How to integrate Flatfile MCP with LangChain

This guide walks you through connecting Flatfile to LangChain using the Composio tool router. By the end, you'll have a working Flatfile agent that can browse your spaces, view your workbooks, track import jobs, add records, and update records through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Flatfile account through Composio's Flatfile MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

This guide walks you through connecting Flatfile to LangChain using the Composio tool router. By the end, you'll have a working Flatfile agent that can browse your spaces, view your workbooks, track import jobs, add records, and update records through natural language commands.

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

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

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

The Flatfile MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Flatfile account. It provides structured and secure access to your environments, spaces, workbooks, sheets, records, files, and jobs, so your agent can discover data workspaces, inspect sheets, read and edit records, track files, and monitor jobs on your behalf.

  • Workspace and workbook discovery: Have your agent find environments, search spaces, and list workbooks with their sheets before starting a data task.
  • Sheet inspection and record retrieval: Let the agent inspect sheet fields and record counts, then read records based on validity, field values, or search terms.
  • Record creation and updates: Direct your agent to add rows to a sheet or update existing records with corrected and transformed values.
  • Record cleanup: Instruct your agent to permanently delete selected records from a Flatfile sheet when they are no longer needed.
  • File and job monitoring: Have your agent list import or export files, check their processing status, and review job progress, results, and diagnostic details.

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 Flatfile 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 Flatfile 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: ['flatfile']
    }
);

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

Configure the agent with the MCP URL

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

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

Delete Records

Permanently delete one to 100 explicitly identified records from one Flatfile sheet.

Get Job

Get detailed status, outcome, result, and diagnostic information for one Flatfile asynchronous job.

Get Sheet

Get one sheet's field configuration and record counts before reading or writing records.

Get Space

Get safe metadata for one Flatfile space without exposing transient access credentials or token-bearing guest links.

Insert Records

Insert one or more rows into a Flatfile sheet using simple field-key to value objects.

List Environments

List Flatfile environments visible to the connected environment secret key and return the IDs needed to find spaces.

List Files

Discover import or export files and their processing status, optionally within a Flatfile space.

List Jobs

Find asynchronous Flatfile jobs by environment, space, workbook, file, or parent job and inspect progress at a glance.

List Records

Read one page of records from a Flatfile sheet, optionally filtering by validity, field, record ID, search value, or FFQL expression.

List Spaces

Find Flatfile spaces by environment, text, namespace, archive state, or template status.

List Workbooks

List workbooks, optionally within a space, with their nested sheets so an agent can continue to sheet and record tasks.

Update Records

Update one or more existing Flatfile records by ID using simple field-key to value objects.

FRAMEWORKS

How to build Flatfile MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Flatfile MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Flatfile MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Flatfile MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Flatfile MCP with Claude Cowork

Codex logo

Codex

Use Flatfile MCP with Codex

OpenClaw logo

OpenClaw

Use Flatfile MCP with OpenClaw

Hermes logo

Hermes

Use Flatfile MCP with Hermes

CLI logo

CLI

Use Flatfile MCP with CLI

Google ADK logo

Google ADK

Use Flatfile MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Flatfile MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Flatfile MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Flatfile MCP with LlamaIndex

CrewAI logo

CrewAI

Use Flatfile MCP with CrewAI

FAQ

Frequently asked questions

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

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

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