How to integrate Parseur MCP with LangChain

This guide walks you through connecting Parseur to LangChain using the Composio tool router. By the end, you'll have a working Parseur agent that can list all documents in your invoices mailbox, create a webhook to send parsed receipts, pause the outgoing webhook for orders mailbox through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Parseur account through Composio's Parseur MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Parseur is an AI-powered data extraction platform that parses emails, PDFs, and documents into structured data. Automate tedious data entry and get actionable information, fast.

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Introduction

This guide walks you through connecting Parseur to LangChain using the Composio tool router. By the end, you'll have a working Parseur agent that can list all documents in your invoices mailbox, create a webhook to send parsed receipts, pause the outgoing webhook for orders mailbox through natural language commands.

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

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

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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 Parseur project to Composio
  • Create a Tool Router MCP session for Parseur
  • Initialize an MCP client and retrieve Parseur tools
  • Build a LangChain agent that can interact with Parseur
  • 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 Parseur MCP server, and what's possible with it?

The Parseur MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Parseur account. It provides structured and secure access to your Parseur data extraction workflows, so your agent can perform actions like managing mailboxes, handling documents, configuring webhooks, and automating template operations on your behalf.

  • Mailbox management and discovery: Let your agent list, browse, and filter all Parseur mailboxes to keep tabs on your parsing operations and document streams.
  • Document listing and retrieval: Effortlessly fetch documents from specific mailboxes, enabling automated sorting, searching, or pagination of your parsed files.
  • Template and parsing rule automation: Ask your agent to list templates within any mailbox, so you can quickly inspect or update parsing rules as your data extraction needs evolve.
  • Webhook configuration and control: Enable your agent to create, update, pause, enable, or delete webhooks, making it easy to automate real-time data delivery to your other systems.
  • Comprehensive webhook inspection: Retrieve detailed webhook information or list all webhooks for a mailbox, ensuring you always know how and where your parsed data is flowing.

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 Parseur 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 Parseur 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: ['parseur']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Parseur 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 Parseur tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "parseur-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 Parseur MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Parseur 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 Parseur 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 Parseur 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: ['parseur']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "parseur-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 Parseur 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 Parseur 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 Parseur action and event your agent gets out of the box.

Copy Document

Tool to copy a document to another mailbox.

Copy Mailbox

Tool to copy a mailbox (parser) in Parseur.

Create custom download

Tool to create a custom download (export configuration) for a mailbox.

Create mailbox

Tool to create a new mailbox (parser) in Parseur.

Delete custom download

Tool to delete a custom download (export configuration) from a mailbox.

Delete document

Tool to delete a specific document by ID.

Delete mailbox

Tool to delete a mailbox (parser) by ID.

Delete webhook

Tool to delete a specific webhook.

Disable webhook

Tool to disable a webhook for a mailbox.

Enable webhook

Enables a paused webhook for a specified mailbox, allowing it to receive and forward parsed document events.

Get Bootstrap Config

Tool to retrieve bootstrap configuration data.

Get Document

Tool to retrieve full details of a specific document by ID.

Get Document Logs

Tool to get document logs for a specific document.

Get Mailbox by ID

Tool to retrieve full mailbox (parser) configuration by ID.

Get Mailbox Schema

Tool to retrieve the JSON schema for a mailbox's parsed fields.

List Custom Downloads

Tool to retrieve custom downloads (export configurations) for a mailbox.

List Documents in Mailbox

Tool to list documents within a specific mailbox.

List Mailboxes (Full Details)

Tool to list mailboxes (parsers) with full configuration details.

List Templates for Mailbox

Tool to list all templates in a given mailbox.

Reprocess a document

Tool to reprocess a document.

Retrieve a webhook

Tool to retrieve details of a specific webhook.

Skip a document

Tool to skip a document.

Update custom download

Tool to update a custom download (export configuration) for a mailbox.

Update Mailbox

Tool to update a mailbox (parser) configuration.

Update webhook

Tool to update an existing webhook’s settings.

Upload Email Document

Tool to upload an email or text document to Parseur for parsing.

FRAMEWORKS

How to build Parseur MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Parseur MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Parseur MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Parseur MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Parseur MCP with Claude Cowork

Codex logo

Codex

Use Parseur MCP with Codex

OpenClaw logo

OpenClaw

Use Parseur MCP with OpenClaw

Hermes logo

Hermes

Use Parseur MCP with Hermes

CLI logo

CLI

Use Parseur MCP with CLI

Google ADK logo

Google ADK

Use Parseur MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Parseur MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Parseur MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Parseur MCP with LlamaIndex

CrewAI logo

CrewAI

Use Parseur MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Parseur MCP server, the agents and LLMs can only access a fixed set of Parseur tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Parseur 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 Parseur tools.

Yes, absolutely. You can configure which Parseur 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 Parseur data and credentials are handled as safely as possible.

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