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Textrazor tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Textrazor related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Textrazor and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['textrazor']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "textrazor-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Textrazor related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Textrazor through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Textrazor action and event your agent gets out of the box.

Get Account Information

This tool retrieves comprehensive information about a TextRazor account, providing essential details about the account's status, usage, and limits.

Classify Text

This tool will classify text into predefined categories using TextRazor's classification capabilities.

Manage Custom Classifiers

This tool manages custom classifiers in TextRazor, allowing users to create, update, and manage custom classification categories.

Dictionary Manager

Manage custom entity dictionaries in TextRazor for enhanced named entity recognition.

Extract Named Entities from Text

Extract named entities (people, places, companies, etc.

Analyze Content with TextRazor

A comprehensive content analysis tool that combines multiple TextRazor extractors to perform a complete analysis of the input text.

FRAMEWORKS

How to build Textrazor MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Textrazor MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Textrazor MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Textrazor MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Textrazor MCP with Claude Cowork

Codex logo

Codex

Use Textrazor MCP with Codex

OpenClaw logo

OpenClaw

Use Textrazor MCP with OpenClaw

Hermes logo

Hermes

Use Textrazor MCP with Hermes

CLI logo

CLI

Use Textrazor MCP with CLI

Google ADK logo

Google ADK

Use Textrazor MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Textrazor MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Textrazor MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Textrazor MCP with LlamaIndex

CrewAI logo

CrewAI

Use Textrazor MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Textrazor MCP server, the agents and LLMs can only access a fixed set of Textrazor tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Textrazor and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Textrazor tools.

Yes, absolutely. You can configure which Textrazor scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Textrazor data and credentials are handled as safely as possible.

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