> ## Documentation Index
> Fetch the complete documentation index at: https://composio-27-feat-docs-revamp.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Lead Generator Agent

> This project demonstrates how to use Composio to create a lead generation agent.

## Overview

The AI Lead Generator Agent is a powerful tool built using Composio’s tooling ecosystem and agentic frameworks such as LlamaIndex. This agent streamlines the lead generation process for businesses by identifying potential leads, extracting valuable data, and organizing all lead information into a structured spreadsheet. With a user-friendly setup process and seamless integration capabilities, this agent can significantly enhance your outreach efficiency and sales pipeline management.

## Getting Started

<Tabs>
  <Tab title="Python">
    <Steps>
      <Step title="Installation">
        ```bash install dependencies
        pip install composio-llamaindex python-dotenv
        ```
      </Step>

      <Step title="Connecting to tools and models">
        ```bash connect to required tools
        composio add peopledatalabs
        composio add googlesheets

        export OPENAI_API_KEY="<your-openai-api-key>"
        ```
      </Step>

      <Step title="Importing the required libraries">
        ```python import required libraries
        from composio_llamaindex import ComposioToolSet, App, Action
        from llama_index.core.agent import FunctionCallingAgentWorker
        from llama_index.core.llms import ChatMessage
        from llama_index.llms.openai import OpenAI
        from dotenv import load_dotenv

        load_dotenv()
        ```
      </Step>

      <Step title="Initializing the Tools and the LLM">
        ```python initialize toolset and llm
        toolset = ComposioToolSet(api_key="")
        tools = toolset.get_tools(apps=[App.PEOPLEDATALABS, App.GOOGLESHEETS])

        llm = OpenAI(model="gpt-4o")
        ```
      </Step>

      <Step title="Setting up Function Calling Worker">
        ```python setup function calling worker
        spreadsheetid = '14T4e0j1XsWjriQYeFMgkM2ihyvLAplPqB9q8hytytcw'
        prefix_messages = [
            ChatMessage(
                role="system",
                content=(
                    f"""
                    You are a lead research agent. Based on user input, find 10 relevant leads using people data labs.
                    After finding the leads, create a Google Sheet with the details for the lead description, and spreadsheet ID: ${spreadsheetid}.
                    Print the list of people and their details and the link to the google sheet."""
                ),
            )
        ]

        agent = FunctionCallingAgentWorker(
            tools=tools,
            llm=llm,
            prefix_messages=prefix_messages,
            max_function_calls=10,
            allow_parallel_tool_calls=False,
            verbose=True,
        ).as_agent()
        ```
      </Step>

      <Step title="Executing the Agent">
        ```python run the agent
        lead_description = 'Senior frontend developers in San Francisco'
        user_input = f"Create a lead list based on the description: {lead_description}"
        response = agent.chat(user_input)
        ```
      </Step>

      <Step title="Final Code">
        ```python final code
        from composio_llamaindex import ComposioToolSet, App, Action
        from llama_index.core.agent import FunctionCallingAgentWorker
        from llama_index.core.llms import ChatMessage
        from llama_index.llms.openai import OpenAI
        from dotenv import load_dotenv

        load_dotenv()
        toolset = ComposioToolSet(api_key="")
        tools = toolset.get_tools(apps=[App.PEOPLEDATALABS, App.GOOGLESHEETS])

        llm = OpenAI(model="gpt-4o")

        spreadsheetid = '14T4e0j1XsWjriQYeFMgkM2ihyvLAplPqB9q8hytytcw'
        prefix_messages = [
            ChatMessage(
                role="system",
                content=(
                    f"""
                    You are a lead research agent. Based on user input, find 10 relevant leads using people data labs.
                    After finding the leads, create a Google Sheet with the details for the lead description, and spreadsheet ID: ${spreadsheetid}.
                    Print the list of people and their details and the link to the google sheet."""
                ),
            )
        ]

        agent = FunctionCallingAgentWorker(
            tools=tools,
            llm=llm,
            prefix_messages=prefix_messages,
            max_function_calls=10,
            allow_parallel_tool_calls=False,
            verbose=True,
        ).as_agent()

        lead_description = 'Senior frontend developers in San Francisco'
        user_input = f"Create a lead list based on the description: {lead_description}"
        response = agent.chat(user_input)
        ```
      </Step>
    </Steps>
  </Tab>

  <Tab title="Javascript">
    <Steps>
      <Step title="Installation">
        ```bash install dependencies
        npm install composio-core ai @ai-sdk/openai dotenv
        ```
      </Step>

      <Step title="Connecting to tools and models">
        ```bash connect to required tools
        composio add peopledatalabs
        composio add googlesheets

        export OPENAI_API_KEY="<your-openai-api-key>"
        export COMPOSIO_API_KEY="<your-composio-api-key>"
        ```
      </Step>

      <Step title="Importing the required libraries">
        ```javascript import required libraries
        import { openai } from "@ai-sdk/openai";
        import { VercelAIToolSet } from "composio-core";
        import dotenv from "dotenv";
        import { generateText } from "ai";

        dotenv.config();
        ```
      </Step>

      <Step title="Initializing the Tools and the LLM">
        ```javascript initialize toolset and llm
        const toolset = new VercelAIToolSet({
          apiKey: process.env.COMPOSIO_API_KEY,
        });

        const tools = await toolset.getTools([App.PEOPLEDATALABS, App.GOOGLESHEETS]);
        ```
      </Step>

      <Step title="Setting up Agent">
        ```javascript setup the ai agent
        const leadDescription = 'Senior frontend developers in San Francisco';
        const spreadsheetid='14T4e0j1XsWjriQYeFMgkM2ihyvLAplPqB9q8hytytcw'
        const output = await generateText({
        model: openai("gpt-4o"),
        streamText: false,
        tools: tools,
        prompt: `
                You are a lead research agent. Based on user input, find 10 relevant leads using people data labs.
                After finding the leads, create a Google Sheet with the details for the lead description: ${leadDescription}, and spreadsheet ID: ${spreadsheetid}.
                Print the list of people and their details and the link to the google sheet.
                `, 
        maxToolRoundtrips: 5,
        });
        ```
      </Step>

      <Step title="Final Code">
        ```javascript final code
        import { openai } from "@ai-sdk/openai";
        import { VercelAIToolSet } from "composio-core";
        import dotenv from "dotenv";
        import { generateText } from "ai";

        dotenv.config();

        const toolset = new VercelAIToolSet({
          apiKey: process.env.COMPOSIO_API_KEY,
        });

        const tools = await toolset.getTools([App.PEOPLEDATALABS, App.GOOGLESHEETS]);

        const leadDescription = 'Senior frontend developers in San Francisco';
        const spreadsheetid='14T4e0j1XsWjriQYeFMgkM2ihyvLAplPqB9q8hytytcw'
        const output = await generateText({
        model: openai("gpt-4o"),
        streamText: false,
        tools: tools,
        prompt: `
                You are a lead research agent. Based on user input, find 10 relevant leads using people data labs.
                After finding the leads, create a Google Sheet with the details for the lead description: ${leadDescription}, and spreadsheet ID: ${spreadsheetid}.
                Print the list of people and their details and the link to the google sheet.
                `, 
        maxToolRoundtrips: 5,
        });

        console.log("🎉Output from agent: ", output.text);

        ```
      </Step>
    </Steps>
  </Tab>
</Tabs>