# How to integrate Brandfetch MCP with LangChain

```json
{
  "title": "How to integrate Brandfetch MCP with LangChain",
  "toolkit": "Brandfetch",
  "toolkit_slug": "brandfetch",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/brandfetch/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/brandfetch/framework/langchain.md",
  "updated_at": "2026-05-12T10:03:59.051Z"
}
```

## Introduction

This guide walks you through connecting Brandfetch to LangChain using the Composio tool router. By the end, you'll have a working Brandfetch agent that can get the official logo for apple inc, list brand colors used by starbucks, find company info for nike by domain through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Brandfetch account through Composio's Brandfetch MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Brandfetch with

- [OpenAI Agents SDK](https://composio.dev/toolkits/brandfetch/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/brandfetch/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/brandfetch/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/brandfetch/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/brandfetch/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/brandfetch/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/brandfetch/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/brandfetch/framework/cli)
- [Google ADK](https://composio.dev/toolkits/brandfetch/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/brandfetch/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/brandfetch/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/brandfetch/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/brandfetch/framework/crew-ai)

## TL;DR

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

The Brandfetch MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Brandfetch account. It provides structured and secure access to company logos, brand colors, and comprehensive brand assets, so your agent can perform actions like fetching brand information, identifying merchants, retrieving brand logos, and searching brands on your behalf.
- Fetch complete brand profiles: Instantly retrieve logos, color palettes, fonts, and visual identity details for any brand using domain, brand ID, ISIN, or ticker symbol.
- Get company firmographic data: Let your agent pull in-depth company information, including industry and organization details, for any brand identifier.
- Merchant identification from transactions: Seamlessly map credit card transaction labels or raw payment descriptions to merchant brands and enrich transaction data with brand assets.
- Retrieve and customize brand logos: Fetch high-quality and up-to-date brand logos, icons, or symbols in light or dark themes and in various dimensions.
- Search and match brands by name: Enable your agent to autocomplete and match brand names to their official URLs and icons, perfect for enriching user experiences or directories.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BRANDFETCH_GET_BRAND_INFO` | Get Brand Information | Retrieves brand information including logos, colors, fonts, and company details using a domain, Brand ID, ISIN, or stock ticker. Logo data may be absent for some domains — do not assume logos are always returned. The response includes multiple logo types (e.g., icon, logo) and themes; explicitly select the desired type and size rather than defaulting to the first URL. |
| `BRANDFETCH_GET_GRAPHQL_VERSION` | Get GraphQL API Version | Tool to retrieve the Brandfetch GraphQL API version. Use when you need to check the current API version via the GraphQL endpoint. |
| `BRANDFETCH_GET_TAXONOMY` | Get Brandfetch Taxonomy | Tool to retrieve Brandfetch's taxonomy via GraphQL API. Use this to get a complete list of industries, countries, and geographic regions used in Brandfetch's classification system. The taxonomy includes hierarchical industry data with parent-child relationships. |
| `BRANDFETCH_GET_TRANSACTION_INFO` | Get Transaction Info | This tool converts payment transaction labels into detailed merchant brand information. It takes a transaction label (like what you see on your credit card statement) and returns comprehensive brand data (including logos, colors, fonts, and company information). It is useful for identifying merchants and enriching transaction data with detailed brand information. |
| `BRANDFETCH_LIST_SUBSCRIBABLE_EVENTS` | List Subscribable Events | Tool to retrieve all available webhook event types that can be subscribed to via the Brandfetch GraphQL API. Returns event names and descriptions for webhook configuration. Available events include brand.claimed, brand.deleted, brand.updated, brand.company.updated, and brand.verified. |
| `BRANDFETCH_LIST_WEBHOOKS` | List Webhooks | Tool to retrieve a list of all webhooks via GraphQL API. Use when you need to query webhook configurations and their statuses in the Brandfetch system. |
| `BRANDFETCH_SEARCH_BRANDS` | Search Brands | Searches for brands by name and returns matching brand information including URLs and icons, enabling rich autocomplete experiences. Use this tool first to resolve a vague name or ticker to a precise domain or brandId before calling BRANDFETCH_GET_BRAND_INFO or BRANDFETCH_GET_LOGO. Results may include multiple candidates; disambiguate using the domain, geography, qualityScore, and verified fields rather than defaulting to the first result. Returns empty results for new or niche brands. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Brandfetch MCP server is an implementation of the Model Context Protocol that connects your AI agent to Brandfetch. It provides structured and secure access so your agent can perform Brandfetch operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Brandfetch tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Brandfetch 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 Brandfetch tools as needed
```python
# Create Tool Router session for Brandfetch
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['brandfetch']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['brandfetch']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "brandfetch-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)
```

```typescript
const client = new MultiServerMCPClient({
    "brandfetch-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Brandfetch 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Brandfetch 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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=['brandfetch']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "brandfetch-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 Brandfetch 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())
```

```typescript
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: ['brandfetch']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "brandfetch-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 Brandfetch 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 Brandfetch 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 Brandfetch MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/brandfetch/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/brandfetch/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/brandfetch/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/brandfetch/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/brandfetch/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/brandfetch/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/brandfetch/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/brandfetch/framework/cli)
- [Google ADK](https://composio.dev/toolkits/brandfetch/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/brandfetch/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/brandfetch/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/brandfetch/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/brandfetch/framework/crew-ai)

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- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.
- [Doppler marketing automation](https://composio.dev/toolkits/doppler_marketing_automation) - Doppler marketing automation is a platform for creating, sending, and tracking email campaigns. It helps you automate marketing workflows and manage subscriber lists for better engagement.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Brandfetch MCP?

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

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

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

---
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