How to integrate Diffbot MCP with LangChain

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Introduction

This guide walks you through connecting Diffbot to LangChain using the Composio tool router. By the end, you'll have a working Diffbot agent that can extract specs and reviews from a product page, summarize key details from a news article url, list all bulk data extraction jobs for your account through natural language commands.

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

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

Also integrate Diffbot with

TL;DR

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

The Diffbot MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Diffbot account. It provides structured and secure access to web data extraction and analysis, so your agent can extract structured data from web pages, analyze content types, retrieve product details, manage bulk jobs, and search extracted datasets on your behalf.

  • Automatic content analysis and extraction: Let your agent analyze any web page and automatically extract structured data such as articles, products, events, images, or videos using AI-powered tools.
  • Article and discussion thread extraction: Effortlessly pull detailed metadata, authors, publication dates, and full discussion threads from news sites, blogs, forums, and comment sections.
  • Product and event data gathering: Instantly extract comprehensive product specifications, pricing, reviews, and event information including venues, dates, and descriptions from e-commerce or event pages.
  • Bulk job management and search: Enable your agent to list, monitor, and search across large-scale crawl or extraction jobs, making it easy to work with massive web data collections.
  • Account and usage insights: Retrieve your Diffbot account details, plan information, and usage statistics to stay on top of quotas and manage your web data operations efficiently.

Supported Tools & Triggers

Tools
Combine Entity ProfilesCombine multiple entity profiles into a unified view using the Diffbot Knowledge Graph.
Create Bulk Extract JobTool to submit a bulk extract job to process multiple URLs with Extract APIs.
Create or Update Custom APITool to create or update the parameters and ruleset of a Custom API.
Create Bulk Enhance JobTool to submit a bulk enhance job to enrich multiple entities asynchronously.
Delete Custom APITool to delete custom API definitions for a given URL pattern.
Delete KG Enhance BulkjobTool to delete an Enhance Bulkjob.
Download Bulk Job ResultsTool to download results of a bulk enhance job with filtering options via POST request.
Enhance Entity with Knowledge GraphEnrich a person or organization with comprehensive data from the Diffbot Knowledge Graph.
Diffbot Extract JobTool to extract structured job posting data from job listing pages.
Diffbot Extract ListTool to extract structured data from list-style pages like news indexes, product listings, and directory pages.
Get Diffbot Account DetailsRetrieves comprehensive Diffbot account information including subscription plan details, credit balance, usage history, and account status.
Diffbot AnalyzeAutomatically analyzes a web page to determine its type and extract structured data.
Get Article DataTool to extract information from articles, including authors, publication dates, and images.
Get Bulk Job DataTool to download extracted results from a completed bulk job.
Get Bulk Job StatusTool to poll the status of a specific Diffbot Knowledge Graph Enhance bulk job.
Get Bulk Job ResultsTool to download the results of a completed Enhance Bulkjob.
Get Bulk Single ResultTool to download the result of a single job within a Diffbot bulk enhance job.
Get Crawl DataDownload extracted results from a completed crawl job.
Get Discussion ThreadExtract structured discussion threads from web pages including forums, comment sections, product reviews, Reddit discussions, and blog comments.
Diffbot Get EventTool to extract event details from web pages.
Diffbot Get ImageTool to extract detailed information about images, including dimensions and recognition data.
Get KG Coverage Report by IDDownload Knowledge Graph coverage report by report ID.
Diffbot Get ProductTool to extract product information such as specifications, prices, availability, and reviews.
Get Video DataTool to extract information from videos, including titles, descriptions, and embedded HTML.
List Bulk JobsTool to list all Bulk jobs associated with a specific token.
List Bulk Jobs Status For TokenTool to get the status of all bulk enhance jobs for a token.
List Custom APIsTool to retrieve all Custom APIs and their extraction rules currently defined on your Diffbot token.
Manage Crawl JobManages Diffbot crawl jobs: pause, restart, delete, or view status.
Resolve Lost IDTool to resolve lost IDs in the Knowledge Graph.
Diffbot Knowledge Graph SearchSearch the Diffbot Knowledge Graph using DQL (Diffbot Query Language).
Search Crawl Job DataTool to query crawl job collections using DQL (Diffbot Query Language).
Start Bulk JobTool to start a Bulk Extract job.
Start Crawl JobInitiates a Diffbot crawl job that spiders a website starting from seed URLs and processes discovered pages with a specified Extract API.
Stop Bulk JobTool to pause (stop) a running Bulk job.
Stop KG Bulk Job By IDTool to stop an active Knowledge Graph Enhance bulk job by its ID.

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 Diffbot 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 Diffbot tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "diffbot-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 Diffbot MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Diffbot 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 Diffbot 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 Diffbot 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=['diffbot']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "diffbot-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 Diffbot 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 Diffbot 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 Diffbot MCP?

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

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

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

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