SIGN UPLOG IN
X (Twitter)LinkedInGitHubDiscordYouTube

How to integrate Docsbot ai MCP with LangChain

Connect LangChain to Docsbot ai MCP. List all bots for your team, generate support ticket from recent chat, and more using natural language, with authentication handled for you.

Get started for freeGet a demo
Docsbot ai logoDocsbot ai
Api Key

Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.

38 Tools

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 create and update bots, add training sources, search bot content, review conversations and leads, and track bot statistics 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.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Docsbot ai with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

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.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step10 STEPS
1

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
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

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();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Docsbot ai functionality through MCP
6

Initialize Composio client

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()
    });
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
7

Create a Tool Router session

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

const 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
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "docsbot_ai-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 });
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
  • getTools() retrieves all available Docsbot ai tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Docsbot ai 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);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

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: ['docsbot_ai']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "docsbot_ai-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 Docsbot ai 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 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.
TOOLS

Supported Tools

Every Docsbot ai action and event your agent gets out of the box.

Capture Conversation Lead

Tool to capture lead information by updating conversation metadata and saving the lead.

Create Bot

Tool to create a new bot within a team.

Create Bot Source

Tool to create a new source for a bot.

Create Webhook

Tool to create a new webhook subscription for a bot.

Delete Bot

Tool to delete a specific bot by its ID.

Delete Conversation

Tool to delete a specific conversation by its ID.

Delete Lead

Tool to delete a specific lead by ID.

Delete Question

Tool to delete a specific question from history.

Delete Source

Tool to delete a specific source from a bot by its ID.

Delete Webhook

Tool to delete a webhook (unsubscribe) by its ID.

Generate Conversation Ticket

Generates a structured support ticket from a Chat Agent conversation.

Get Bot Details

Tool to fetch details of a specific bot by ID within a team.

Get Bot Monthly Reports

Tool to retrieve monthly statistical reports for a bot.

Get Bot Statistics

Tool to retrieve comprehensive statistics and analytics for a bot over a time period or date range.

Get Source Details

Tool to retrieve detailed information about a specific source by its ID.

Get Team Details

Tool to fetch details of a specific team by its ID.

Get Upload URL

Get a presigned upload URL for uploading files as sources.

Get Webhook Details

Tool to retrieve details of a specific webhook by ID.

List Team Bots

List all bots for a given team.

List Bot Conversations

Tool to list conversation history for a bot with pagination.

List Bot Leads

Tool to list captured leads for a bot with pagination and date filtering.

List Questions

Tool to list all questions asked of a specific bot.

List Research Jobs

Tool to list all deep research jobs for a bot with pagination support.

List Bot Sources

Retrieves a paginated list of all sources for a specific bot within a team.

List Team Members

Tool to list all members of a team including their roles.

List Teams

Tool to list all teams.

List Bot Webhooks

List all registered webhooks for a bot.

Rate Answer

Tool to rate an answer from chat APIs as positive (1), neutral (0), or negative (-1).

Refresh Source

Tool to refresh a source to re-index its content.

Semantic Search Bot Content

Tool to perform semantic search on a bot's indexed content.

Test Escalated Webhook

Tool to trigger a test delivery of the conversation.

Test Lead Webhook

Tool to trigger a test lead webhook delivery.

Test Research Webhook

Tool to trigger a deep research webhook delivery test.

Trigger Rated Webhook Test

Tool to trigger a conversation.

Update Bot

Update a bot's configuration settings such as name, description, model, temperature, and appearance.

Update Team

Tool to update specific fields for a team.

Update Webhook

Update a bot webhook's target URL, subscribed events, label, or expiration date.

Upload File to Cloud Storage

Upload a file to cloud storage via a presigned URL.

FRAMEWORKS

How to build Docsbot ai MCP Agent with another framework

Claude Code logo

Claude Code

Use Docsbot ai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Docsbot ai MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Docsbot ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Docsbot ai MCP with Claude Agent SDK

Codex logo

Codex

Use Docsbot ai MCP with Codex

OpenClaw logo

OpenClaw

Use Docsbot ai MCP with OpenClaw

Hermes logo

Hermes

Use Docsbot ai MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Docsbot ai MCP with Atomic Agent

Google ADK logo

Google ADK

Use Docsbot ai MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Docsbot ai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Docsbot ai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Docsbot ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Docsbot ai MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Docsbot ai MCP with Pydantic AI

AutoGen logo

AutoGen

Use Docsbot ai MCP with AutoGen

FAQ

Frequently asked questions

Start with Docsbot ai.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Docsbot ai tool your agent needs.Free to start.

Start building