How to integrate Lusha MCP with Pydantic AI

This guide walks you through connecting Lusha to Pydantic AI using the Composio tool router. By the end, you'll have a working Lusha agent that can enrich prospects from target company list, find verified emails for sales leads, get phone numbers for decision-makers through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Lusha account through Composio's Lusha MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for freeGet a demo
Lusha logoLusha
Api Key

Lusha is a B2B contact enrichment and prospecting platform for verified emails, phone numbers, and company data. It helps sales and go-to-market teams find accurate lead information faster.

7 Tools

Introduction

This guide walks you through connecting Lusha to Pydantic AI using the Composio tool router. By the end, you'll have a working Lusha agent that can enrich prospects from target company list, find verified emails for sales leads, get phone numbers for decision-makers through natural language commands.

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

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

Also integrate Lusha with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Lusha
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Lusha workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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

The Lusha MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lusha account. It provides structured and secure access to your webhook subscriptions and audit activity, so your agent can create, review, update, and delete subscriptions, and monitor account activity on your behalf.

  • Webhook subscription setup: Have your agent create one or more webhook subscriptions for the real-time signal notifications you want to receive.
  • Subscription review: Let the agent list your webhook subscriptions and retrieve the details of a specific subscription.
  • Subscription updates: Direct your agent to change an existing webhook subscription when your notification needs evolve.
  • Subscription cleanup: Instruct your agent to delete webhook subscriptions that your team no longer uses.
  • Account activity monitoring: Have your agent review audit logs and aggregated statistics covering API calls and webhook deliveries.

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Lusha
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Lusha
  • MCPServerStreamableHTTP connects to the Lusha MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Lusha
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["lusha"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Lusha 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
lusha_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[lusha_mcp],
    instructions=(
        "You are a Lusha assistant. Use Lusha tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Lusha endpoint
  • The agent uses GPT-5 to interpret user commands and perform Lusha operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Lusha.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Lusha API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Lusha and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Lusha
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["lusha"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    lusha_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[lusha_mcp],
        instructions=(
            "You are a Lusha assistant. Use Lusha tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Lusha.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Lusha through Composio's Tool Router. With this setup, your agent can perform real Lusha actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Lusha for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Lusha action and event your agent gets out of the box.

Create Subscription

Tool to create one or more webhook subscriptions for real-time signal notifications.

Delete Subscriptions

Delete one or more webhook subscriptions by their IDs.

Get Audit Logs

Retrieve audit logs for your account including API calls and webhook deliveries.

Get Audit Log Stats

Retrieve aggregated statistics for audit logs.

Get Subscription

Tool to get details of a specific webhook subscription by ID.

List Subscriptions

List all webhook subscriptions for your account with pagination support.

Update Subscription

Update an existing webhook subscription configuration.

FRAMEWORKS

How to build Lusha MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Lusha MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Lusha MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Lusha MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Lusha MCP with Claude Cowork

Codex logo

Codex

Use Lusha MCP with Codex

OpenClaw logo

OpenClaw

Use Lusha MCP with OpenClaw

Hermes logo

Hermes

Use Lusha MCP with Hermes

CLI logo

CLI

Use Lusha MCP with CLI

Google ADK logo

Google ADK

Use Lusha MCP with Google ADK

LangChain logo

LangChain

Use Lusha MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Lusha MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Lusha MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Lusha MCP with LlamaIndex

CrewAI logo

CrewAI

Use Lusha MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Pydantic AI 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 Lusha tools.

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

Start with Lusha.It takes 30 seconds.

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

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

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
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
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
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
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
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
Jerome Leclanche
Co-Founder, Ingram Technologies