How to integrate Docsbot ai MCP with LangChain

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Introduction

This guide walks you through connecting Docsbot ai to LangChain using the Composio tool router. By the end, you'll have a working Docsbot ai agent that can list all bots for my team, generate support ticket from recent chat, update bot description to new branding, list unanswered questions for a bot through natural language commands.

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

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

TL;DR

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

The Docsbot ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docsbot ai account. It provides structured and secure access to your Docsbot ai bots, teams, and conversation data, so your agent can perform actions like creating bots, managing teams, generating support tickets, and analyzing user questions on your behalf.

  • Custom bot creation and management: Instantly create new Docsbot ai bots or update existing ones, letting your agent provision and configure bots for different documentation needs.
  • Team administration and overview: Allow your agent to fetch details about your teams or list all teams associated with your account, making it easier to manage collaboration and bot access.
  • Automated support ticket generation: Easily convert chatbot conversations into structured support tickets, so your agent can help streamline customer support and issue tracking.
  • Bot question and source analytics: Retrieve lists of questions asked to your bots or review all data sources connected to a given bot, empowering your agent to surface insights or monitor bot effectiveness.
  • Seamless bot and data cleanup: Direct your agent to delete bots or manage bot sources, helping you keep your Docsbot ai environment tidy and up to date.

Supported Tools & Triggers

Tools
Create BotTool to create a new bot within a team.
Delete BotTool to delete a specific bot by its id.
Generate Conversation TicketTool to generate a structured support ticket from a chat agent conversation.
Get Bot DetailsTool to fetch details of a specific bot by id within a team.
Get Team DetailsTool to fetch details of a specific team by its id.
List Team BotsTool to list all bots for a given team.
List QuestionsTool to list all questions asked of a specific bot.
List Bot SourcesTool to list all sources for a specific bot.
List TeamsTool to list all teams.
Update BotTool to update specific fields for a bot (e.
Update TeamTool to update specific fields for a team.
Upload File to Cloud StorageTool to upload a file to cloud storage via a presigned url.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

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()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Docsbot ai functionality through MCP

Initialize Composio client

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")
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 Docsbot ai tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Docsbot ai
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['docsbot_ai']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "docsbot_ai-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)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Docsbot ai MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Docsbot ai tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Docsbot ai 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")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Docsbot ai and LangChain:

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=['docsbot_ai']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "docsbot_ai-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 Docsbot ai 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())

Conclusion

You've successfully built a LangChain agent that can interact with Docsbot ai 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 Docsbot ai MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Docsbot ai MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Docsbot ai MCP with Claude Agent SDK

Google ADK

Google ADK

Use Docsbot ai MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Docsbot ai MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Docsbot ai MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Docsbot ai MCP with LlamaIndex

CrewAI

CrewAI

Use Docsbot ai MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Docsbot ai MCP?

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

Can I manage the permissions and scopes for Docsbot ai while using Tool Router?

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

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Agent.ai
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Context
ASU
Letta
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HubSpot
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Altera
DataStax
Entelligence
Rolai

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