How to integrate Airparser MCP with LangChain

This guide walks you through connecting Airparser to LangChain using the Composio tool router. By the end, you'll have a working Airparser agent that can extract invoice data from supplier emails, parse resumes into structured candidate records, export receipt fields from uploaded pdfs through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Airparser account through Composio's Airparser MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Airparser is an AI-powered parser for extracting structured data from emails, documents, PDFs, and files. It turns messy inbound content into clean fields your apps and workflows can use.

10 Tools

Introduction

This guide walks you through connecting Airparser to LangChain using the Composio tool router. By the end, you'll have a working Airparser agent that can extract invoice data from supplier emails, parse resumes into structured candidate records, export receipt fields from uploaded pdfs through natural language commands.

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

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

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

The Airparser MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Airparser account. It provides structured and secure access to your document inboxes, extraction schemas, parsed documents, and account usage, so your agent can create parsers, extract structured data, review documents, manage schemas, and monitor parsing credits on your behalf.

  • Document parsing and extraction: Have your agent upload a document to an inbox, wait for processing, and return the extracted data as structured JSON.
  • Inbox and parser management: Let the agent create inboxes for document families, review their parsing settings, list available inboxes, or delete inboxes you no longer need.
  • Extraction schema control: Direct your agent to create or replace an inbox's extraction schema, or copy a working schema from one inbox to another.
  • Parsed document review: Instruct your agent to list or search documents in an inbox, then retrieve a document's parsing status and extracted output.
  • Account usage monitoring: Have your agent check your Airparser plan, account verification state, and parsing credit usage.

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 Airparser 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 Airparser 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: ['airparser']
    }
);

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

Configure the agent with the MCP URL

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

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

Clone Extraction Schema

Replace a destination inbox's extraction schema with a copy of a source inbox's schema.

Create Inbox

Create an Airparser inbox/parser for a document family using a text or vision extraction engine.

Delete Inbox

Permanently delete an Airparser inbox and every document it contains.

Get Account Usage

Return the connected Airparser account's role, verification state, plan, and parsing-credit usage without exposing personal, secret, or internal account fields.

Get Document

Get parsed output and status for a document, optionally including extended source and navigation details.

Get Inbox

Get one Airparser inbox, including its current extraction schema and useful parsing configuration.

List Documents

List and search documents in one inbox, one page at a time.

List Inboxes

List the Airparser inboxes available to the connected account.

Parse Document Synchronously

Upload one document to an inbox and wait for its extracted JSON.

Upsert Extraction Schema

Create or completely replace the extraction schema used by an Airparser inbox.

FRAMEWORKS

How to build Airparser MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Airparser MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Airparser MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Airparser MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Airparser MCP with Claude Cowork

Codex logo

Codex

Use Airparser MCP with Codex

OpenClaw logo

OpenClaw

Use Airparser MCP with OpenClaw

Hermes logo

Hermes

Use Airparser MCP with Hermes

CLI logo

CLI

Use Airparser MCP with CLI

Google ADK logo

Google ADK

Use Airparser MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Airparser MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Airparser MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Airparser MCP with LlamaIndex

CrewAI logo

CrewAI

Use Airparser MCP with CrewAI

FAQ

Frequently asked questions

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

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

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