How to integrate Parallel MCP with LangChain

This guide walks you through connecting Parallel to LangChain using the Composio tool router. By the end, you'll have a working Parallel agent that can find top articles on generative ai trends, summarize recent news about electric vehicles, batch search for competitors' product launches through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Parallel account through Composio's Parallel MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for free Get a demo

Parallel

Api Key

Parallel is a Task API for automated, structured web research and data extraction. It transforms natural language queries into precise, schema-driven outputs for streamlined workflows.

32 Tools

Managed auth

Connect Parallel without auth hassles

We manage OAuth, API keys, token refresh, and scopes — you just build.

Try for free

Introduction

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

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

Also integrate Parallel 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 Parallel project to Composio
Create a Tool Router MCP session for Parallel
Initialize an MCP client and retrieve Parallel tools
Build a LangChain agent that can interact with Parallel
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 Parallel MCP server, and what's possible with it?

The Parallel MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Parallel account. It provides structured and secure access to advanced web research automation, so your agent can perform actions like launching batch research tasks, running semantic searches, monitoring task progress, and generating research suggestions on your behalf.

Automated web research task creation: Instantly create structured research tasks or batch multiple queries for parallel execution, saving time and effort.
Semantic search across multiple topics: Direct your agent to run parallel semantic searches and retrieve top-matching documents or data for several queries at once.
Real-time task group monitoring: Let your agent stream live updates about the progress, completion, or status of ongoing research task groups.
Context-driven research suggestions: Have the agent suggest the next best research tasks based on your project or intent, keeping your workflow efficient and on track.
Task group retrieval and management: Fetch detailed information about specific research task groups to review results or track progress seamlessly.

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 Parallel 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 Parallel 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: ['parallel']
    }
);

const url = session.mcp.url;

What's happening:

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "parallel-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 Parallel MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Parallel 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 Parallel 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 Parallel 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: ['parallel']
        }
    );

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

Add Enrichment to FindAll Run

Tool to add an enrichment to a FindAll run.

Add Runs to Task Group

Tool to initiate multiple task runs within a TaskGroup.

Cancel FindAll Run

Tool to cancel an active FindAll run by findall_id.

Create Chat Completions

Tool to get realtime chat completions from Parallel AI.

Create Monitor

Tool to create a web monitor that periodically runs the specified query.

Create Task Group

Tool to create a new task group.

Create Task Run

Tool to create and initiate a task run.

Delete Monitor

Tool to delete a monitor, stopping all future executions.

Extend FindAll Run

Tool to extend a FindAll run by adding additional matches to the current match limit.

Extract Content from URLs

Tool to extract relevant content from specific web URLs.

Fetch Task Group Runs

Tool to retrieve task runs from a Task Group as a resumable stream.

Start FindAll Run

Tool to start a FindAll run.

Get FindAll Run Result

Tool to fetch the final (or latest available) FindAll candidates and result payload for a run.

Get FindAll Run Schema

Tool to retrieve the schema configuration of a FindAll run by findall_id.

Ingest FindAll Run

Tool to transform a natural language search objective into a structured FindAll specification.

List Monitor Events

Tool to list events for a monitor from up to the last 300 event groups.

List Monitors

Tool to list active monitors for the user.

Retrieve Event Group

Tool to retrieve an event group for a monitor.

Retrieve FindAll Run Status

Tool to retrieve status and metadata for a FindAll run by findall_id.

Retrieve Monitor

Tool to retrieve a specific monitor by ID.

Retrieve Task Group

Tool to retrieve details of a specific task group.

Retrieve Task Group Run

Tool to retrieve run status by run_id for a task group.

Retrieve Task Run

Tool to retrieve run status by run_id.

Retrieve Task Run Input

Tool to retrieve the input data of a specific task run by run_id.

Retrieve Task Run Result

Tool to retrieve the result of a task run by run_id, blocking until the run completes.

Parallel Search

Tool to perform parallel semantic search.

Simulate Event

Tool to simulate sending an event for a monitor.

Stream FindAll Events

Tool to stream events from a FindAll run.

Stream Task Group Events

Tool to stream events for a Task Group.

Stream Task Run Events

Tool to stream events for a Task Run.

Suggest Task

Tool to suggest tasks based on user intent.

Update Monitor

Tool to update a monitor's configuration.

FRAMEWORKS

How to build Parallel MCP Agent with another framework

OpenAI Agents SDK

Use Parallel MCP with OpenAI Agents SDK

Claude Agent SDK

Use Parallel MCP with Claude Agent SDK

Claude Code

Use Parallel MCP with Claude Code

Claude Cowork

Use Parallel MCP with Claude Cowork

Codex

Use Parallel MCP with Codex

OpenClaw

Use Parallel MCP with OpenClaw

Hermes

Use Parallel MCP with Hermes

CLI

Use Parallel MCP with CLI

Google ADK

Use Parallel MCP with Google ADK

Vercel AI SDK

Use Parallel MCP with Vercel AI SDK

Mastra AI

Use Parallel MCP with Mastra AI

LlamaIndex

Use Parallel MCP with LlamaIndex

CrewAI

Use Parallel MCP with CrewAI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Excel

Oauth2S2s Oauth2

Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.

21risk

Api Key

21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.

Abstract

Api Key

Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.

Addressfinder

Api Key

Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.

FAQ

Frequently asked questions

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

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

Start with Parallel.It takes 30 seconds.

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

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

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 Parallel MCP with LangChain

Connect Parallel without auth hassles

Introduction

Also integrate Parallel with

TL;DR

What is LangChain?

What is the Parallel 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 Parallel 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

Excel

21risk

Abstract

Addressfinder

Frequently asked questions

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

Can I use Tool Router MCP with LangChain?+

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

How safe is my data with Composio Tool Router?+

Start with Parallel.It takes 30 seconds.

Teams ship real agents on Composio