How to integrate Lyzr MCP with Autogen

This guide walks you through connecting Lyzr to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Lyzr
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Lyzr tools
  • Run a live chat loop where you ask the agent to perform Lyzr operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Lyzr account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Lyzr via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Lyzr connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Lyzr session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["lyzr"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Lyzr tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Lyzr assistant agent with MCP tools
    agent = AssistantAgent(
        name="lyzr_assistant",
        description="An AI assistant that helps with Lyzr operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Lyzr tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Lyzr related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Lyzr tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Lyzr session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["lyzr"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Lyzr assistant agent with MCP tools
        agent = AssistantAgent(
            name="lyzr_assistant",
            description="An AI assistant that helps with Lyzr operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Lyzr related question or task to the agent.\n")

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Lyzr through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Lyzr, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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. Autogen 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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