How to integrate Affinda MCP with LangChain

This guide walks you through connecting Affinda to LangChain using the Composio tool router. By the end, you'll have a working Affinda agent that can extract invoice data from uploaded pdf, delete a document no longer needed, create a new tag for hr documents through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Affinda account through Composio's Affinda MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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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.

105 Tools

Introduction

This guide walks you through connecting Affinda to LangChain using the Composio tool router. By the end, you'll have a working Affinda agent that can extract invoice data from uploaded pdf, delete a document no longer needed, create a new tag for hr documents through natural language commands.

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

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

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

The Affinda MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Affinda account. It provides structured and secure access to your document processing workflows, so your agent can upload files, extract data, organize workspaces, label documents, and automate annotation management on your behalf.

  • AI-powered document upload and extraction: Instantly have your agent upload new documents for parsing and extract structured data from various formats using Affinda's advanced AI models.
  • Workspace and collection management: Let your agent create, group, and organize documents into collections and workspaces, keeping your document processing streamlined and organized.
  • Automated annotation updates: Empower your agent to batch update or modify multiple document annotations in a single request, saving you time on manual corrections.
  • Document tagging and organization: Direct your agent to create tags and label documents, making it easy to categorize and quickly retrieve important files.
  • Effortless cleanup and resource management: Have your agent delete unwanted documents or collections, ensuring your Affinda account stays tidy and relevant at all times.

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 Affinda 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 Affinda 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: ['affinda']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Tag to Documents

Tool to add a tag to multiple documents in a single operation.

Batch Update Annotations

Batch update multiple document annotations in a single API call.

Create API User

Tool to create a new API user within an organization.

Batch Create Annotations

Batch create multiple document annotations in a single API call.

Create Collection

Tool to create a new collection.

Create Data Field For Collection

Tool to create a data field for a collection along with a new data point.

Create Data Source

Tool to create a custom mapping data source.

Create Data Source Value

Tool to add a new value to a mapping data source.

Create Document

Upload a document to Affinda for parsing and data extraction.

Create Document Type

Tool to create a new document type in the specified organization.

Create Extractor

Tool to create a new extractor.

Create Document from Data

Create a document from structured resume or job description data for Search & Match.

Create Index

Tool to create a new index for search and match functionality.

Create Invitation

Tool to create a new organization invitation.

Create Job Description Search

Search through parsed job descriptions using custom criteria or resume matching.

Create Job Description Search Embed URL

Tool to create and return a signed URL for the embeddable job description search tool.

Create Organization

Tool to create a new organization.

Create RESTHook Subscription

Tool to create a new RESTHook subscription.

Create Resume Search

Tool to search through parsed resumes using three methods: match to a job description, match to a resume, or custom criteria.

Create Resume Search Embed URL

Tool to create and return a signed URL for the embeddable resume search tool.

Create Tag

Creates a new tag in the specified workspace.

Create Validation Result

Create a validation result for document annotations in Affinda.

Batch Create Validation Results

Batch create multiple validation results for document annotations in a single API call.

Create Workspace

Tool to create a new workspace.

Create Workspace Membership

Tool to add a user to a workspace by creating a membership.

Batch Delete Annotations

Batch delete multiple document annotations in a single API call.

Delete Collection

Permanently delete a collection from Affinda by its identifier.

Delete Data Source

Permanently delete a mapping data source from the database by its identifier.

Delete Data Source Value

Tool to delete a specific value from a mapping data source.

Delete Document

Tool to delete a specific document by its ID.

Delete Document Type

Tool to permanently delete a document type by its identifier.

Delete Index

Tool to permanently delete an index from Affinda by its name.

Delete Invitation

Tool to delete an invitation by its identifier.

Delete Organization

Permanently deletes an organization from Affinda.

Delete Resthook Subscription

Tool to delete a specific resthook subscription by ID.

Delete Tag

Permanently delete a tag from Affinda by its ID.

Delete Validation Results

Delete multiple validation results in a single API call.

Delete Workspace

Tool to delete a specific workspace by its ID.

Delete Workspace Membership

Tool to remove a user from a workspace by membership ID.

Get All API Users

Tool to retrieve a list of all API users.

Get All Document Splitters

Tool to get a list of all document splitters.

Get All Invitations

Tool to retrieve all invitations you created or sent to you.

Get Organization Memberships

Retrieve all organization memberships across the account.

Get Tags

Tool to list all tags.

Get All Validation Results

Tool to list validation results for documents.

Get Workspace Memberships

Retrieve all workspace memberships across the account.

Get Annotations

Retrieves all annotations for a specific document.

Get Collection

Tool to retrieve details of a specific collection by its ID.

Get Collections

Tool to retrieve a list of all collections.

Get Data Source

Tool to retrieve details of a specific mapping data source by its identifier.

Get Data Source Value

Tool to retrieve a specific value from a mapping data source.

Get Data Source Values

Tool to retrieve all values from a mapping data source.

Get Document

Retrieve full details and parsed data for a specific document by its identifier.

Get Document Redacted

Tool to retrieve the redacted version of a document as a PDF file.

Get Documents

Tool to retrieve a list of all documents.

Get Document Splitter

Tool to retrieve details of a specific document splitter by its identifier.

Get Document Type

Tool to retrieve details of a specific document type by its ID.

Get Document Type JSON Schema

Tool to generate a JSON schema from a document type by its identifier.

Get Document Type Pydantic Models

Tool to generate Pydantic model code from a document type's schema.

Get Document Types

Retrieve all document types accessible to the authenticated user.

Get Extractor

Tool to retrieve detailed information about a specific extractor by its identifier.

Get Extractors

Retrieve all extractors available for an organization.

Get Index Documents

Tool to retrieve all indexed documents for a specific index.

Get Invitation

Tool to retrieve details of a specific organization invitation by its identifier.

Get Job Description Search Config

Tool to get the configuration for the logged in user's embeddable job description search tool.

Get Mapping

Tool to retrieve a specific mapping by its identifier.

Get Organization

Tool to retrieve details of a specific organization by its ID.

Get Organization Membership

Tool to retrieve details of a specific organization membership by its ID.

Get Organizations

Retrieves all organizations accessible to the authenticated user.

Get Resthook Subscription

Tool to retrieve details of a specific resthook subscription by its ID.

Get RESTHook Subscriptions

Tool to retrieve a list of all RESTHook subscriptions.

Get Tag

Tool to retrieve details of a specific tag by its ID.

Get Usage by Workspace

Retrieves monthly document processing usage statistics for a specific workspace.

Get Workspace

Tool to retrieve details of a specific workspace by its ID.

Get Workspace Membership

Tool to retrieve details of a specific workspace membership by its ID.

Get Workspaces

Tool to retrieve a list of all workspaces.

List Data Points

Tool to retrieve all data points.

List Data Sources

Tool to retrieve the list of all custom mapping data sources.

List Indexes

Tool to retrieve a list of all search indexes.

List Mappings

Tool to retrieve the list of all custom data mappings.

List Occupation Groups

Tool to retrieve the list of searchable occupation groups.

List Resume Search Config

Tool to get the configuration for the logged in user's embeddable resume search tool.

List Resume Search Job Title Suggestions

Tool to get job title suggestions based on provided job title(s).

List Resume Search Skill Suggestions

Tool to get skill suggestions based on provided skills.

Remove Tag from Documents

Remove a tag from multiple documents in a single batch operation.

Replace Data Source Values

Tool to completely replace all values in a mapping data source.

Split Document Pages

Split a document into multiple documents by dividing its pages.

Update Annotation

Tool to update data of a single annotation in Affinda.

Update Collection

Tool to update specific fields of a collection.

Update Data Field For Collection

Tool to update a data field configuration for a collection's data point.

Update Data Source Value

Tool to update an existing value in a mapping data source.

Update Document

Tool to update specific fields of a document.

Update Document Data

Update parsed data for a resume or job description document in Affinda.

Update Document Type

Tool to update a document type by its identifier.

Update Extractor

Tool to update specific fields of an extractor.

Update Index

Tool to update the name of an existing search index.

Update Invitation

Tool to update an organization invitation's role.

Update Job Description Search Config

Tool to update the configuration for the logged in user's embeddable job description search tool.

Update Mapping

Tool to update a specific mapping's settings.

Update Organization

Tool to update specific fields of an organization.

Update Organization Membership

Tool to update an organization membership's role.

Update RESTHook Subscription

Tool to update an existing RESTHook subscription.

Update Resume Search Config

Tool to update the configuration for the logged in user's embeddable resume search tool.

Update Tag

Tool to update data of a tag.

Update Workspace

Tool to update specific fields of a workspace.

FRAMEWORKS

How to build Affinda MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Affinda MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Affinda MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Affinda MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Affinda MCP with Claude Cowork

Codex logo

Codex

Use Affinda MCP with Codex

OpenClaw logo

OpenClaw

Use Affinda MCP with OpenClaw

Hermes logo

Hermes

Use Affinda MCP with Hermes

CLI logo

CLI

Use Affinda MCP with CLI

Google ADK logo

Google ADK

Use Affinda MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Affinda MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Affinda MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Affinda MCP with LlamaIndex

CrewAI logo

CrewAI

Use Affinda MCP with CrewAI

FAQ

Frequently asked questions

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

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

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

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Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

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CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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