How to integrate Lyzr MCP with LangChain

This guide walks you through connecting Lyzr to LangChain using the Composio tool router. By the end, you'll have a working Lyzr agent that can create ai agents, chat with your agents, schedule workflows, manage agent memory, and run ai checks through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Lyzr account through Composio's Lyzr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Lyzr is an AI agent platform for building, running, evaluating, and managing agents. It helps teams ship safer AI workflows with Studio services for memory, voice, simulations, and knowledge bases.

22 Tools

Introduction

This guide walks you through connecting Lyzr to LangChain using the Composio tool router. By the end, you'll have a working Lyzr agent that can create ai agents, chat with your agents, schedule workflows, manage agent memory, and run ai checks through natural language commands.

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

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

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TL;DR

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

The Lyzr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lyzr account. It provides structured and secure access to your agents, memory, knowledge bases, Responsible AI policies, simulations, schedules, workflows, and voice records, so your agent can create and manage agents, run conversations, search stored knowledge, review safety results, and monitor operations on your behalf.

  • Agent creation and conversations: Have your agent create single-task agents with chosen models and instructions, activate or deactivate them, run conversations, inspect their setup, or delete them.
  • Memory and knowledge retrieval: Let the agent store conversation messages, search memories across sessions, clear session history, inspect knowledge bases, and retrieve relevant documents.
  • Responsible AI and simulation review: Direct your agent to review available safety policies, evaluate text for blocks or redactions, and examine simulation performance and reported issues.
  • Schedules and workflow monitoring: Instruct your agent to create, review, or delete scheduled agent runs, then inspect SuperFlow workflows and their execution status.
  • Voice activity review: Have your agent list available voice agents and review call transcripts by agent, session, organization, or date range.

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 Lyzr 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 Lyzr 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: ['lyzr']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Lyzr 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 Lyzr tools as needed
8

Configure the agent with the MCP URL

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

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

Add Memory Messages

Store a batch of conversation messages in Lyzr Memory for an owner, session, and optional agent, with optional synchronous fact extraction.

Chat With Agent

Send a message or message history to an active Agent Studio agent and return its synchronous inference response for a session.

Clear Memory Session

Permanently clear messages and memories for one explicit owner and session in Lyzr Memory.

Create Agent

Create a single-task Agent Studio agent with an LLM provider, model, instructions, and optional tool configuration.

Create Agent Schedule

Create a cron schedule that runs one Agent Studio agent with a message in a specified timezone.

Delete Agent

Permanently delete one Agent Studio agent by ID.

Delete Agent Schedule

Permanently delete one agent cron schedule and remove its associated scheduler job.

Evaluate Text With RAI Policy

Apply one owned Responsible AI policy to text and return processed text, block decisions, warnings, detections, and redactions.

Get Agent

Return the current configuration and status of one Agent Studio agent by ID.

Get Knowledge Base

Return one Lyzr Knowledge Base configuration by its known RAG configuration ID.

Get Simulation Overview

Return aggregate Agent Simulation counts and average evaluation pass rate for the connected workspace.

List Agents

Return all Agent Studio agents owned by the connected API key, including IDs needed by lifecycle and inference tools.

List Agent Schedules

Return one page of agent cron schedules, optionally filtered by user, agent, or active state.

List Agent Simulation Issues

Return recent performance, error, and Responsible AI issues across monitored agents, optionally filtered by resolution and severity.

List RAI Policies

Return Responsible AI policies owned by the authenticated user, including policy IDs and enabled guardrail configuration.

List SuperFlow Executions

Return one page of SuperFlow execution records, optionally filtered by workflow and execution status.

List SuperFlow Workflows

Return all SuperFlow workflow definitions accessible to the connected API key, including workflow IDs and graph data.

List Voice Agents

Return saved Voice agents accessible to the connected organization, including IDs used to filter transcripts.

List Voice Transcripts

Return one page of accessible Voice call transcripts, optionally filtered by agent, session, organization, or date range.

Retrieve Knowledge

Retrieve relevant documents from a known Lyzr Knowledge Base using configurable basic, MMR, hypothetical-document, or time-aware retrieval.

Search Memories

Semantically search stored memories for one owner, within one session or across that owner's sessions, using Lyzr's hybrid vector and BM25 retrieval.

Set Agent Status

Activate or deactivate an Agent Studio agent.

FAQ

Frequently asked questions

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

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

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