# How to integrate Ragic MCP with LangChain

```json
{
  "title": "How to integrate Ragic MCP with LangChain",
  "toolkit": "Ragic",
  "toolkit_slug": "ragic",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/ragic/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/ragic/framework/langchain.md",
  "updated_at": "2026-05-12T10:23:18.149Z"
}
```

## Introduction

This guide walks you through connecting Ragic to LangChain using the Composio tool router. By the end, you'll have a working Ragic agent that can find all customer records updated today, search invoices with status pending payment, list project tasks assigned to john doe through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Ragic account through Composio's Ragic MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Ragic with

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

## TL;DR

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

The Ragic MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ragic account. It provides structured and secure access to your Ragic databases, enabling your agent to search, filter, and analyze records just like you would on the Ragic platform.
- Powerful record search: Instruct your agent to quickly find specific database records using keywords, fields, or filters across your Ragic tables.
- Automated data analysis: Let your agent scan through records to extract insights, summarize trends, or identify outliers without manual effort.
- Custom query execution: Have the agent run tailored searches to surface exactly the information you need from your custom Ragic forms and sheets.
- Real-time database lookup: Ask your agent to instantly retrieve up-to-date details from any Ragic database for reporting, decision-making, or workflow automation.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RAGIC_DELETE_ENTRY` | Delete Entry | Tool to delete an entry from a Ragic sheet. Use when you need to remove a specific record from a sheet by its record ID. |
| `RAGIC_DOWNLOAD_FILE` | Download File | Tool to download uploaded files, images, or email attachments from Ragic. Use the file reference returned from API responses (e.g., from record fields). |
| `RAGIC_GET_ACTION_BUTTONS` | Get Action Buttons | Tool to retrieve the list of available action buttons on a Ragic sheet. Returns action button IDs and names. Use category=massOperation to get buttons available for mass operations. |
| `RAGIC_GET_CUSTOM_PRINT_REPORT` | Get Custom Print Report | Tool to retrieve a custom print report for a Ragic record in various formats (pdf, png, docx). Use when you need to generate and download a formatted report using a specific template. |
| `RAGIC_GET_ENTRY` | Get Entry | Tool to retrieve a single entry from a Ragic sheet by its record ID. Use when you need to fetch specific record data including field values and optional subtable data. |
| `RAGIC_GET_RECORD_AS_PDF` | Get Record as PDF | Tool to retrieve a PDF version of a Ragic record. Use when you need to export or download a record in PDF format. The PDF will contain the full record data in a formatted document. |
| `RAGIC_SEARCH_RECORDS` | Search Records | Search Records |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

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- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
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- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

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

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

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

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

---
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