How to integrate Gigasheet MCP with LangChain

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Introduction

This guide walks you through connecting Gigasheet to LangChain using the Composio tool router. By the end, you'll have a working Gigasheet agent that can list all columns in my sales dataset, download export url for last week's data, apply saved filter to monthly report sheet, show all filter templates in my workspace through natural language commands.

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

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

TL;DR

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

The Gigasheet MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gigasheet account. It provides structured and secure access to your big data spreadsheets, so your agent can perform actions like retrieving datasets, applying filters, exporting data, managing sheets, and integrating with connector sources on your behalf.

  • Dataset retrieval and inspection: Instantly fetch metadata or details for any dataset or sheet, such as column names, types, and structure, so you can quickly understand and analyze your data.
  • Automated data export and download: Direct your agent to initiate data exports and retrieve download links for processed datasets, streamlining big data extraction directly to your tools or workflows.
  • Smart filtering and template application: Apply saved filter templates to sheets or retrieve available filter templates, enabling rapid, repeatable data curation without manual setup.
  • Sheet and folder management: Effortlessly delete sheets or folders—including recursive deletions—so you can keep your workspace organized and clutter-free.
  • Connector and integration management: List and manage connector connections to keep all your external data sources in sync with Gigasheet, making data aggregation seamless and automated.

Supported Tools & Triggers

Tools
Delete sheet or folder by handleTool to delete a sheet or folder by handle.
Get Client State Current VersionTool to fetch the current client-state version metadata for a sheet.
Get Connector ConnectionsTool to list connector connections.
Get Dataset by HandleTool to get dataset metadata.
Get Dataset ColumnsTool to list all column metadata (IDs, names, types) for a dataset.
Get Dataset Export Download URLTool to retrieve the download URL for an exported dataset.
Get Dataset ViewsTool to list all views associated with a specific dataset.
Get Docs Formulas FunctionsTool to retrieve all supported formula functions.
Apply Filter Template On SheetTool to fetch a saved filter template's model for a given sheet.
Get Filter TemplatesTool to retrieve all saved filter templates.
Generate New HandleTool to generate a new unique dataset handle.
Get User Autofill InfoTool to fetch autofill info for the authenticated user.
Get Authenticated User InfoTool to fetch the authenticated user's details.
Append Rows to Sheet by NameTool to append rows to a sheet by column names.
Initiate Dataset ExportTool to initiate an export of a dataset.
Insert Blank Row in DatasetTool to insert a blank row with null values into a dataset.
Rename Columns to UniqueTool to rename all columns in a dataset to unique names.
Save Current ViewTool to persist the current view in a Gigasheet dataset.
Get Filtered Row IndexTool to retrieve the filtered-set row index for a given unfiltered row number.
Combine Files by NameTool to combine multiple files by a shared column name.
Export Gigasheet to S3Tool to export Gigasheet data to AWS S3.
Import from S3Tool to import data from AWS S3 into your Gigasheet Library.
Request API AccessTool to request access to the Gigasheet API.
Unroll Delimited ColumnTool to explode a column containing delimited data into multiple rows.
Upload from URLTool to upload data to Gigasheet from a specified URL.
Set Dataset Client State VersionTool to set the client state version of a dataset.
Update cell by column name and rowTool to update a cell in a dataset by specifying column name and row number.
Share fileTool to share a Gigasheet file with specified recipients.
Create/Update Filter TemplateTool to create or update a saved filter template.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv 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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Gigasheet functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Gigasheet tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Gigasheet
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['gigasheet']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "gigasheet-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Gigasheet MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Gigasheet tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Gigasheet related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Gigasheet and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['gigasheet']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "gigasheet-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Gigasheet related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Gigasheet 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.

How to build Gigasheet MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Gigasheet MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Gigasheet MCP with Claude Agent SDK

Google ADK

Google ADK

Use Gigasheet MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Gigasheet MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Gigasheet MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Gigasheet MCP with LlamaIndex

CrewAI

CrewAI

Use Gigasheet MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Gigasheet MCP?

With a standalone Gigasheet MCP server, the agents and LLMs can only access a fixed set of Gigasheet tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Gigasheet and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

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 Gigasheet tools.

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

Yes, absolutely. You can configure which Gigasheet 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.

How safe is my data with Composio Tool Router?

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 Gigasheet data and credentials are handled as safely as possible.

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