How to integrate Dovetail MCP with LangChain

This guide walks you through connecting Dovetail to LangChain using the Composio tool router. By the end, you'll have a working Dovetail agent that can summarize all data points for project x, create a new insight from interview notes, list every contact added this month through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Dovetail account through Composio's Dovetail MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.

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Introduction

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

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

Also integrate Dovetail with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The Dovetail MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Dovetail account. It provides structured and secure access to your research workspace, so your agent can perform actions like creating insights, managing contacts, organizing channels, and retrieving research notes on your behalf.

Automated insight creation: Let your agent synthesize findings and store new insights in your Dovetail projects, streamlining your research analysis workflow.
Channel and topic management: Easily create, organize, or delete channels and topics to keep your research data structured and accessible.
Contact management and retrieval: Automatically add new research contacts or list all contacts in your workspace for better respondent tracking.
Research note access: Ask your agent to fetch detailed information about specific notes, giving you instant access to key research materials.
Data point recording and classification: Capture and categorize new data points within channels, ensuring every piece of feedback or observation is logged and ready for analysis.

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:

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

Step-by-step Guide

Step by step10 STEPS

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

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

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

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 Dovetail functionality through MCP

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 Dovetail tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Dovetail 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 Dovetail tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "dovetail-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 Dovetail MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Dovetail tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

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 Dovetail 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

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 Dovetail 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: ['dovetail']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "dovetail-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 Dovetail 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 Dovetail 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 Dovetail action and event your agent gets out of the box.

Create Channel

Creates a new channel in Dovetail to organize and collect feedback data.

Create Contact

Tool to create a new contact in Dovetail.

Create Data

Tool to create a data item in a Dovetail project with text content, title, and/or structured fields.

Create Data Point

Tool to create a data point within a channel.

Create Doc

Tool to create a doc in a Dovetail project with text content, title and/or custom fields.

Create Insight

Creates a new insight in Dovetail to store synthesized research findings, observations, or conclusions.

Create Note

Tool to create a note in a Dovetail project with text content, title and/or custom fields.

Create Project

Tool to create a new project in your Dovetail workspace.

Create Topic

Tool to create a new topic in a Dovetail channel.

Delete Channel

Tool to delete an existing channel.

Delete Data

Tool to delete an existing data item.

Delete Doc

Tool to delete an existing doc.

Delete Insight

Tool to delete an existing insight.

Delete Note

Tool to delete an existing note.

Delete Topic

Tool to delete an existing topic.

Export Data

Tool to export data in HTML or Markdown format.

Export Doc

Tool to export a doc in HTML or Markdown format.

Export Insight

Tool to export an insight in HTML or Markdown format.

Export Note

Tool to export a note from Dovetail in HTML or Markdown format.

Get Contact

Tool to retrieve details of a specific contact.

Get Data

Tool to retrieve details of a specific data item by ID.

Get Doc

Tool to retrieve details of a specific doc by ID.

Get File

Tool to retrieve details of a specific file by its ID.

Get Folder

Tool to retrieve details of a specific folder.

Get Insight

Tool to retrieve details of a specific insight by ID.

Get Note

Tool to retrieve details of a specific note.

Get Project

Tool to retrieve details of a specific project.

Get Token Info

Retrieves information about the current API token, including its unique identifier and the associated workspace subdomain.

Import Data File

Tool to import a public URL of a file as new data in Dovetail.

Import Doc File

Tool to import a public file URL as a new doc in Dovetail.

Import Insight from File

Tool to import a file from a public URL as a new insight in Dovetail.

Import Note File

Tool to import a file from a public URL as a new note in Dovetail.

List Contacts

Retrieves a paginated list of contacts from a Dovetail workspace.

List Data

Tool to list data items in Dovetail.

List Docs

Tool to list docs in a Dovetail workspace with optional filtering, sorting, and pagination.

List Folders

Tool to get a list of folders associated with a workspace.

List Highlights

List highlights from your Dovetail workspace with optional filtering and pagination.

List Insights

Tool to get a list of insights associated with a workspace.

List Notes

List notes in Dovetail workspace with optional pagination and sorting.

List Projects

Tool to list all projects in Dovetail.

List Tags

List all tags in the authenticated Dovetail workspace.

List User Docs

Tool to get a list of docs associated with a user in Dovetail.

List User Insights

List personal insights for a user in Dovetail.

Magic Search

Tool to perform a magic search across workspace data.

Update Channel

Tool to update an existing channel's title or context.

Update Contact

Tool to update an existing contact in Dovetail.

Update Data

Tool to update a data item in Dovetail.

Update Doc

Tool to update a doc in Dovetail.

Update Insight

Updates an existing insight in Dovetail, allowing you to modify the title and custom fields.

Update Note

Tool to update an existing note in Dovetail.

Update Topic

Tool to update an existing topic.

FRAMEWORKS

How to build Dovetail MCP Agent with another framework

OpenAI Agents SDK

Use Dovetail MCP with OpenAI Agents SDK

Claude Agent SDK

Use Dovetail MCP with Claude Agent SDK

Claude Code

Use Dovetail MCP with Claude Code

Claude Cowork

Use Dovetail MCP with Claude Cowork

Codex

Use Dovetail MCP with Codex

OpenClaw

Use Dovetail MCP with OpenClaw

Hermes

Use Dovetail MCP with Hermes

CLI

Use Dovetail MCP with CLI

Google ADK

Use Dovetail MCP with Google ADK

Vercel AI SDK

Use Dovetail MCP with Vercel AI SDK

Mastra AI

Use Dovetail MCP with Mastra AI

LlamaIndex

Use Dovetail MCP with LlamaIndex

CrewAI

Use Dovetail MCP with CrewAI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Firecrawl

Api Key

Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.

Tavily

Api Key

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

Api Key

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

Api Key

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.

FAQ

Frequently asked questions

With a standalone Dovetail MCP server, the agents and LLMs can only access a fixed set of Dovetail tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Dovetail and many other apps based on the task at hand, all through a single MCP endpoint.

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 Dovetail tools.

Yes, absolutely. You can configure which Dovetail 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.

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 Dovetail data and credentials are handled as safely as possible.

Start with Dovetail.It takes 30 seconds.

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

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Dovetail MCP with LangChain

Connect Dovetail without auth hassles

Introduction

Also integrate Dovetail with

TL;DR

What is LangChain?

What is the Dovetail MCP server, and what's possible with it?

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Dovetail MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Firecrawl

Tavily

Exa

Serpapi

Frequently asked questions

What are the differences in Tool Router MCP and Dovetail MCP?+

Can I use Tool Router MCP with LangChain?+

Can I manage the permissions and scopes for Dovetail while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Dovetail.It takes 30 seconds.

Teams ship real agents on Composio