How to integrate ApptiveGrid MCP with LangChain

This guide walks you through connecting ApptiveGrid to LangChain using the Composio tool router. By the end, you'll have a working ApptiveGrid agent that can list overdue records in tasks grid, create form entry for customer feedback, summarize records from sales pipeline grid through natural language commands. This guide will help you understand how to give your LangChain agent real control over a ApptiveGrid account through Composio's ApptiveGrid MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ApptiveGrid is a collaborative no-code database and app platform for spaces, grids, forms, and structured records. It helps teams organize operational data, collect inputs, and build lightweight business apps without custom backend work.

7 Tools

Introduction

This guide walks you through connecting ApptiveGrid to LangChain using the Composio tool router. By the end, you'll have a working ApptiveGrid agent that can list overdue records in tasks grid, create form entry for customer feedback, summarize records from sales pipeline grid through natural language commands.

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

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

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

The ApptiveGrid MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ApptiveGrid account. It provides structured and secure access to your spaces, grids, field structures, and records, so your agent can discover workspaces, inspect grids, browse records, retrieve specific entries, and create new records on your behalf.

  • Account overview: Have your agent check your ApptiveGrid profile, plan, and available seats before working with your spaces.
  • Space and grid discovery: Let the agent list your available spaces and explore the grids contained within each one.
  • Grid structure inspection: Direct your agent to review a grid's fields and structure before reading or adding data.
  • Record browsing and retrieval: Instruct your agent to list, filter, and sort grid records, or fetch a specific record for closer review.
  • New record creation: Have your agent add a record to a grid using values that match its defined fields.

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 ApptiveGrid 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 ApptiveGrid 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: ['apptivegrid']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Record

Create one record in a grid from a provider-native object keyed by that grid's field IDs or field keys; inspect the grid first to construct valid values.

Get Current User

Return the connected ApptiveGrid user's ID, plan, seat availability, and resource links; use it to identify the user before listing spaces.

Get Grid

Return a grid's metadata and dynamic field definitions; use it before listing, reading, or creating records.

Get Record

Return one record from a grid by record ID, optionally rendered with a provider layout.

Get Space

Return a space and its grid summaries or grid links; use it to discover grid IDs before reading records.

List Records

Return one 1-based page of records from a grid, optionally using the provider's layout, filter, and sorting definitions.

List Spaces

Return one 1-based page of spaces available to a user, including IDs needed to inspect a space.

FRAMEWORKS

How to build ApptiveGrid MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use ApptiveGrid MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use ApptiveGrid MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use ApptiveGrid MCP with Claude Cowork

Codex logo

Codex

Use ApptiveGrid MCP with Codex

OpenClaw logo

OpenClaw

Use ApptiveGrid MCP with OpenClaw

Hermes logo

Hermes

Use ApptiveGrid MCP with Hermes

CLI logo

CLI

Use ApptiveGrid MCP with CLI

Google ADK logo

Google ADK

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

Use ApptiveGrid MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use ApptiveGrid MCP with LlamaIndex

CrewAI logo

CrewAI

Use ApptiveGrid MCP with CrewAI

FAQ

Frequently asked questions

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

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

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