How to integrate Chaser MCP with LangChain

This guide walks you through connecting Chaser to LangChain using the Composio tool router. By the end, you'll have a working Chaser agent that can create a new invoice for customer abc ltd, list all customers with overdue invoices, update email address for customer by external id through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Chaser account through Composio's Chaser MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Chaser is accounts receivable automation software that sends invoice reminders and helps businesses get paid faster. It streamlines the collections process to save time and improve cash flow.

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Introduction

This guide walks you through connecting Chaser to LangChain using the Composio tool router. By the end, you'll have a working Chaser agent that can create a new invoice for customer abc ltd, list all customers with overdue invoices, update email address for customer by external id through natural language commands.

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

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

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TL;DR

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

The Chaser MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Chaser account. It provides structured and secure access to your accounts receivable data, so your agent can create and manage invoices, update customer records, generate credit notes, and retrieve key financial details on your behalf.

  • Automated invoice management: Create, update, and track invoices programmatically—making it easy for your agent to help you stay on top of accounts receivable.
  • Customer information handling: Retrieve, create, and update customer records so your assistant can help onboard new clients or keep customer details up-to-date.
  • Credit note processing: Generate and fetch credit notes, allowing your agent to handle adjustments and reconciliations quickly and accurately.
  • Organization data retrieval: Access up-to-date information about your organization, including IDs, currency settings, and compliance details, for seamless financial operations.
  • Efficient accounts review: Instantly pull lists of customers or credit notes to review outstanding balances, statuses, and financial health—all through conversational prompts.

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 Chaser 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 Chaser 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: ['chaser']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Contact Person

Tool to create a new contact person for a customer in Chaser.

Create Invoice

Tool to create a new invoice record in the organization.

Create Overpayment

Creates a new overpayment record in Chaser for tracking customer overpayments.

Delete Contact Person

Tool to delete a contact person from a customer record in Chaser.

Get Contact Person by ID

Tool to get a specific contact person by ID for a customer.

Get Credit Note by ID

Retrieve detailed information for a specific credit note by its ID.

Get Credit Notes

Retrieves a list of credit notes from Chaser.

Get Current Organisation

Tool to retrieve information about the current organisation associated with the API credentials.

Get Customer by ID

Retrieve detailed information for a specific customer by their Chaser customer ID.

Get Customers

Tool to retrieve a list of all customers associated with the organization.

Get Invoice by ID

Tool to retrieve detailed information for a specific invoice by its ID.

Get Organization

Tool to retrieve information about the connected organizations.

Get Overpayment

Retrieve detailed information for a specific overpayment by its ID.

Get Status

Tool to check the status of the Chaser API.

List Contact Persons

Tool to retrieve contact persons for a specific customer.

List Invoices

Tool to retrieve invoices with pagination and filtering.

List Overpayments

Tool to retrieve overpayments from Chaser with pagination and filtering.

Create Credit Note

Creates a new credit note record in Chaser for tracking customer credits.

Create Customer

Tool to create a new customer record in Chaser.

Update Credit Note

Update an existing credit note in Chaser.

Update Customer

Tool to update an existing customer's information using their unique Chaser customer ID.

Update Invoice

Update an existing invoice in Chaser by its internal ID.

Update Contact Person

Tool to update a contact person for a customer in Chaser.

Update Overpayment

Tool to update an overpayment record in Chaser.

Upload Invoice PDF

Upload a PDF file to an existing invoice in Chaser.

Bulk Upsert Customers

Tool to bulk upsert up to 100 customers in a single operation.

Bulk Upsert Contact Persons

Tool to bulk insert or update contact persons for a customer.

Bulk Upsert Credit Notes

Tool to bulk upsert up to 100 credit notes in a single request.

Bulk Upsert Invoices

Tool to bulk upsert up to 100 invoices in a single request.

Bulk Upsert Overpayments

Tool to bulk upsert up to 100 overpayments in Chaser, matching by overpayment_id.

FRAMEWORKS

How to build Chaser MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Chaser MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Chaser MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Chaser MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Chaser MCP with Claude Cowork

Codex logo

Codex

Use Chaser MCP with Codex

OpenClaw logo

OpenClaw

Use Chaser MCP with OpenClaw

Hermes logo

Hermes

Use Chaser MCP with Hermes

CLI logo

CLI

Use Chaser MCP with CLI

Google ADK logo

Google ADK

Use Chaser MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Chaser MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Chaser MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Chaser MCP with LlamaIndex

CrewAI logo

CrewAI

Use Chaser MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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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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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