How to integrate Lyzr MCP with CrewAI

This guide walks you through connecting Lyzr to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 a Composio API key and configure your Lyzr connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Lyzr
  • Build a conversational loop where your agent can execute Lyzr operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Lyzr connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Lyzr via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Lyzr MCP URL
6

Create a Composio Tool Router session for Lyzr

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["lyzr"])

url = session.mcp.url
What's happening:
  • You create a Lyzr only session through Composio
  • Composio returns an MCP HTTP URL that exposes Lyzr tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Lyzr and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["lyzr"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Lyzr through Composio's Tool Router. The agent can perform Lyzr operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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. CrewAI 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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