How to integrate Figma MCP with LangChain

This guide walks you through connecting Figma to LangChain using the Composio tool router. By the end, you'll have a working Figma agent that can add a comment to this figma file, convert design tokens to tailwind css, delete a reaction from a comment through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Figma account through Composio's Figma 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 Figma to LangChain using the Composio tool router. By the end, you'll have a working Figma agent that can add a comment to this figma file, convert design tokens to tailwind css, delete a reaction from a comment through natural language commands.

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

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

Also integrate Figma with

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TL;DR

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

The Figma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Figma account. It provides structured and secure access to your Figma workspace, so your agent can perform actions like commenting on designs, managing design tokens, linking developer resources, and automating collaboration workflows on your behalf.

  • Automated commenting and feedback loops: Have your agent add, reply to, or delete comments on Figma files and branches to streamline design reviews and team discussions.
  • Design token management and conversion: Let the agent extract, update, or convert design tokens in your files, including generating Tailwind CSS configurations for seamless dev handoff.
  • Developer resource integration: Automatically attach, update, or remove dev resources linked to Figma nodes, bridging the gap between design and development with contextual documentation or code references.
  • Webhook setup and automation: Enable your agent to create or delete webhooks for team events, making it easy to trigger notifications or workflows based on design activity.
  • Collaborative variable management: Empower the agent to batch-create, modify, or delete variables, collections, and modes across your design system, keeping everything consistent and up to date.

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 Figma 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 Figma 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: ['figma']
    }
);

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

Configure the agent with the MCP URL

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

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

Add a comment to a file

Posts a new comment to a Figma file or branch, optionally replying to an existing root comment (replies cannot be nested); `region_height` and `region_width` in `client_meta` must be positive if defining a comment region.

Add a reaction to a comment

Posts a specified emoji reaction to an existing comment in a Figma file or branch, requiring valid file_key and comment_id.

Create a webhook

Creates a Figma webhook to receive POST notifications when specific events occur.

Create dev resources

Creates and attaches multiple uniquely-URLed development resources to specified Figma nodes, up to 10 per node.

Create, modify, or delete variables

Manages variables, collections, modes, and their values in a Figma file via batch create/update/delete operations; use temporary IDs to link new related items in one request and ensure `variableModeValues` match the target variable's `resolvedType`.

Delete a comment

Deletes a specific comment from a Figma file or branch, provided the authenticated user is the original author of the comment.

Delete a reaction

Deletes a specific emoji reaction from a comment in a Figma file; the user must have originally created the reaction.

Delete a webhook

Permanently deletes an existing webhook, identified by its unique `webhook_id`; this operation is irreversible.

Delete dev resource

Deletes a development resource (used to link Figma design elements to external developer information like code or tasks) from a specified Figma file.

Design tokens to tailwind

Convert design tokens to Tailwind CSS configuration.

Detect Background Layers

Detect background layers for selected nodes in a Figma file.

Discover Figma Resources

Smart Figma resource discovery - extract IDs from any Figma URL.

Download Figma Images

Download images from Figma file nodes.

Extract design tokens

Extract design tokens from Figma files by combining styles, variables, and node-extracted values.

Extract Prototype Interactions

Extract prototype interactions and animations from Figma files.

Get activity logs

Retrieves activity log events from Figma, allowing filtering by event types, time range, and pagination.

Get a webhook

Retrieves detailed information about a specific webhook by its ID, provided the webhook exists and is accessible to the user.

Get comments in a file

Retrieves all comments from an existing Figma file, identified by a valid `file_key`, returning details like content, author, position, and reactions, with an option for Markdown formatted content.

Get component

Fetches metadata for a specific component using its unique identifier.

Get component set

Retrieves detailed metadata for a specific published Figma component set using its unique `key`.

Get current user

Retrieves detailed information for the currently authenticated Figma user.

Get dev resources

Retrieves development resources (e.

Get file components

Retrieves published components from a Figma file, which must be a main file (not a branch) acting as a library.

Get file component sets

Retrieves all published component sets from the specified Figma main file (file_key must not be for a branch).

Get file json

Get Figma Design file data with automatic simplification.

Get file metadata

Get Figma file metadata including name, creator, last modification details, thumbnail, and access information.

Get file nodes

Fetch JSON for specific node IDs from a Figma file to avoid full-file payload limits.

Get files in a project

Fetches a list of files in a Figma project, optionally including branch metadata.

Get file styles

Retrieves a list of published styles (like colors, text attributes, effects, and layout grids) from a specified main Figma file (not a branch).

Get image fills

Retrieves temporary (14-day expiry) download URLs for all image fills in a Figma file; requires `imageRef` from `Paint` objects to map URLs.

Get library analytics component action data

Retrieves component insertion and detachment analytics for a specified Figma library, groupable by 'component' or 'team' and filterable by a date range (YYYY-MM-DD).

Get library analytics component usage data

Retrieves component usage analytics for a specified Figma library file (identified by `file_key`), with data groupable by 'component' or 'file'.

Get library analytics style action data

Retrieves style usage analytics (insertions, detachments) for a Figma library, grouped by 'style' or 'team'; if providing a date range, ensure end_date is not before start_date.

Get library analytics style usage data

Retrieves style usage analytics for a published Figma library.

Get library analytics variable action data

Retrieves weekly, paginated analytics data on variable insertions and detachments for a specified Figma library (identified by `file_key`), groupable by 'variable' or 'team', and filterable by an optional date range.

Get library analytics variable usage data

Retrieves paginated analytics data on variable usage from a specified Figma library, grouped by 'file' or 'variable', for libraries with enabled analytics.

Get local variables

Retrieves all local/remote variables for a Figma file/branch; crucial for obtaining mode-specific values which `/v1/files/{file_key}/variables/published` omits.

Get payments

Retrieves a user's payment information for a Figma plugin, widget, or Community file; the authenticated identity must own the resource.

Get projects in a team

Retrieves projects within a specified Figma team that are visible to the authenticated user.

Get published variables

Retrieves variables published from a specified Figma file; this API is available only to full members of Enterprise organizations.

Get reactions for a comment

Retrieves reactions for a specific comment in a Figma file.

Get SCIM service provider config

Get Figma's SCIM service provider configuration.

Get style

Retrieves detailed metadata for a specific style in Figma using its unique style key.

Get team components

Retrieves components published in a specific Figma team's library; the team must have published components, otherwise an empty list is returned.

Get team component sets

Retrieves a paginated list of published component sets (collections of reusable UI elements) from a specified Figma team's library.

Get team styles

Retrieves a paginated list of published styles (fill colors, text styles, effects, grids) from a specified Figma team's library.

Get webhooks

Retrieves all webhooks registered for a specified Figma context (team, project, or file).

Get versions of a file

Retrieves the version history for a Figma file or branch, as specified by its `file_key`.

Get webhook requests

Retrieves a history of webhook requests for a specific Figma webhook subscription; data is available for requests sent within the last seven days.

Render images of file nodes

Render Figma nodes as images (PNG, JPG, SVG, PDF).

Update a webhook

Updates an existing Figma webhook, identified by `webhook_id`, allowing modification of its event type, endpoint, passcode, status, or description.

Update dev resources

Updates the name and/or URL of one or more existing Figma Dev Resources, each identified by its unique `id`.

FRAMEWORKS

How to build Figma MCP Agent with another framework

ChatGPT logo

ChatGPT

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Claude Code logo

Claude Code

Use Figma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Figma MCP with Claude Cowork

Codex logo

Codex

Use Figma MCP with Codex

Cursor logo

Cursor

Use Figma MCP with Cursor

OpenClaw logo

OpenClaw

Use Figma MCP with OpenClaw

Hermes logo

Hermes

Use Figma MCP with Hermes

CLI logo

CLI

Use Figma MCP with CLI

Google ADK logo

Google ADK

Use Figma MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Figma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Figma MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Figma MCP with LlamaIndex

CrewAI logo

CrewAI

Use Figma MCP with CrewAI

OpenCode logo

OpenCode

Use Figma MCP with OpenCode

VS Code logo

VS Code

Use Figma MCP with VS Code

FAQ

Frequently asked questions

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

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

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
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.

Abhi Arya
Abhi Arya
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.

Nirman Dave
Nirman Dave
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.

Ryan Yu
Ryan Yu
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.

Tomisin Jenrola
Tomisin Jenrola
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.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies