# How to integrate Extracta ai MCP with LangChain

```json
{
  "title": "How to integrate Extracta ai MCP with LangChain",
  "toolkit": "Extracta ai",
  "toolkit_slug": "extracta_ai",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/extracta_ai/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/extracta_ai/framework/langchain.md",
  "updated_at": "2026-05-12T10:11:04.640Z"
}
```

## Introduction

This guide walks you through connecting Extracta ai to LangChain using the Composio tool router. By the end, you'll have a working Extracta ai agent that can extract tables from a pdf invoice, pull key data from a scanned receipt, get extraction results for uploaded contract through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Extracta ai account through Composio's Extracta ai MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Extracta ai with

- [OpenAI Agents SDK](https://composio.dev/toolkits/extracta_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/extracta_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/extracta_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/extracta_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/extracta_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/extracta_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/extracta_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/extracta_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/extracta_ai/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/extracta_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/extracta_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/extracta_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/extracta_ai/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Extracta ai project to Composio
- Create a Tool Router MCP session for Extracta ai
- Initialize an MCP client and retrieve Extracta ai tools
- Build a LangChain agent that can interact with Extracta ai
- 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 Extracta ai MCP server, and what's possible with it?

The Extracta ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Extracta ai account. It provides structured and secure access to your document extraction workflows, so your agent can initiate new extractions, monitor extraction status, retrieve results, and manage extraction processes automatically on your behalf.
- Automated document data extraction: Instantly trigger new extraction processes on PDFs, images, or text files without manual intervention.
- Real-time extraction status tracking: Let your agent check the progress, view details, and fetch results from any ongoing or completed extraction.
- Seamless extraction management: Effortlessly delete or clean up extraction jobs after completion or if no longer needed, keeping your workspace organized.
- End-to-end workflow orchestration: Combine extraction initiation, monitoring, and cleanup into a single automated pipeline for maximum efficiency.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `EXTRACTA_AI_CREATE_CLASSIFICATION` | Create Classification | Creates a new document classification configuration. Define a list of possible document types with their characteristics (name, description, unique words). Returns a classification ID that can be used to upload documents for automatic type prediction. This is the first step before uploading documents for classification. |
| `EXTRACTA_AI_CREATE_EXTRACTION` | Create Extraction | Creates a new extraction configuration for processing documents. Define what fields to extract (e.g., names, dates, amounts) and processing options. Returns an extraction ID that can be used to upload and process files. This is the first step before uploading documents for extraction. |
| `EXTRACTA_AI_DELETE_CLASSIFICATION` | Delete Classification | Permanently deletes an entire document classification process including all associated batches, results, and uploaded files. Use this when you want to remove a classification that is no longer needed. WARNING: This action cannot be undone. |
| `EXTRACTA_AI_DELETE_EXTRACTION` | Delete Extraction | Permanently deletes an extraction job and its configuration from the system. Use this when you want to remove an extraction job that is no longer needed. This action is idempotent - calling it multiple times with the same ID will not cause errors. Requires the extraction ID obtained from creating or viewing an extraction. |
| `EXTRACTA_AI_GET_BATCH_RESULTS` | Get Batch Results | Retrieves extraction results for a specific batch of documents. Returns the extracted data for each file in the batch, along with processing status and file information. If the batch is still processing, results may be empty or incomplete. Maintain 2-second intervals between consecutive requests to avoid rate-limiting. |
| `EXTRACTA_AI_GET_CREDITS` | Get Credits | Retrieves the current credit balance available on the account. The system operates on a per-page consumption model where 1 credit = 1 page of document processing. Use this action to check remaining credits before processing documents. |
| `EXTRACTA_AI_UPDATE_CLASSIFICATION` | Update Classification | Updates an existing document classification by modifying its parameters. Use this to change the classification name, description, or document types (including their keywords and linked extractions). Requires the classification ID from a previously created classification. |
| `EXTRACTA_AI_UPDATE_EXTRACTION` | Update Extraction | Updates an existing document extraction process by modifying specified parameters. Only fields provided in the request are modified; omitted fields remain unchanged. Use this to change the extraction's name, description, language, fields to extract, or processing options without recreating the entire extraction job. |
| `EXTRACTA_AI_VIEW_CLASSIFICATION` | View Classification | Retrieves details of an existing classification configuration including name, description, document types, associated keywords, and linked extraction templates. Use this action to verify classification setup or retrieve configuration details for debugging and auditing purposes. |
| `EXTRACTA_AI_VIEW_EXTRACTION` | View Extraction | Retrieves detailed configuration and status information for an existing extraction job. Returns the extraction's name, description, language, configured fields, processing options, and any associated batches. Use this action to: - Check the configuration of an extraction job - Verify the fields that will be extracted - View processing options (table extraction, handwriting recognition, etc.) - Monitor batch status if files have been uploaded for processing Requires: extraction_id from a previously created extraction (via Create Extraction action) |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Extracta ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Extracta ai. It provides structured and secure access so your agent can perform Extracta ai operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

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

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Extracta ai tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Extracta ai 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 Extracta ai tools as needed
```python
# Create Tool Router session for Extracta ai
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['extracta_ai']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "extracta_ai-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "extracta_ai-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Extracta ai related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Extracta ai 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['extracta_ai']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "extracta_ai-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Extracta ai related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
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: ['extracta_ai']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "extracta_ai-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 Extracta ai 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 Extracta ai 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.

## How to build Extracta ai MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/extracta_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/extracta_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/extracta_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/extracta_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/extracta_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/extracta_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/extracta_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/extracta_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/extracta_ai/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/extracta_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/extracta_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/extracta_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/extracta_ai/framework/crew-ai)

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.
- [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems.
- [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project.
- [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks.
- [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API.
- [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Extracta ai MCP?

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

### Can I use Tool Router MCP with LangChain?

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 Extracta ai tools.

### Can I manage the permissions and scopes for Extracta ai while using Tool Router?

Yes, absolutely. You can configure which Extracta ai 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.

### How safe is my data with Composio Tool Router?

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 Extracta ai data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)