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Telegram Group Scraping: Extract Members Safely
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Telegram Group Scraping: Extract Members Safely

A crypto trader running a signals channel scraped 4,200 members from three competing groups, filtered for users with "trader" or "DeFi" in their bios, and added 1,100 targeted subscribers in a single week through personalized outreach. That campaign started with a Python script, a Telethon session, and a clear understanding of how Telegram's API exposes group participant data. This guide covers every step of that process: from raw API calls to cleaned, campaign-ready lists.

What Telegram Group Scraping Actually Means

Scraping a Telegram group means programmatically extracting the member list, message history, or both from a group or supergroup using Telegram's API. The output is structured data: usernames, display names, user IDs, bio text, last-seen timestamps, and message content. This data feeds outreach campaigns, audience research, competitor analysis, and lead generation workflows.

The distinction from manual data collection matters. Manually copying usernames from a group with 5,000 members would take days. A scraping script finishes the same job in minutes, with structured output you can filter, deduplicate, and pipe directly into a Mass DMs campaign.

Telegram exposes two primary methods for extracting group data. The first is get_participants, which returns the full member list of a group you belong to. The second is iterating through message history to collect unique senders. Each method has different strengths, limitations, and rate limit profiles, and the best scraping workflows combine both.

Groups and supergroups behave differently under the API. Regular groups cap at 200 members and expose all participants directly. Supergroups can hold up to 200,000 members, but the API caps participant retrieval at around 10,000. Understanding these boundaries before writing a single line of code prevents wasted effort and unexpected blocks.

Why Scraping Matters for Outreach

A fitness coach posted a free workout plan to 15 Telegram fitness groups. Response rate: near zero. That same coach scraped members from those groups, filtered for users with "personal trainer" or "fitness" in their bios, and sent personalized DMs referencing the specific group they shared. Conversion rate jumped from under 0.5% to 7.3%. The difference was not the offer. It was the targeting.

Outreach without targeting is noise. Every person in a Telegram group opted into that group for a reason, and that reason is your targeting signal. A member of "DeFi Alpha Hunters" is more likely to care about DeFi tools than a random Telegram user. A member of "Shopify Dropshipping Secrets" is pre-qualified for e-commerce offers. The group itself is the filter.

Scraping converts that implicit interest into actionable data. Instead of guessing who might care about your channel or product, you extract a list of people who already demonstrated interest by joining a relevant community. This is the foundation of every high-performing Telegram outreach campaign.

The Economics of Targeted vs. Untargeted Outreach

Sending 10,000 untargeted DMs with a 0.3% response rate yields 30 responses. Sending 2,000 targeted DMs from a scraped and filtered list with a 6% response rate yields 120 responses. The targeted approach costs less in account resources, carries lower risk of spam reports, and produces four times the results from one-fifth the volume. Every serious Telegram marketer scrapes before they send.

Use Cases Beyond Direct Outreach

Scraping is not limited to DM campaigns. Competitor analysis becomes concrete when you can see exactly who participates in competing groups. Audience overlap studies reveal which communities share members and which are isolated. Trend detection emerges from monitoring message content across multiple groups over time. Influencer identification becomes systematic when you can rank users by message frequency, reaction counts, and presence across multiple groups. Floqal Analytics pairs well with this data for tracking what happens after outreach.

Telegram API Basics for Scraping

Before touching a single library, you need Telegram API credentials. Go to my.telegram.org, sign in with your phone number, and create a new application. You will receive an api_id (integer) and api_hash (string). These credentials authenticate your scraping client against Telegram's servers.

Telegram offers two API layers: the Bot API and the Client API (also called MTProto or the User API). The Bot API is limited to bot-specific actions and cannot access group member lists unless the bot is an admin. The Client API, accessed through libraries like Telethon (Python) or GramJS (JavaScript), operates as a regular user account and has full access to any group the account has joined.

get_participants: The Primary Scraping Method

The get_participants method returns a list of user objects for a given group or supergroup. Each user object contains the user ID, first name, last name, username, phone (if accessible), bio/about text (via a separate API call), and status information including last-seen timestamp.

For supergroups, the API returns participants in batches. You specify an offset and limit per request, iterating until no more users are returned or until you hit the 10,000 ceiling. You can also pass filter objects to retrieve only specific participant types: admins, bots, recently active users, or users matching a search query.

get_messages: Scraping Through Message History

The alternative approach iterates through a group's message history using get_messages or iter_messages and collects the from_id (sender) of each message. This method bypasses the 10,000 participant limit because it accesses messages, not the member list directly. A group with 50,000 members where 15,000 have posted at least one message will yield 15,000 unique sender IDs through message iteration.

The trade-off: message sender scraping only captures active participants. Lurkers who joined but never posted will not appear. For outreach purposes, this is often an advantage, because active posters are more engaged and more likely to respond to a DM than silent lurkers.

Understanding the API Response Structure

Each participant object returned by the API contains structured data. The user.id is the unique numeric identifier that never changes, even if the user modifies their username or display name. The user.username is the human-readable handle (without the @ prefix). The user.first_name and user.last_name fields contain the display name. The user.bot boolean flag indicates whether the account is a bot. The user.status object reveals last-seen information: recently online, within the past week, within the past month, or hidden by privacy settings.

For each user, you can make a secondary API call to GetFullUser to retrieve the bio/about text. This is where keyword filtering becomes powerful: a user's bio often contains their profession, interests, and self-description, which are the strongest targeting signals available.

Python Approach: Telethon Setup and Code Examples

Telethon is the most widely used Python library for interacting with Telegram's Client API. It wraps the raw MTProto protocol into clean, async Python methods. This complete setup and scraping example.

Installation and Authentication

Install Telethon with pip:

pip install telethon

Create your client and authenticate:

from telethon.sync import TelegramClient

api_id = 12345678          # from my.telegram.org
api_hash = 'your_api_hash' # from my.telegram.org
phone = '+1234567890'

client = TelegramClient('scraping_session', api_id, api_hash)
client.start(phone=phone)
print('Connected successfully')

The first time you run this, Telethon will prompt for your phone number verification code sent via Telegram. After authentication, a session file is created locally, so subsequent runs skip the verification step.

Scraping the Full Member List

from telethon.tl.functions.channels import GetParticipantsRequest
from telethon.tl.types import ChannelParticipantsSearch
import csv

group_username = 'target_group_username'
channel = client.get_entity(group_username)

all_participants = []
offset = 0
limit = 200

while True:
    participants = client(GetParticipantsRequest(
        channel=channel,
        filter=ChannelParticipantsSearch(''),
        offset=offset,
        limit=limit,
        hash=0
    ))
    if not participants.users:
        break
    all_participants.extend(participants.users)
    offset += len(participants.users)
    print(f'Fetched {len(all_participants)} members so far...')

# Export to CSV
with open('members.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['user_id', 'username', 'first_name', 'last_name', 'is_bot'])
    for user in all_participants:
        writer.writerow([
            user.id,
            user.username or '',
            user.first_name or '',
            user.last_name or '',
            user.bot
        ])

print(f'Exported {len(all_participants)} members to members.csv')

This script connects to the target group, retrieves participants in batches of 200, and exports the results to a CSV file. The ChannelParticipantsSearch('') filter with an empty string returns all participants. You can pass a search string to filter server-side, which is faster than downloading everyone and filtering locally for simple name matches.

Scraping Message Senders

from collections import defaultdict

group_username = 'target_group_username'
senders = defaultdict(int)

async def scrape_senders():
    async for message in client.iter_messages(group_username, limit=10000):
        if message.sender_id:
            senders[message.sender_id] += 1

    # Sort by message count (most active first)
    sorted_senders = sorted(senders.items(), key=lambda x: x[1], reverse=True)

    print(f'Found {len(sorted_senders)} unique senders')
    for sender_id, count in sorted_senders[:20]:
        try:
            user = await client.get_entity(sender_id)
            print(f'@{user.username or "N/A"} - {count} messages')
        except Exception:
            print(f'ID {sender_id} - {count} messages (could not resolve)')

with client:
    client.loop.run_until_complete(scrape_senders())

This approach scans the last 10,000 messages, counts how many each unique sender posted, and ranks them by activity. The most active members are typically the highest-value outreach targets because they are genuinely engaged with the topic.

Fetching User Bios for Keyword Filtering

from telethon.tl.functions.users import GetFullUserRequest
import asyncio

async def get_user_bio(user_id):
    try:
        full = await client(GetFullUserRequest(user_id))
        return full.full_user.about or ''
    except Exception:
        return ''

async def enrich_with_bios(user_list, keywords):
    matched = []
    for user in user_list:
        bio = await get_user_bio(user.id)
        if any(kw.lower() in bio.lower() for kw in keywords):
            matched.append({
                'id': user.id,
                'username': user.username,
                'name': f'{user.first_name or ""} {user.last_name or ""}'.strip(),
                'bio': bio
            })
        await asyncio.sleep(0.5)  # respect rate limits
    return matched

# Usage: filter for users with specific keywords in their bios
keywords = ['trader', 'DeFi', 'crypto', 'investor']
results = client.loop.run_until_complete(
    enrich_with_bios(all_participants[:500], keywords)
)
print(f'Found {len(results)} keyword-matched users')

Bio enrichment adds a 0.5-second delay between requests to stay within rate limits. For large lists, this step is the bottleneck. A list of 5,000 users takes roughly 40 minutes to fully enrich. Prioritize enriching your most promising segments first.

Member List Scraping vs. Message Sender Scraping

A marketing agency scraped a 12,000-member crypto group using both methods. The member list returned 9,847 users (the API cap). The message sender scan of the last 50,000 messages returned 3,200 unique senders. The agency ran identical DM campaigns to 500 users from each list. The member list segment converted at 3.1%. The message sender segment converted at 8.7%. Active participants responded nearly three times as often.

When to Use Member List Scraping

Member list scraping is the right choice when you need the broadest possible reach. It captures everyone: active posters, occasional participants, and silent lurkers. This is useful when your niche is small and you cannot afford to filter out any potential leads. It is also the only option for groups where most members rarely post, such as announcement-style groups or channels with linked discussion groups where the discussion group has low activity.

The limitation is the 10,000 cap on supergroups. If your target group has 50,000 members, you will only retrieve a subset through get_participants. The subset is not random; it tends to skew toward more recently joined members, though the exact ordering is not documented.

When to Use Message Sender Scraping

Message sender scraping is superior when lead quality matters more than volume. Every user in the output has demonstrably engaged with the group by posting at least one message. You can further rank them by message count, recency of last message, or specific keywords in their messages.

This method also bypasses the 10,000 participant cap. By scanning message history (which can go back hundreds of thousands of messages), you can extract senders from groups of any size, limited only by how much history you want to process and the API's message retrieval rate limits.

Combining Both Methods

The strongest approach combines both. Start with message sender scraping to identify active, engaged users. Then supplement with member list scraping to catch recently joined members who have not posted yet but may still be interested. Merge the two lists, deduplicate by user ID, and flag each user's source (active sender, passive member, or both). This gives you a complete dataset with built-in quality scoring.

Filtering Scraped Data

Raw scraped data is a starting point, not a finished product. A 10,000-member extraction might contain 2,000 bots, 1,500 inactive accounts, 800 duplicates from overlapping groups, and 3,000 users with no relevance to your niche. Filtering transforms noise into signal.

Activity-Based Filtering

Telegram exposes user status through the API: "recently" (online within the last few days), "last week," "last month," or "long time ago." Users with "long time ago" status or no status at all are either inactive or have privacy settings that hide their activity. For outreach campaigns, filtering for "recently" or "last week" status dramatically improves response rates. A user who was active on Telegram in the past few days is far more likely to see and respond to a DM than one who last logged in months ago.

from telethon.tl.types import UserStatusRecently, UserStatusLastWeek

active_users = [
    user for user in all_participants
    if isinstance(user.status, (UserStatusRecently, UserStatusLastWeek))
    and not user.bot
]
print(f'{len(active_users)} recently active, non-bot users')

Keyword Filtering

Keyword filters operate on three fields: username, display name, and bio. Bio filtering is the most powerful because users describe their interests, profession, and expertise in their bio text. A user whose bio says "Forex trader | 5 years experience" is a precise match for a forex signals channel. A user named "John" with no bio gives you nothing to work with.

Build keyword lists specific to your niche. For a SaaS tool targeting marketers, relevant keywords might include: "marketer," "marketing," "lead gen," "SEO," "PPC," "content," "agency," "freelance." Cast a wide initial net, then narrow based on campaign performance data.

Bot Detection and Removal

Telegram marks bot accounts with a boolean flag (user.bot = True), making programmatic removal straightforward. However, not all spam or low-quality accounts are flagged as bots. Additional heuristics for identifying non-human accounts include:

Removing bots from your list is not just about saving DM volume. Messaging bot accounts triggers no engagement, wastes account resources, and in some cases can trigger spam detection because bots do not interact with your messages the way real users do.

Deduplication Across Multiple Groups

If you scrape five groups in the same niche, active users will appear in multiple groups. Sending the same DM to the same user twice (or five times) is worse than useless. It marks your account as spam in the recipient's mind and increases the probability of reports.

seen_ids = set()
unique_users = []

for user in all_scraped_users:
    if user.id not in seen_ids:
        seen_ids.add(user.id)
        unique_users.append(user)

print(f'Deduplicated: {len(all_scraped_users)} -> {len(unique_users)} unique users')

Deduplication by user ID (not username, which can change) ensures each person receives your message exactly once, regardless of how many source groups they belong to.

Chrome Extensions and Third-Party Tools

Not every Telegram marketer wants to write Python scripts. Several tools provide GUI-based scraping without any coding. The landscape breaks into three categories: Chrome extensions, desktop applications, and SaaS platforms.

Chrome Extensions

Chrome extensions for Telegram Web scraping typically inject JavaScript into the Telegram Web interface to extract visible member lists. Their main advantage is simplicity: install the extension, open a group in Telegram Web, and click a button to export members. The downsides are significant: they only see members currently loaded in the browser's DOM (not the full member list), they are fragile against Telegram Web UI updates, and they run entirely client-side with no rate limit management. For small groups under 1,000 members, browser extensions work. For anything larger, they hit walls quickly.

Desktop Applications

Dedicated scraping applications (often built on Telethon or TDLib under the hood) provide a graphical interface for account authentication, group selection, filter configuration, and export options. These tools handle rate limiting, session management, and batch processing automatically. The better ones support multiple account rotation, which distributes API requests across several accounts to increase throughput and reduce per-account risk.

The risk with third-party desktop tools is trust. You are entering your Telegram credentials into software you did not write. Only use tools with established reputations, open-source codebases, or transparent security practices. An application that steals your session file can access your entire Telegram account.

SaaS Platforms with Built-In Scraping

Floqal's Mass DMs platform includes group scraping as a built-in feature, integrated directly into the outreach workflow. Instead of scraping with one tool, exporting to CSV, cleaning in a spreadsheet, and importing into a separate DM tool, everything happens in a single dashboard. You select target groups, apply keyword and activity filters, deduplicate across sources, and launch your DM campaign without switching tools or handling raw data files.

This integration eliminates the most common failure points: data format mismatches between scraping and sending tools, lost user IDs during CSV round-trips, and manual deduplication errors. For teams running campaigns regularly, the time savings compound quickly.

Comparing the Approaches

Rate Limits and How to Handle Them

Telegram enforces rate limits on API requests to prevent abuse. If you send too many requests too quickly, the API returns a FloodWaitError with a mandatory wait time (in seconds) before your next request will be accepted. Ignoring rate limits does not just slow you down; repeated violations can result in temporary or permanent account restrictions.

Understanding FloodWaitError

A FloodWaitError includes a seconds attribute telling you exactly how long to wait. Common triggers include: rapid-fire get_participants calls, fetching too many user bios in a short window, resolving too many usernames or user IDs, and sending too many messages. Wait times range from a few seconds for minor infractions to several hours for aggressive patterns.

from telethon.errors import FloodWaitError
import asyncio

async def safe_get_participants(client, channel, offset, limit):
    try:
        return await client(GetParticipantsRequest(
            channel=channel,
            filter=ChannelParticipantsSearch(''),
            offset=offset,
            limit=limit,
            hash=0
        ))
    except FloodWaitError as e:
        print(f'Rate limited. Waiting {e.seconds} seconds...')
        await asyncio.sleep(e.seconds + 1)
        return await safe_get_participants(client, channel, offset, limit)

Practical Rate Limit Strategies

Prevention is better than recovery. These patterns keep you well within Telegram's tolerance:

Account Safety During Scraping

Fresh accounts that immediately start heavy API usage get flagged faster than established accounts with normal usage patterns. If you are setting up accounts specifically for scraping, "warm" them first: join a few groups, send some messages to contacts, use the account normally for a few days before starting automated operations. An account that has existed for months with regular human activity can sustain higher API throughput than a brand-new account that starts scraping on day one.

Cleaning and Preparing Lists for Campaigns

The gap between a raw scraped list and a campaign-ready list is where most outreach campaigns either succeed or fail. A clean list converts. A dirty list wastes resources and risks account restrictions.

The Cleaning Pipeline

Apply these steps to transform raw data into a high-quality outreach list:

  1. Remove bots: Filter out any user where user.bot == True. This is the simplest and highest-impact cleaning step.
  2. Remove users without usernames: Users who have not set a public username cannot receive DMs from non-contacts. They are unreachable for outreach, so keeping them in your list inflates your count without adding reachable targets.
  3. Filter by activity status: Remove users whose last-seen status indicates prolonged inactivity ("last month" or "long time ago"). These accounts are either abandoned or belong to users who check Telegram infrequently enough that your DM will be buried.
  4. Apply keyword filters: If you collected bio data, filter for users whose bios match your target keywords. This step typically reduces list size by 60-80% while increasing per-user conversion probability by 3-5x.
  5. Deduplicate: Remove duplicate entries by user ID. This is mandatory if you scraped multiple groups.
  6. Exclude previous campaign recipients: If you have run prior campaigns, maintain a master exclusion list of user IDs you have already messaged. Never send the same outreach message to someone twice.
  7. Segment by quality: Divide your clean list into tiers. Tier 1: keyword-matched, recently active, with profile photos (highest quality). Tier 2: active but no keyword match. Tier 3: everyone else. Run your best message copy against Tier 1 first, iterate based on results, then expand to lower tiers.

Export Formats

The standard export format for Telegram member lists is CSV with columns for user ID, username, first name, last name, bio, activity status, and source group. CSV works universally across spreadsheet tools, databases, and DM platforms. If you are feeding data into an API, JSON arrays of user objects are more convenient. Floqal's platform accepts both formats, and its built-in scraper skips the export/import step entirely.

Data Validation Before Campaign Launch

Before launching any DM campaign from a scraped list, validate a random sample of 20-30 entries manually. Open those usernames in Telegram and verify: Do the accounts exist? Are they real people (not bots you missed)? Do their profiles match the keywords you filtered for? Are their privacy settings compatible with receiving DMs? This 10-minute sanity check catches systematic errors in your scraping or filtering pipeline that could waste an entire campaign.

Using Scraped Lists with Mass DMs

Scraping produces the target list. Mass DMs turn that list into subscribers, customers, or leads. The connection between scraping quality and DM performance is direct: a precisely filtered list of 1,000 users will outperform a random list of 10,000 in every metric that matters.

From List to Campaign

A practical workflow for turning a scraped list into a running campaign:

  1. Import your cleaned list into your DM tool. If using Floqal, the built-in scraper feeds directly into the campaign builder with no import step.
  2. Write 3-5 message variants. Each variant should communicate the same core value proposition with different wording, tone, or opening lines. This enables split testing.
  3. Set sending parameters: messages per hour, delay between messages, daily caps, and account rotation schedule. Conservative settings (15-25 messages per hour per account) protect account health.
  4. Launch a small test batch. Send the first 100-200 messages and measure: delivery rate, read rate (if trackable), response rate, and subscription/conversion rate. Identify the winning message variant.
  5. Scale the winning variant. Once you have a statistically meaningful winner from your test batch, roll it out to the full list. Continue monitoring for changes in response rate that might signal list fatigue or account issues.

Message Personalization from Scraped Data

Scraped data enables personalization beyond first-name tokens. If you know which group a user was scraped from, reference it: "I noticed you are a member of [Group Name]." If you have their bio, reference their stated interest: "Saw you are into DeFi." This level of specificity signals that the message is not a broadcast, it was written for them. Personalized DMs consistently outperform generic ones by 2-4x in response rate. For a deep walkthrough of DM strategy, see our complete guide to Telegram Mass DMs.

Tracking and Attribution

Track which scraped segments produce the best results. If you scraped five groups and one consistently delivers higher conversion rates, that group's audience is a better match for your offer. Double down on similar groups. If a keyword filter ("trader" in bio) outperforms another ("investor" in bio), refine your targeting. This feedback loop between scraping filters and campaign performance is how outreach campaigns improve over time. You can track your channel's subscriber trend and engagement changes using Floqal Analytics to correlate scraping sources with actual outcomes.

Legal and Ethical Considerations

Telegram group scraping exists in a legal gray area that varies by jurisdiction. Understanding the boundaries protects your accounts, your business, and your reputation.

What the Law Says

In the European Union, GDPR classifies usernames and display names as personal data when they can identify an individual. Processing this data requires a lawful basis. The most commonly cited basis for B2B outreach is "legitimate interest," but this requires a balancing test: your commercial interest in contacting the user must not override their reasonable expectation of privacy. In practice, this means targeting must be relevant, messaging must be transparent about its purpose, and recipients must have a clear way to opt out.

In the United States, there is no federal equivalent to GDPR for general personal data processing. The CAN-SPAM Act governs commercial email but does not directly cover Telegram DMs. State-level laws like CCPA (California) impose data handling requirements if you collect and store personal information from California residents. The absence of a clear legal framework does not mean anything goes; it means you need to apply reasonable standards and consult legal counsel if operating at scale.

Telegram's Terms of Service

Telegram's Terms of Service prohibit using the platform for spam and unsolicited mass messaging. The line between "outreach" and "spam" is defined by relevance and recipient experience. A personalized message to someone in a relevant group about a service that matches their stated interests is outreach. An identical generic message blasted to 50,000 random users is spam. The content, targeting, and volume of your campaigns determine which side of that line you fall on.

Ethical Best Practices

Risk Mitigation

Separate your scraping and sending infrastructure from your primary Telegram accounts. Use dedicated accounts for API operations, and do not run scraping tools from accounts that contain important personal conversations or business communications. If an account gets restricted, you want it to be a replaceable operations account, not your main presence on the platform.

Getting Started with Floqal's Built-In Scraping

Everything described in this guide, from member extraction and bio enrichment to keyword filtering, deduplication, and DM campaign execution, is available as a single integrated workflow in Floqal's Mass DMs platform.

What Floqal Handles Automatically

From Scrape to Send in Minutes

The typical workflow on Floqal takes five minutes from start to running campaign:

  1. Enter target group usernames
  2. Set your keyword filters and activity requirements
  3. Review the filtered list (counts, preview of matched users)
  4. Write your message variants (or use AI-powered generation)
  5. Set sending speed and daily limits
  6. Launch

No Python scripts. No CSV exports. No juggling between scraping tools and DM tools. Everything runs from a single interface with real-time tracking of delivery, reads, and responses.

Scaling Beyond Manual Scraping

Manual scraping with custom scripts works for occasional campaigns. For teams running outreach at scale, where you need to scrape dozens of groups, maintain exclusion lists across campaigns, rotate accounts, and track performance across different audience segments, the operational overhead of manual approaches scales quickly. Floqal's integrated approach keeps the complexity manageable as campaign volume increases. Check pricing for current plans, or sign in to your dashboard to start scraping immediately.

If you are building a Telegram channel alongside your outreach efforts, our complete channel scaling guide covers everything from profile optimization to cross-platform promotion strategies. And if you need to populate a new channel with content quickly, Channel Clone lets you replicate posts from any public channel into yours.

Frequently Asked Questions

Is it legal to scrape Telegram group members?

Scraping publicly available usernames and display names from Telegram groups is generally permissible, but you must comply with local data protection laws such as GDPR. Avoid collecting private data, always respect user privacy settings, and never store or sell scraped data as personal information databases.

How many members can you scrape from a Telegram group?

Telegram's API returns up to 10,000 members per group using the get_participants method. For larger groups, you can supplement with message sender scraping, which collects usernames from users who have posted messages. Rate limits apply, so pace your requests to avoid temporary bans.

What is the difference between member scraping and message sender scraping?

Member scraping pulls the full participant list of a group, including silent members. Message sender scraping extracts usernames only from users who have actually posted messages. Sender scraping returns more active users but misses lurkers, while member scraping gives you the complete picture.

Can you scrape members from a private Telegram group?

You can only scrape members from groups you have joined. If the group is private but you are a member, the API still allows participant extraction. You cannot scrape groups you have not joined, regardless of whether they are public or private.

What tools can you use to scrape Telegram groups without coding?

Several Chrome extensions and desktop applications offer GUI-based Telegram scraping without writing code. Floqal's Mass DMs tool includes built-in group scraping with keyword filtering, activity detection, and deduplication, so you can extract, filter, and message members from a single dashboard.

Ready to Start Scraping?

Floqal's Mass DMs tool includes built-in group scraping with keyword filtering, activity detection, and deduplication. Extract, filter, and message from a single dashboard.

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