# How to integrate Botsonic MCP with LangChain

```json
{
  "title": "How to integrate Botsonic MCP with LangChain",
  "toolkit": "Botsonic",
  "toolkit_slug": "botsonic",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/botsonic/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/botsonic/framework/langchain.md",
  "updated_at": "2026-05-12T10:03:47.192Z"
}
```

## Introduction

This guide walks you through connecting Botsonic to LangChain using the Composio tool router. By the end, you'll have a working Botsonic agent that can list all bots in your account, export all conversation threads for review, bulk upload website urls to train a bot through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Botsonic account through Composio's Botsonic MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Botsonic with

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

## TL;DR

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

The Botsonic MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Botsonic account. It provides structured and secure access to your chatbots, enabling your agent to manage bots, upload training data, oversee conversations, and handle FAQs with ease.
- Bulk training data uploads: Effortlessly upload multiple URLs or documents to your bot for rapid and comprehensive training updates.
- Bot management and retrieval: Instantly list all your bots, fetch detailed data, or export entire bot assets for backup or review.
- Conversation analytics and monitoring: Retrieve all conversations related to any bot—perfect for analyzing user interactions or tracking support queries.
- FAQ and starter question management: List, update, or remove FAQ entries and starter questions to keep your chatbot responses relevant and up to date.
- Data and file cleanup: Direct your agent to delete outdated files or bot data, ensuring your chatbot remains efficient and well-organized.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BOTSONIC_BULK_UPLOAD_URLS` | Bulk Upload Bot URLs | Tool to bulk upload URLs for bot training. Use when you need to upsert multiple document URLs into a bot in one request. |
| `BOTSONIC_CREATE_STARTER_QUESTION` | Create Starter Question | Tool to create a new starter question for the bot. Use when you need to add a preset question-answer pair that users can quickly select. |
| `BOTSONIC_DELETE_STARTER_QUESTION` | Delete Starter Question | Deletes a specific starter question from a bot using its unique identifier. This operation is destructive and permanent. First retrieve available starter questions using the Get All Starter Questions action to obtain valid IDs before deletion. |
| `BOTSONIC_DELETE_UPLOADED_FILE` | Delete Uploaded File | Delete a specific uploaded file/bot data entry by its unique identifier. This permanently removes the file from the bot's training data. Use this action when you need to clean up outdated content, remove incorrect data, or manage bot training materials. The file ID can be obtained using the Get All Bot Data action. Returns the full deleted object details including metadata, status, and timestamps. |
| `BOTSONIC_GET_ALL_BOT_DATA` | Get All Bot Data | Tool to retrieve all data associated with the bot, including files and resources. Use when you need a comprehensive export of bot assets for backup or inspection. |
| `BOTSONIC_GET_ALL_BOTS` | Get All Bots | Retrieve all bots associated with your account. Supports pagination, search, and sorting to efficiently manage and query bot configurations. Use this when you need to list, search, or filter existing bots. |
| `BOTSONIC_GET_ALL_CONVERSATIONS` | Get All Conversations | Tool to retrieve all conversations related to the bot. Use after authentication when you need a paginated list of conversation threads for review or analytics. |
| `BOTSONIC_GET_ALL_CONVERSATIONS_WITH_SOURCE` | Get All Conversations With Source | Tool to retrieve all conversations with source information. Use when you need detailed conversation data including source tracking, user form data, and comprehensive metadata for analytics or conversation management. |
| `BOTSONIC_GET_ALL_FAQS` | Get All FAQs | Retrieve all frequently asked questions (FAQs) associated with your bot in paginated format. Returns a list of FAQ entries with their questions, answers, status, and metadata. Supports filtering by search query, sorting by various attributes, and pagination controls. Use this when you need to list, review, or manage bot FAQ entries. |
| `BOTSONIC_GET_ALL_STARTER_PRESETS` | Get All Starter Presets | Tool to retrieve all starter presets for a bot by bot ID. Use when you need to fetch the bot's welcome message and starter questions configured for user interactions. |
| `BOTSONIC_GET_ALL_STARTER_QUESTIONS` | Get All Starter Questions | Tool to retrieve all starter questions. Use after authenticating when you need to list the bot’s opening prompts. |
| `BOTSONIC_UPDATE_STARTER_QUESTION` | Update Starter Question | Tool to update an existing starter question by its unique identifier. Use after confirming the ID and desired updates. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

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- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
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## Frequently Asked Questions

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

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)