How to integrate PredictLeads MCP with LangChain

This guide walks you through connecting PredictLeads to LangChain using the Composio tool router. By the end, you'll have a working PredictLeads agent that can discover companies, find similar companies, search technologies, and review company intelligence through natural language commands. This guide will help you understand how to give your LangChain agent real control over a PredictLeads account through Composio's PredictLeads MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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PredictLeads is a company intelligence API for hiring, technology, funding, news, product, and connection data. Use it to spot growth signals and enrich sales, research, and market workflows with fresh company context.

9 Tools

Introduction

This guide walks you through connecting PredictLeads to LangChain using the Composio tool router. By the end, you'll have a working PredictLeads agent that can discover companies, find similar companies, search technologies, and review company intelligence through natural language commands.

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

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

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

The PredictLeads MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your PredictLeads account. It provides structured and secure access to company profiles, hiring activity, technology adoption, financing, news, products, and business connections, so your agent can discover companies, research growth signals, compare similar businesses, search technologies, and monitor followed companies on your behalf.

  • Target company discovery: Have your agent find companies that match specific firmographic criteria and gather extended profiles for sales research.
  • Company intelligence research: Direct your agent to retrieve company details and investigate hiring, technology detections, news, financing, regulatory filings, business connections, website changes, repositories, and products.
  • Growth signal discovery: Let the agent search across companies for job openings, technology adoption, news, financing, investor portfolio connections, products, and startup platform posts.
  • Market and competitor analysis: Instruct your agent to find businesses similar to a chosen company and search the tracked technology catalog by name.
  • Account and company monitoring: Have your agent review followed companies and check your PredictLeads subscription status, monthly credit quota, and credit usage.

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 PredictLeads 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 PredictLeads 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: ['predict_leads']
    }
);

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

Configure the agent with the MCP URL

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

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

Discover Companies

Find companies using firmographic filters.

Discover Intelligence

Discover one page of jobs, technology adoption, news, financing, investor portfolio connections, products, or startup-platform posts across companies.

Get API Subscription

Check PredictLeads subscription status, monthly credit quota, and credits used.

Get Company

Retrieve one company by PredictLeads UUID or bare domain.

Get Company Intelligence

Retrieve one page of a company's jobs, technology detections, news, financing, SEC filings, connections, website changes, GitHub repositories, or products.

Get Intelligence Record

Retrieve one job opening, extended technology detection, technology, news event, SEC filing, or product by identifier.

Get Similar Companies

Find one page of companies similar to a specified PredictLeads company UUID or domain.

List Followed Companies

List one page of companies already followed for PredictLeads webhook updates.

Search Technologies

Search PredictLeads' tracked technology catalog by fuzzy name.

FRAMEWORKS

How to build PredictLeads MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use PredictLeads MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use PredictLeads MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use PredictLeads MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use PredictLeads MCP with Claude Cowork

Codex logo

Codex

Use PredictLeads MCP with Codex

OpenClaw logo

OpenClaw

Use PredictLeads MCP with OpenClaw

Hermes logo

Hermes

Use PredictLeads MCP with Hermes

CLI logo

CLI

Use PredictLeads MCP with CLI

Google ADK logo

Google ADK

Use PredictLeads MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use PredictLeads MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use PredictLeads MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use PredictLeads MCP with LlamaIndex

CrewAI logo

CrewAI

Use PredictLeads MCP with CrewAI

FAQ

Frequently asked questions

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

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

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