How to integrate Crustdata MCP with LangChain

This guide walks you through connecting Crustdata to LangChain using the Composio tool router. By the end, you'll have a working Crustdata agent that can find tech companies with recent funding milestones, enrich this lead's profile with latest data, list top decision makers in saas startups through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Crustdata account through Composio's Crustdata MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Crustdata

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CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.

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Introduction

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

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

Also integrate Crustdata with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The Crustdata MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Crustdata account. It provides structured and secure access to real-time company and people intelligence, so your agent can perform actions like lead enrichment, market research, investor portfolio analysis, and workforce trend tracking on your behalf.

Comprehensive person and company enrichment: Instantly enrich leads or companies with up-to-date details for customer profiling, data verification, or targeted outreach.
Advanced decision maker filtering: Find and analyze decision makers across organizations using complex filters, titles, and segmentation for your sales or marketing efforts.
Investor portfolio and funding milestone analysis: Retrieve in-depth investor portfolio data, analyze funding milestones, and generate reports for investment research or deal sourcing.
Workforce and job market trend insights: Fetch headcount and job listing timeseries data to track organizational growth, hiring activity, or competitive shifts in specific industries.
Social and web activity monitoring: Collect and analyze LinkedIn posts and web traffic data for any company to assess engagement, sentiment, and digital footprint for market intelligence and outreach strategies.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS

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

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

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

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 Crustdata functionality through MCP

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 Crustdata tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Crustdata 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 Crustdata tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "crustdata-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 Crustdata MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Crustdata tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

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 Crustdata 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

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 Crustdata 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: ['crustdata']
        }
    );

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

Enrich person screener

The screener_person_enrich endpoint enriches person data by providing additional information based on the given query.

Fetch headcount by facet timeseries

Retrieves headcount data as a timeseries with faceted analysis capabilities.

Fetch investor portfolio data

Retrieves comprehensive investor portfolio data from the Data Lab section of the CrustData API.

Filter decision makers data

Filters and retrieves decision maker data from the CrustData B2B SaaS integration platform based on complex criteria.

Post funding milestone timeseries data

The FundingMilestoneTimeseries endpoint retrieves time-series data related to funding milestones for companies.

Post headcount timeseries data

Retrieves filtered and sorted headcount timeseries data from the CrustData Data Lab.

Post job listings table data

This endpoint retrieves filtered and sorted job listings data for specified company tickers from a chosen dataset in the CrustData platform.

Post web traffic data

Retrieves filtered and sorted web traffic data from the CrustData platform.

Retrieve linkedin posts

Retrieves LinkedIn posts for a specified company using CrustData's screener functionality.

Screener company information

The GetCompanyScreener endpoint allows users to search and filter companies based on various criteria such as headcount, growth rate, funding, and more.

Screen metrics and filter conditions

The ScreenData endpoint enables advanced data screening and filtering on the CrustData platform.

Search companies with filters

The CompanySearch endpoint enables users to search and filter companies using the CrustData API.

Search for job id in screener

The screener_person_search endpoint allows users to search for persons associated with a specific job ID within the CrustData B2B SaaS integration platform.

Search linkedin posts by keyword

This endpoint enables searching for LinkedIn posts using a specific keyword.

FRAMEWORKS

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OpenAI Agents SDK

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Claude Agent SDK

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Claude Cowork

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Codex

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OpenClaw

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Hermes

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CLI

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Google ADK

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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CrewAI

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FAQ

Frequently asked questions

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

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

Start with Crustdata.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Crustdata tool your agent needs.Free to start.

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Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

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.

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.

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.

Nirman Dave

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.

Ryan Yu

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.

Tomisin Jenrola

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.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Crustdata MCP with LangChain

Connect Crustdata without auth hassles

Introduction

Also integrate Crustdata with

TL;DR

What is LangChain?

What is the Crustdata MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Crustdata MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Reddit

Facebook

Linkedin

Active campaign

Frequently asked questions

What are the differences in Tool Router MCP and Crustdata MCP?+

Can I use Tool Router MCP with LangChain?+

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

How safe is my data with Composio Tool Router?+

Start with Crustdata.It takes 30 seconds.

Teams ship real agents on Composio