# How to integrate Datagma MCP with LangChain

```json
{
  "title": "How to integrate Datagma MCP with LangChain",
  "toolkit": "Datagma",
  "toolkit_slug": "datagma",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/datagma/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/datagma/framework/langchain.md",
  "updated_at": "2026-05-12T10:08:20.171Z"
}
```

## Introduction

This guide walks you through connecting Datagma to LangChain using the Composio tool router. By the end, you'll have a working Datagma agent that can identify top competitors in your industry, find recent market trends for saas, analyze growth opportunities in fintech through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Datagma account through Composio's Datagma MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Datagma with

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

## TL;DR

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

The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Datagma account. It provides structured and secure access to your Datagma data intelligence platform, so your agent can perform actions like uncovering market insights, tracking competitor activities, analyzing industry trends, and supporting strategic growth decisions on your behalf.
- In-depth market insights extraction: Enable your agent to gather and analyze real-time market data to identify emerging opportunities and potential threats.
- Competitor metrics tracking: Let your agent monitor competitor performance, product launches, and strategic moves for sharper benchmarking.
- Growth opportunity identification: Task your agent with surfacing new business prospects and growth areas using Datagma's data intelligence resources.
- Customized analytics reporting: Have your agent generate tailored reports and dashboards that summarize key metrics and actionable insights.
- Trend and pattern analysis: Empower your agent to spot industry trends, shifts in customer behavior, and evolving market dynamics for proactive strategy planning.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `DATAGMA_DETECT_JOB_CHANGE` | Detect Job Change | Tool to detect if a contact changed jobs. Use when verifying a contact’s current employment details by email. |
| `DATAGMA_ENRICH_PERSON_OR_COMPANY` | Enrich Person or Company | Enrich person or company data using LinkedIn URLs, emails, domains, or names. Returns enriched data including: contact information, LinkedIn profiles, company details, work experience, education, phone numbers (with phoneFull), and company metrics. Input types: LinkedIn profile URL (~100% success), email (~60% success), name+company (~90% success), company domain/name, or SIREN number (French companies). |
| `DATAGMA_FIND_WORK_EMAIL` | Find Work Email | Find verified work email address for a person using their name and company. Returns a professionally verified email address with validation metadata including SMTP checks and MX records. Requires either fullName or firstName+lastName, plus company domain or LinkedIn company slug. |
| `DATAGMA_GET_CREDITS` | Get Credits | Get the current credit balance for the authenticated Datagma API account. Use this to check how many API credits remain before making enrichment calls. |
| `DATAGMA_GET_TWITTER_BY_EMAIL` | Get Twitter Profile By Email | Retrieve Twitter account information associated with an email address. This action looks up Twitter username and display name for a given email address using Datagma's enrichment database. Returns Twitter username, display name, and the queried email if a match is found, or status 'NOT_FOUND' if no Twitter account is associated with the email. Use this when you need to: - Find someone's Twitter handle from their email address - Verify if an email has an associated Twitter account - Enrich contact data with social media information |
| `DATAGMA_GET_TWITTER_BY_USERNAME` | Get Twitter Profile by Username | Enrich Twitter profile data using Datagma's database. Returns contact information (email), social media profiles (LinkedIn, Facebook, GitHub), and professional details (skills, interests, industry) associated with a Twitter username. Note: Not all usernames are in Datagma's database. A 'not found' response (code 5) indicates the username hasn't been indexed yet. |
| `DATAGMA_REVERSE_PHONE_LOOKUP` | Reverse Phone Lookup | Tool to reverse-lookup information associated with a phone number. Use when you have a phone number and need associated details (e.g., carrier, location). |
| `DATAGMA_SEARCH_PHONE_NUMBERS` | Search Phone Numbers | Find mobile phone numbers using email address and/or LinkedIn profile URL. Returns list of phone numbers with confidence scores and optional WhatsApp verification. Best results when both email and LinkedIn URL are provided. |

## Supported Triggers

None listed.

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

The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agent to Datagma. It provides structured and secure access so your agent can perform Datagma 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 Datagma 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 Datagma 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 Datagma tools as needed
```python
# Create Tool Router session for Datagma
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['datagma']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "datagma-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({
    "datagma-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 Datagma 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 Datagma 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=['datagma']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "datagma-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 Datagma 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: ['datagma']
        }
    );

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

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

## Related Toolkits

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- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
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- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.
- [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly.

## Frequently Asked Questions

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

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

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

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

---
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