How to integrate Affinda MCP with LangChain

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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

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.

Supported Tools & Triggers

Tools
Add Tag to DocumentsTool to add a tag to multiple documents in a single operation.
Batch Update AnnotationsBatch update multiple document annotations in a single API call.
Create API UserTool to create a new API user within an organization.
Batch Create AnnotationsBatch create multiple document annotations in a single API call.
Create CollectionTool to create a new collection.
Create Data Field For CollectionTool to create a data field for a collection along with a new data point.
Create Data SourceTool to create a custom mapping data source.
Create Data Source ValueTool to add a new value to a mapping data source.
Create DocumentUpload a document to Affinda for parsing and data extraction.
Create Document TypeTool to create a new document type in the specified organization.
Create ExtractorTool to create a new extractor.
Create Document from DataCreate a document from structured resume or job description data for Search & Match.
Create IndexTool to create a new index for search and match functionality.
Create InvitationTool to create a new organization invitation.
Create Job Description SearchSearch through parsed job descriptions using custom criteria or resume matching.
Create Job Description Search Embed URLTool to create and return a signed URL for the embeddable job description search tool.
Create OrganizationTool to create a new organization.
Create RESTHook SubscriptionTool to create a new RESTHook subscription.
Create Resume SearchTool to search through parsed resumes using three methods: match to a job description, match to a resume, or custom criteria.
Create Resume Search Embed URLTool to create and return a signed URL for the embeddable resume search tool.
Create TagCreates a new tag in the specified workspace.
Create Validation ResultCreate a validation result for document annotations in Affinda.
Batch Create Validation ResultsBatch create multiple validation results for document annotations in a single API call.
Create WorkspaceTool to create a new workspace.
Create Workspace MembershipTool to add a user to a workspace by creating a membership.
Batch Delete AnnotationsBatch delete multiple document annotations in a single API call.
Delete CollectionPermanently delete a collection from Affinda by its identifier.
Delete Data SourcePermanently delete a mapping data source from the database by its identifier.
Delete Data Source ValueTool to delete a specific value from a mapping data source.
Delete DocumentTool to delete a specific document by its ID.
Delete Document TypeTool to permanently delete a document type by its identifier.
Delete IndexTool to permanently delete an index from Affinda by its name.
Delete InvitationTool to delete an invitation by its identifier.
Delete OrganizationPermanently deletes an organization from Affinda.
Delete Resthook SubscriptionTool to delete a specific resthook subscription by ID.
Delete TagPermanently delete a tag from Affinda by its ID.
Delete Validation ResultsDelete multiple validation results in a single API call.
Delete WorkspaceTool to delete a specific workspace by its ID.
Delete Workspace MembershipTool to remove a user from a workspace by membership ID.
Get All API UsersTool to retrieve a list of all API users.
Get All Document SplittersTool to get a list of all document splitters.
Get All InvitationsTool to retrieve all invitations you created or sent to you.
Get Organization MembershipsRetrieve all organization memberships across the account.
Get TagsTool to list all tags.
Get All Validation ResultsTool to list validation results for documents.
Get Workspace MembershipsRetrieve all workspace memberships across the account.
Get AnnotationsRetrieves all annotations for a specific document.
Get CollectionTool to retrieve details of a specific collection by its ID.
Get CollectionsTool to retrieve a list of all collections.
Get Data SourceTool to retrieve details of a specific mapping data source by its identifier.
Get Data Source ValueTool to retrieve a specific value from a mapping data source.
Get Data Source ValuesTool to retrieve all values from a mapping data source.
Get DocumentRetrieve full details and parsed data for a specific document by its identifier.
Get Document RedactedTool to retrieve the redacted version of a document as a PDF file.
Get DocumentsTool to retrieve a list of all documents.
Get Document SplitterTool to retrieve details of a specific document splitter by its identifier.
Get Document TypeTool to retrieve details of a specific document type by its ID.
Get Document Type JSON SchemaTool to generate a JSON schema from a document type by its identifier.
Get Document Type Pydantic ModelsTool to generate Pydantic model code from a document type's schema.
Get Document TypesRetrieve all document types accessible to the authenticated user.
Get ExtractorTool to retrieve detailed information about a specific extractor by its identifier.
Get ExtractorsRetrieve all extractors available for an organization.
Get Index DocumentsTool to retrieve all indexed documents for a specific index.
Get InvitationTool to retrieve details of a specific organization invitation by its identifier.
Get Job Description Search ConfigTool to get the configuration for the logged in user's embeddable job description search tool.
Get MappingTool to retrieve a specific mapping by its identifier.
Get OrganizationTool to retrieve details of a specific organization by its ID.
Get Organization MembershipTool to retrieve details of a specific organization membership by its ID.
Get OrganizationsRetrieves all organizations accessible to the authenticated user.
Get Resthook SubscriptionTool to retrieve details of a specific resthook subscription by its ID.
Get RESTHook SubscriptionsTool to retrieve a list of all RESTHook subscriptions.
Get TagTool to retrieve details of a specific tag by its ID.
Get Usage by WorkspaceRetrieves monthly document processing usage statistics for a specific workspace.
Get WorkspaceTool to retrieve details of a specific workspace by its ID.
Get Workspace MembershipTool to retrieve details of a specific workspace membership by its ID.
Get WorkspacesTool to retrieve a list of all workspaces.
List Data PointsTool to retrieve all data points.
List Data SourcesTool to retrieve the list of all custom mapping data sources.
List IndexesTool to retrieve a list of all search indexes.
List MappingsTool to retrieve the list of all custom data mappings.
List Occupation GroupsTool to retrieve the list of searchable occupation groups.
List Resume Search ConfigTool to get the configuration for the logged in user's embeddable resume search tool.
List Resume Search Job Title SuggestionsTool to get job title suggestions based on provided job title(s).
List Resume Search Skill SuggestionsTool to get skill suggestions based on provided skills.
Remove Tag from DocumentsRemove a tag from multiple documents in a single batch operation.
Replace Data Source ValuesTool to completely replace all values in a mapping data source.
Split Document PagesSplit a document into multiple documents by dividing its pages.
Update AnnotationTool to update data of a single annotation in Affinda.
Update CollectionTool to update specific fields of a collection.
Update Data Field For CollectionTool to update a data field configuration for a collection's data point.
Update Data Source ValueTool to update an existing value in a mapping data source.
Update DocumentTool to update specific fields of a document.
Update Document DataUpdate parsed data for a resume or job description document in Affinda.
Update Document TypeTool to update a document type by its identifier.
Update ExtractorTool to update specific fields of an extractor.
Update IndexTool to update the name of an existing search index.
Update InvitationTool to update an organization invitation's role.
Update Job Description Search ConfigTool to update the configuration for the logged in user's embeddable job description search tool.
Update MappingTool to update a specific mapping's settings.
Update OrganizationTool to update specific fields of an organization.
Update Organization MembershipTool to update an organization membership's role.
Update RESTHook SubscriptionTool to update an existing RESTHook subscription.
Update Resume Search ConfigTool to update the configuration for the logged in user's embeddable resume search tool.
Update TagTool to update data of a tag.
Update WorkspaceTool to update specific fields of a workspace.

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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

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

Initialize Composio client

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")
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

Create a Tool Router session

# Create Tool Router session for Affinda
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['affinda']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "affinda-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)
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
  • get_tools() retrieves all available Affinda tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Affinda 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")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

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=['affinda']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "affinda-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 Affinda 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())

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.

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FAQ

What are the differences in Tool Router MCP and Affinda MCP?

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.

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 Affinda tools.

Can I manage the permissions and scopes for Affinda while using Tool Router?

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.

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 Affinda data and credentials are handled as safely as possible.

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