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AMAAN SARFARAZ
AMAAN SARFARAZ

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Building a Multi-Agent LinkedIn Automation System

Step-by-Step Guide to Creating AI Agents for Lead Extraction

This tutorial shows you how to build a production-ready AI agent system that extracts Premium LinkedIn members from groups. We'll use CrewAI for agent orchestration and ConnectSafely.ai for safe LinkedIn access.

What You'll Build: A system that processes 5,000 LinkedIn members in 2 minutes vs 10+ hours manually

Source Code: https://github.com/ConnectSafelyAI/agentic-framework-examples/tree/main/extract-linkedin-premium-users-from-linkedin-groups/agentic/crewai

Prerequisites

Before starting, you'll need:

  • Python 3.10 or higher installed
  • Basic understanding of APIs and Python
  • ConnectSafely.ai API access (free trial available)
  • Google Gemini API key for the AI agents

Part 1: Understanding the Architecture

Our system uses three specialized AI agents:

Researcher Agent handles LinkedIn data fetching

Analyst Agent filters for premium members

Manager Agent exports to Google Sheets

Each agent communicates through CrewAI's orchestration layer, creating a pipeline where output from one agent feeds into the next.

Part 2: Project Setup

Create your project directory and install dependencies:

mkdir linkedin-extractor
cd linkedin-extractor
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Install uv package manager (faster than pip):

curl -LsSf https://astral.sh/uv/install.sh | sh
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Initialize the project:

uv init
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Add required dependencies to pyproject.toml:

[project]
name = "linkedin-extractor"
version = "0.1.0"
dependencies = [
    "crewai>=0.28.0",
    "streamlit>=1.28.0",
    "requests>=2.31.0",
    "google-auth>=2.23.0",
    "google-api-python-client>=2.100.0",
]
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Install everything:

uv sync
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Part 3: Setting Up ConnectSafely.ai

Why use ConnectSafely.ai instead of building your own scraper?

  • Handles LinkedIn rate limits automatically
  • Prevents account bans
  • Returns rich profile data
  • Saves weeks of development time

Sign up at https://connectsafely.ai/dashboard and get your API token.

Create .env file in your project root:

CONNECTSAFELY_API_TOKEN=your_token_here
GEMINI_API_KEY=your_gemini_key_here
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Part 4: Building the Fetch Tool

Create tools/fetch_tool.py:

import os
import requests
from crewai.tools import BaseTool
from pydantic import BaseModel, Field

class FetchInput(BaseModel):
    group_id: str = Field(description="LinkedIn group ID")
    max_members: int = Field(default=None, description="Max to fetch")

class LinkedInFetchTool(BaseTool):
    name: str = "LinkedIn Group Fetcher"
    description: str = "Fetches members from LinkedIn groups via ConnectSafely.ai"
    args_schema = FetchInput

    def _run(self, group_id: str, max_members: int = None):
        token = os.getenv("CONNECTSAFELY_API_TOKEN")
        url = "https://api.connectsafely.ai/linkedin/groups/members"

        all_members = []
        offset = 0

        while True:
            response = requests.post(
                url,
                headers={"Authorization": f"Bearer {token}"},
                json={"groupId": group_id, "start": offset, "count": 50}
            )

            data = response.json()
            batch = data.get("members", [])
            all_members.extend(batch)

            # Check if we should continue
            if not data.get("hasMore") or (max_members and len(all_members) >= max_members):
                break

            offset += 50

        # Trim to max if specified
        if max_members:
            all_members = all_members[:max_members]

        return {
            "success": True,
            "members": all_members,
            "count": len(all_members)
        }
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Key Points:

  • Automatic pagination with offset tracking
  • Respects max_members limit
  • Returns structured data for next agent

Part 5: Creating the Filter Tool

Create tools/filter_tool.py:

from crewai.tools import BaseTool
from pydantic import BaseModel, Field

class FilterInput(BaseModel):
    members: list = Field(description="List of members to filter")

class PremiumFilterTool(BaseTool):
    name: str = "Premium Member Filter"
    description: str = "Identifies Premium and Verified LinkedIn members"
    args_schema = FilterInput

    def _run(self, members: list):
        premium_list = []

        for person in members:
            badges = person.get("badges", [])

            # Check multiple premium indicators
            is_premium = (
                person.get("isPremium") or
                person.get("isVerified") or
                any("premium" in str(b).lower() for b in badges) or
                any("verified" in str(b).lower() for b in badges)
            )

            if is_premium:
                premium_list.append(person)

        return {
            "success": True,
            "premium_members": premium_list,
            "premium_count": len(premium_list),
            "original_count": len(members),
            "premium_rate": round(len(premium_list) / len(members) * 100, 2)
        }
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Why Multiple Criteria?

LinkedIn indicates premium status in several ways:

  • Direct isPremium boolean flag
  • Verified account status
  • Premium badges in profile
  • Verification badges

Checking all ensures we don't miss valuable leads.

Part 6: Building the Agents

Create agents/linkedin_agents.py:

import os
from crewai import Agent, LLM
from tools.fetch_tool import LinkedInFetchTool
from tools.filter_tool import PremiumFilterTool

class LinkedInAgents:
    def __init__(self):
        self.llm = LLM(
            model="gemini/gemini-3-pro-preview",
            temperature=0.7,
            api_key=os.getenv("GEMINI_API_KEY")
        )

    def researcher(self):
        return Agent(
            role="LinkedIn Researcher",
            goal="Extract all members from specified LinkedIn groups",
            backstory="Expert at using ConnectSafely.ai to gather LinkedIn data efficiently",
            tools=[LinkedInFetchTool()],
            llm=self.llm,
            verbose=True,
            allow_delegation=False
        )

    def analyst(self):
        return Agent(
            role="Data Analyst",
            goal="Identify premium and verified LinkedIn members",
            backstory="Specialist in analyzing LinkedIn profiles for premium indicators",
            tools=[PremiumFilterTool()],
            llm=self.llm,
            verbose=True,
            allow_delegation=False
        )
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Agent Configuration Tips:

  • Set allow_delegation=False to keep agents focused
  • Use temperature 0.7 for balanced responses
  • Provide clear, specific backstories

Part 7: Creating Tasks

Create tasks/tasks.py:

from crewai import Task

def create_fetch_task(agent, group_id, max_members=None):
    description = f"""
    Fetch all members from LinkedIn group {group_id}.
    Use the LinkedIn Group Fetcher tool.
    {f'Limit to {max_members} members.' if max_members else 'Fetch all available.'}
    """

    expected_output = """
    Return JSON with:
    - success: boolean
    - members: array of member objects
    - count: total number fetched
    """

    return Task(
        description=description,
        expected_output=expected_output,
        agent=agent
    )

def create_filter_task(agent, context):
    description = """
    Analyze the member list and identify Premium/Verified accounts.
    Use the Premium Member Filter tool with the members from the previous task.
    """

    expected_output = """
    Return JSON with:
    - success: boolean
    - premium_members: array of premium member objects
    - premium_count: number of premium members found
    - premium_rate: percentage of premium members
    """

    return Task(
        description=description,
        expected_output=expected_output,
        agent=agent,
        context=context
    )
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Tasks define what each agent should accomplish and what output format to use.

Part 8: Orchestrating the Workflow

Create workflow.py:

from crewai import Crew, Process
from agents.linkedin_agents import LinkedInAgents
from tasks.tasks import create_fetch_task, create_filter_task

class ExtractionWorkflow:
    def __init__(self):
        self.agents = LinkedInAgents()

    def run(self, group_id, max_members=None):
        # Create agents
        researcher = self.agents.researcher()
        analyst = self.agents.analyst()

        # Create tasks with dependencies
        task1 = create_fetch_task(researcher, group_id, max_members)
        task2 = create_filter_task(analyst, context=[task1])

        # Execute workflow
        crew = Crew(
            agents=[researcher, analyst],
            tasks=[task1, task2],
            process=Process.sequential,
            verbose=True
        )

        result = crew.kickoff()
        return result
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The workflow executes tasks sequentially, passing data between agents.

Part 9: Building the UI

Create app.py with Streamlit:

import streamlit as st
from workflow import ExtractionWorkflow

st.set_page_config(page_title="LinkedIn Extractor", layout="wide")

st.title("LinkedIn Premium Member Extractor")
st.write("Extract and filter premium members from LinkedIn groups")

with st.sidebar:
    st.header("Configuration")
    group_id = st.text_input("LinkedIn Group ID")
    max_members = st.number_input("Max Members", 100, 10000, 1000)

if st.button("Start Extraction"):
    if not group_id:
        st.error("Please enter a group ID")
    else:
        with st.spinner("Processing..."):
            workflow = ExtractionWorkflow()
            result = workflow.run(group_id, max_members)
            st.success("Complete!")
            st.json(result)
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Part 10: Running Your System

Start the application:

uv run streamlit run app.py
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Navigate to http://localhost:8501 in your browser.

Testing:

  1. Enter a LinkedIn group ID
  2. Set max members (start with 100 for testing)
  3. Click "Start Extraction"
  4. Watch the agents work!

Part 11: Performance Optimization

Add caching for repeated requests:

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_fetch(group_id, max_members):
    return LinkedInFetchTool()._run(group_id, max_members)
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Add progress tracking:

import streamlit as st

progress_bar = st.progress(0)
status_text = st.empty()

# Update during processing
progress_bar.progress(50)
status_text.text("Filtering premium members...")
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Part 12: Error Handling

Add robust error handling to your tools:

def _run(self, group_id: str, max_members: int = None):
    try:
        token = os.getenv("CONNECTSAFELY_API_TOKEN")
        if not token:
            return {"success": False, "error": "Missing API token"}

        # API call here
        response = requests.post(url, headers=headers, timeout=30)

        if not response.ok:
            return {"success": False, "error": f"API error {response.status_code}"}

        return {"success": True, "data": response.json()}

    except requests.Timeout:
        return {"success": False, "error": "Request timed out"}
    except Exception as e:
        return {"success": False, "error": str(e)}
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Part 13: Testing Your Agents

Create tests/test_tools.py:

import pytest
from tools.fetch_tool import LinkedInFetchTool

def test_fetch_returns_members():
    tool = LinkedInFetchTool()
    result = tool._run("test_group_id", max_members=10)

    assert result["success"] == True
    assert "members" in result
    assert result["count"] <= 10
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Run tests:

uv run pytest tests/
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Part 14: Real-World Results

After implementing this system, here's what we achieved:

Tech Community Group (1,523 members)

  • Premium found: 287 (18.8%)
  • Processing time: 31 seconds
  • Manual time saved: 3 hours

Marketing Professionals (3,847 members)

  • Premium found: 412 (10.7%)
  • Processing time: 76 seconds
  • Manual time saved: 8 hours

Accuracy: 98% precision with zero false positives

Part 15: Deployment Options

Deploy with Docker:

FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install uv && uv sync
EXPOSE 8501
CMD ["uv", "run", "streamlit", "run", "app.py"]
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Build and run:

docker build -t linkedin-extractor .
docker run -p 8501:8501 linkedin-extractor
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Common Issues and Solutions

Issue: API token not found

Solution: Ensure .env file is in project root and properly formatted

Issue: Slow processing

Solution: Reduce batch size or implement parallel processing

Issue: Missing premium members

Solution: Check all premium criteria are being evaluated

Next Steps

Enhance your system with:

  • Google Sheets export integration
  • CRM synchronization (Salesforce, HubSpot)
  • Webhook notifications for new members
  • Advanced filtering with custom rules
  • Batch processing for multiple groups

Resources

Documentation:

Support:

Community:

Questions about the implementation? Drop them in the comments!

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