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AMAAN SARFARAZ
AMAAN SARFARAZ

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Build an AI Agent to Export LinkedIn Search Results to Google Sheets

#ai #automation #agents #linkedin

Ever spent hours manually searching LinkedIn and copying profile data into spreadsheets? What if an AI agent could do all that for you with a simple command like "Find 100 CEOs in the United States and export to Google Sheets"?

In this article, I'll show you how to build exactly that—an AI-powered agent that automates LinkedIn profile searching and Google Sheets export. We'll implement it using four different agentic frameworks so you can choose the one that fits your tech stack.

The Problem

Building lead lists, candidate pipelines, or market research databases from LinkedIn is tedious:

  • Manual searching: Hours browsing for the right profiles
  • Copy-paste nightmare: Moving data into spreadsheets one by one
  • Inconsistent data: Different formats and missing fields
  • Rate limiting risks: Manual scraping can get your account restricted

The Solution: An AI Agent

We'll build an agent that:

  1. Understands natural language commands
  2. Searches LinkedIn via ConnectSafely.ai API (no scraping, platform-compliant)
  3. Formats the data consistently
  4. Exports directly to Google Sheets or JSON

Four Framework Implementations

I've implemented this agent in four different frameworks:

Framework Language Interface
AutoGen Python Streamlit Web
CrewAI Python Streamlit Web
LangGraph TypeScript CLI
Mastra TypeScript Web UI

Let's look at each one!

1. AutoGen Implementation (Python)

AutoGen from Microsoft uses a modular architecture with a single assistant agent.

# agents/assistant.py
from tools import search_people, export_to_sheets

class LinkedInExportAssistant:
    def __init__(self):
        self.tools = [
            search_people,
            export_to_sheets,
            export_to_json
        ]
        self.assistant = create_assistant_agent(
            api_key=os.getenv("GEMINI_API_KEY"),
            model="gemini-2.5-pro",
            tools=self.tools
        )

    async def execute_async(self, command: str):
        result = await self.assistant.run(task=command)
        return clean_response(result)
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The Streamlit UI makes it easy to interact:

# App.py
command = st.chat_input("Enter your command...")
if command:
    result = client.execute(command=command)
    st.markdown(result)
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Run it:

cd autogen
uv sync
uv run streamlit run App.py
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2. CrewAI Implementation (Python)

CrewAI uses a task-based approach with built-in memory:

# agents/agents.py
from crewai import Agent, LLM
from crewai.tools import tool

@tool("Search LinkedIn People")
def search_people(keywords: str, location: str = None):
    """Search for LinkedIn profiles."""
    # API call to ConnectSafely.ai
    pass

agent = Agent(
    role="LinkedIn Export Specialist",
    goal="Search LinkedIn profiles and export results",
    tools=[search_people, export_to_sheets],
    llm=LLM(model="gemini/gemini-2.5-pro"),
    memory=True,
)
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Run it:

cd crewai
uv sync
uv run streamlit run App.py
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3. LangGraph Implementation (TypeScript)

LangGraph uses a state graph architecture for complex workflows:

// agents/linkedin-export-agent.ts
import { StateGraph } from "@langchain/langgraph";

const workflow = new StateGraph(AgentState)
  .addNode("agent", callModel)
  .addNode("tools", toolNode)
  .addEdge("__start__", "agent")
  .addConditionalEdges("agent", shouldContinue, {
    tools: "tools",
    end: "__end__",
  })
  .addEdge("tools", "agent");

export const linkedInExportAgent = workflow.compile();
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Tools are defined with Zod schemas:

// tools/search-people.ts
export const searchPeopleTool = tool(
  async ({ keywords, location, title, limit }) => {
    const res = await fetch(
      "https://api.connectsafely.ai/linkedin/search/people",
      {
        method: "POST",
        headers: { Authorization: `Bearer ${API_TOKEN}` },
        body: JSON.stringify({ keywords, location, title, limit }),
      }
    );
    return JSON.stringify(await res.json());
  },
  {
    name: "search-people",
    schema: z.object({
      keywords: z.string(),
      location: z.string().optional(),
      title: z.string().optional(),
      limit: z.number().default(100),
    }),
  }
);
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Run it:

cd langGraph
bun install
bun run dev
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4. Mastra Implementation (TypeScript)

Mastra offers the simplest configuration with built-in memory:

// agents/linkedin-export-agent.ts
import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";

export const linkedInExportAgent = new Agent({
  name: "LinkedIn to Sheets Export Agent",
  model: "google/gemini-3-flash-preview",
  instructions: linkedInExportAgentInstructions,
  tools: {
    searchPeopleTool,
    exportToSheetsTool,
    exportToJsonTool,
  },
  memory: new Memory({
    storage: new LibSQLStore({ url: "file:./mastra.db" }),
  }),
});
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Run it:

cd mastra
bun install
bun run dev
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The ConnectSafely.ai API

All implementations use ConnectSafely.ai for LinkedIn data access:

  • Platform-compliant: No scraping, no ban risk
  • Bearer token auth: Simple API key authentication
  • Rich data: Full profile information
  • Rate limiting: Built-in protection

Example API call:

const response = await fetch(
  "https://api.connectsafely.ai/linkedin/search/people",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${CONNECTSAFELY_API_TOKEN}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      keywords: "CEO SaaS",
      location: "United States",
      title: "Head of Growth",
      limit: 100,
    }),
  }
);
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Setting Up Google Sheets Export

  1. Create a Google Cloud project
  2. Enable Google Sheets API
  3. Create OAuth credentials:
    • Go to APIs & Services → Credentials
    • Create OAuth client ID (Desktop app type)
    • Generate refresh token using OAuth Playground
    • Required scopes: https://www.googleapis.com/auth/spreadsheets, https://www.googleapis.com/auth/drive 
# .env
GOOGLE_CLIENT_ID=your_oauth_client_id
GOOGLE_CLIENT_SECRET=your_oauth_client_secret
GOOGLE_REFRESH_TOKEN=your_refresh_token
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Which Framework Should You Choose?

If you want... Choose
Modular Python code AutoGen
Task-based workflows CrewAI
Complex stateful flows LangGraph
Quick setup with built-in UI Mastra

Try It Yourself

The full source code is available on GitHub:

git clone https://github.com/ConnectSafelyAI/agentic-framework-examples
cd agentic-framework-examples/linkedin-to-sheets-export
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Each framework has its own README with detailed setup instructions.

What's Next?

This is just the beginning! You could extend this agent to:

  • Schedule automated searches
  • Filter results by company size or industry
  • Add email enrichment
  • Build automated outreach sequences

Questions? Drop them in the comments below!

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