Telegram has become one of the most popular messaging platforms, with millions of active channels sharing everything from news updates to cryptocurrency signals, educational content, and community announcements. As developers and data engineers, we often need to archive, analyze, or monitor these channels programmatically. Whether you're building analytics dashboards, content moderation systems, or research archives, understanding how to efficiently retrieve Telegram channel data is essential.

In this comprehensive guide, I'll walk you through everything you need to know about fetching historical posts from Telegram channels and monitoring new content in real-time. We'll explore both the MTProto Client API and the Bot API, complete with production-ready code examples and best practices learned from real-world implementations.

Ready-to-Use Implementation: If you want to get started quickly, check out my open-source TelegramChannelMessageScraper – a production-ready tool that implements the techniques described in this guide.

Why Retrieve Telegram Channel Data?

Before diving into the technical details, let's explore some common use cases:

Research & Archiving: Academic researchers and journalists often need to preserve complete channel histories for analysis, studying information dissemination patterns, or documenting important events.

Analytics & Business Intelligence: Companies monitor competitor channels, track engagement metrics, analyze content performance, and gather market intelligence from public channels.

Content Moderation: Automated systems can scan channels for policy violations, inappropriate content, or spam detection using keyword matching and AI-powered analysis.

Alert Systems: Real-time monitoring enables instant notifications when specific keywords appear, critical updates are posted, or certain conditions are met.

Cross-platform Integration: Automatically cross-post content from Telegram to other platforms like Discord, Twitter, or your own website.

Understanding Telegram's API Landscape

Telegram offers two distinct API families, each with different capabilities and trade-offs:

MTProto Client API

The MTProto API is Telegram's native protocol, offering the most comprehensive access to platform features. Popular Python libraries implementing this protocol include:

• Telethon: Mature, feature-rich, with excellent documentation

• Pyrogram: Modern, elegant API design with strong type hints

• TDLib: Official library by Telegram, available for multiple languages

Key Capabilities:

• ✅ Full access to complete channel history (all messages ever posted)

• ✅ Join public channels via username (e.g., @channelname)

• ✅ Join private channels using invite links

• ✅ Rich event system for real-time monitoring

• ❌ Requires user account authentication (phone number verification)

• ❌ More complex setup and session management

Bot API

The Bot API is a simplified HTTP-based interface designed specifically for bot applications.

Key Capabilities:

• ✅ Simple HTTP requests (no complex protocol implementation)

• ✅ Easy webhook integration for real-time updates

• ✅ Straightforward token-based authentication

• ❌ No historical access – bots only see messages posted after they join

• ❌ Requires bot to be added as a channel administrator

• ❌ Limited to real-time monitoring only

Critical Limitation: The Bot API's most significant constraint is that it cannot retrieve messages posted before the bot joined the channel. If you need complete historical data, MTProto is your only option.

Authentication & Setup

MTProto Setup (Telethon/Pyrogram)

Step 1: Register Your Application

Visit my.telegram.org/auth and log in with your phone number. Navigate to "API Development Tools" and create a new application. You'll receive:

• api_id: An integer identifier (e.g., 12345678)

• api_hash: A string hash (e.g., "abcdef1234567890abcdef1234567890")

Step 2: Initialize the Client

from telethon import TelegramClient

# Your API credentials
api_id = 12345678
api_hash = "your_api_hash_here"

# Create client with session name (stores authentication)
client = TelegramClient('session_name', api_id, api_hash)

# Start the client (will prompt for phone number first time)
await client.start()

The first time you run this code, Telethon will:

1. Ask for your phone number

2. Send you a verification code via Telegram

3. Create a session file (session_name.session) storing your authentication

Once authenticated, the session file allows you to reconnect without re-entering credentials.

Step 3: Join Target Channels

For public channels:

# Join by username
await client.join_channel('@channelname')

For private channels (requires an invite link):

from telethon.tl.functions.messages import ImportChatInviteRequest

# Extract hash from invite link: t.me/+DKcwQbX3QRphMjFk
invite_hash = 'DKcwQbX3QRphMjFk'
await client(ImportChatInviteRequest(invite_hash))

Bot API Setup

Step 1: Create a Bot

1. Open Telegram and search for @BotFather

2. Send /newbot command

3. Follow the prompts to name your bot

4. Receive your HTTP API token: 123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11

Step 2: Add Bot as Channel Admin

1. Open your target channel's settings

2. Add the bot as an administrator

3. Grant these permissions:

   • Read Messages (essential for receiving updates)

   • Post Messages (optional, for automated posting)

   • Edit Messages (optional)

   • Delete Messages (optional)

Step 3: Configure Webhook or Polling

Choose between two update delivery methods:

Option A: Webhooks (recommended for production)

import requests

TOKEN = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
WEBHOOK_URL = "https://your-domain.com/webhook"

response = requests.post(
    f"https://api.telegram.org/bot{TOKEN}/setWebhook",
    json={
        "url": WEBHOOK_URL,
        "allowed_updates": ["channel_post"],
        "secret_token": "your_secret_token_here"  # For security
    }
)

Option B: Long Polling (simpler for development)

import requests

TOKEN = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
offset = 0

while True:
    response = requests.get(
        f"https://api.telegram.org/bot{TOKEN}/getUpdates",
        params={
            "offset": offset,
            "timeout": 30,
            "allowed_updates": ["channel_post"]
        }
    )

    updates = response.json()["result"]
    for update in updates:
        if "channel_post" in update:
            process_post(update["channel_post"])
        offset = update["update_id"] + 1

Retrieving Historical Posts (MTProto Only)

One of the most powerful features of the MTProto API is the ability to fetch complete channel history. This is accomplished primarily through the messages.getHistory method.

Understanding Pagination

Telegram returns messages in batches (up to 100 per request). To retrieve all messages, you need to paginate through the history:

Key Parameters:

• peer: The channel entity or identifier

• limit: Number of messages per request (max 100)

• offset_id: Message ID to start from (0 for most recent)

• min_id: Only return messages with ID greater than this

• max_id: Only return messages with ID less than this

Telethon Implementation: Automatic Pagination

Telethon provides an elegant iterator that handles pagination automatically:

from telethon import TelegramClient
from telethon.tl.functions.messages import ImportChatInviteRequest
import asyncio

async def fetch_all_messages():
    api_id = 12345678
    api_hash = "your_api_hash_here"

    client = TelegramClient('session_name', api_id, api_hash)
    await client.start()

    # Join private channel if needed
    try:
        invite_hash = 'DKcwQbX3QRphMjFk'
        await client(ImportChatInviteRequest(invite_hash))
        print("Successfully joined channel")
    except Exception as e:
        print(f"Already in channel or error: {e}")

    # Fetch all messages (oldest first)
    channel_entity = await client.get_entity('t.me/+DKcwQbX3QRphMjFk')
    message_count = 0

    async for message in client.iter_messages(
        channel_entity,
        reverse=True,  # Start from oldest
        limit=None     # Fetch all messages
    ):
        message_count += 1

        # Extract message data
        message_data = {
            'id': message.id,
            'date': message.date,
            'text': message.text,
            'views': message.views,
            'forwards': message.forwards,
            'author': message.post_author,
            'has_media': message.media is not None
        }

        # Process message (save to database, analyze, etc.)
        print(f"Message {message.id}: {message.text[:50] if message.text else '[Media]'}...")

        # Handle rate limiting gracefully
        if message_count % 100 == 0:
            print(f"Processed {message_count} messages...")
            await asyncio.sleep(1)  # Brief pause to respect rate limits

    print(f"Total messages fetched: {message_count}")
    await client.disconnect()

# Run the async function
asyncio.run(fetch_all_messages())

Manual Pagination for Advanced Control

For more granular control over the pagination process (useful for incremental updates or resuming interrupted fetches):

from telethon import TelegramClient
from telethon.tl.functions.messages import GetHistoryRequest
from telethon.errors import FloodWaitError
import asyncio

async def manual_pagination():
    client = TelegramClient('session_name', api_id, api_hash)
    await client.start()

    channel = await client.get_entity('t.me/+DKcwQbX3QRphMjFk')
    offset_id = 0
    limit = 100
    all_messages = []

    while True:
        try:
            history = await client(GetHistoryRequest(
                peer=channel,
                offset_id=offset_id,
                offset_date=None,
                add_offset=0,
                limit=limit,
                max_id=0,
                min_id=0,
                hash=0
            ))

            if not history.messages:
                break  # No more messages

            all_messages.extend(history.messages)
            offset_id = history.messages[-1].id

            print(f"Fetched {len(history.messages)} messages, total: {len(all_messages)}")

            # Save progress for resumability
            with open('last_offset_id.txt', 'w') as f:
                f.write(str(offset_id))

            await asyncio.sleep(1)  # Rate limiting

        except FloodWaitError as e:
            print(f"Rate limited. Waiting {e.seconds} seconds...")
            await asyncio.sleep(e.seconds)

    return all_messages

Handling Media Files

When messages contain media (photos, videos, documents), you can download them:

async for message in client.iter_messages(channel):
    if message.photo:
        # Download photo
        filename = f"photo_{message.id}.jpg"
        await client.download_media(message.photo, filename)

    elif message.document:
        # Download document/video
        filename = f"doc_{message.id}_{message.document.attributes[0].file_name}"
        await client.download_media(message.document, filename)

    elif message.video:
        filename = f"video_{message.id}.mp4"
        await client.download_media(message.video, filename)

Real-time Monitoring

MTProto Event-Based Monitoring

Telethon's event system provides the most elegant solution for real-time monitoring:

from telethon import TelegramClient, events
import asyncio

api_id = 12345678
api_hash = "your_api_hash_here"
client = TelegramClient('session_name', api_id, api_hash)

# Handler for new messages
@client.on(events.NewMessage(chats=['t.me/+DKcwQbX3QRphMjFk']))
async def new_message_handler(event):
    message = event.message

    print(f"📨 New post in channel!")
    print(f"   ID: {message.id}")
    print(f"   Date: {message.date}")
    print(f"   Text: {message.text[:100] if message.text else '[Media only]'}")
    print(f"   Views: {message.views}")

    # Your processing logic
    await process_new_message(message)

# Handler for edited messages
@client.on(events.MessageEdited(chats=['t.me/+DKcwQbX3QRphMjFk']))
async def edited_message_handler(event):
    message = event.message
    print(f"✏️ Message {message.id} was edited")
    await process_edited_message(message)

async def process_new_message(message):
    # Example: Save to database
    # Example: Send notification
    # Example: Trigger webhook
    # Example: Analyze content with AI
    pass

async def main():
    await client.start()
    print("🔄 Monitoring started. Press Ctrl+C to stop.")
    await client.run_until_disconnected()

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

Bot API Webhook Implementation

For production systems, webhooks provide the most efficient real-time monitoring:

from flask import Flask, request, jsonify
import hmac
import hashlib

app = Flask(__name__)

BOT_TOKEN = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
SECRET_TOKEN = "your_secret_token_here"

@app.route('/webhook', methods=['POST'])
def webhook_handler():
    # Verify request authenticity
    telegram_secret = request.headers.get('X-Telegram-Bot-Api-Secret-Token')
    if telegram_secret != SECRET_TOKEN:
        return jsonify({'error': 'Unauthorized'}), 401

    # Parse update
    update = request.get_json()

    # Process channel posts only
    if 'channel_post' in update:
        channel_post = update['channel_post']

        message_data = {
            'message_id': channel_post['message_id'],
            'chat_id': channel_post['chat']['id'],
            'chat_title': channel_post['chat']['title'],
            'date': channel_post['date'],
            'text': channel_post.get('text', ''),
        }

        # Handle media
        if 'photo' in channel_post:
            message_data['media_type'] = 'photo'
            message_data['file_id'] = channel_post['photo'][-1]['file_id']

        elif 'video' in channel_post:
            message_data['media_type'] = 'video'
            message_data['file_id'] = channel_post['video']['file_id']

        elif 'document' in channel_post:
            message_data['media_type'] = 'document'
            message_data['file_id'] = channel_post['document']['file_id']

        # Process the message
        process_channel_post(message_data)

        print(f"✅ Processed message {message_data['message_id']}")

    return jsonify({'ok': True})

def process_channel_post(data):
    # Your business logic here
    # Examples:
    # - Save to database
    # - Send to message queue
    # - Trigger notifications
    # - Analyze with AI
    # - Forward to other channels
    pass

if __name__ == '__main__':
    # In production, use a proper WSGI server like Gunicorn
    app.run(host='0.0.0.0', port=8080)

Deployment Considerations:

• Host on a server with HTTPS (required by Telegram)

• Use platforms like Cloudflare Workers, Vercel, AWS Lambda

• Implement request signature verification for security

• Handle duplicate updates (store processed message IDs)

Bot API Long Polling (Alternative)

For simpler deployments or testing:

import requests
import time

TOKEN = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
offset = 0

def get_updates():
    global offset

    while True:
        try:
            response = requests.get(
                f"https://api.telegram.org/bot{TOKEN}/getUpdates",
                params={
                    "offset": offset,
                    "timeout": 30,  # Long polling timeout
                    "allowed_updates": ["channel_post"]
                },
                timeout=35
            )

            data = response.json()

            if not data["ok"]:
                print(f"❌ Error: {data}")
                time.sleep(5)
                continue

            updates = data["result"]

            for update in updates:
                if "channel_post" in update:
                    process_channel_post(update["channel_post"])

                offset = update["update_id"] + 1

                # Persist offset for resumability
                with open('offset.txt', 'w') as f:
                    f.write(str(offset))

        except requests.exceptions.RequestException as e:
            print(f"⚠️ Connection error: {e}")
            time.sleep(5)
        except KeyboardInterrupt:
            print("\n👋 Stopping...")
            break

def process_channel_post(post):
    print(f"📬 New post: {post.get('text', '[Media]')[:50]}")
    # Your processing logic

if __name__ == '__main__':
    # Load last offset if exists
    try:
        with open('offset.txt', 'r') as f:
            offset = int(f.read())
    except FileNotFoundError:
        offset = 0

    print("🔄 Starting long polling...")
    get_updates()

No-Code Solution: n8n Integration

For non-developers or rapid prototyping, n8n offers visual workflow automation with Telegram integration.

Setup Process:

1. Create Bot: Use @BotFather to create a bot and get a token

2. Add to Channel: Make the bot an admin in your target channel

3. Configure n8n:

   • Add a Telegram Trigger node

   • Select the "Channel Post" event type

   • Enter your bot token

4. Build Workflow: Connect processing nodes

Example Workflows:

Content Archive to Database:

Telegram Trigger → Extract Text/Media → PostgreSQL Node

Cross-platform Posting:

Telegram Trigger → Filter (has media) → Twitter API Node

AI-Powered Analysis:

Telegram Trigger → OpenAI Node (sentiment analysis) → Email Alert

Keyword Monitoring:

Telegram Trigger → IF Node (keyword match) → Slack/Discord Webhook

n8n's visual interface makes it easy to build complex automation without code, perfect for business users or quick prototyping before building custom solutions.

Rate Limits & Best Practices

Understanding Rate Limits

Bot API:

• Global rate: ~30 requests per second

• Channel messages: ~20 messages per minute

• Update retention: Maximum 24 hours

MTProto:

• Variable rate limits based on account age and behavior

• FloodWaitError indicates rate limit hit

• Typical backoff: 10-300 seconds

Handling FLOOD_WAIT Errors

from telethon.errors import FloodWaitError
import asyncio

async def fetch_with_retry(client, entity):
    retries = 0
    max_retries = 5

    while retries < max_retries:
        try:
            messages = await client.get_messages(entity, limit=100)
            return messages

        except FloodWaitError as e:
            wait_time = e.seconds
            print(f"⏳ Rate limited. Waiting {wait_time} seconds...")

            # Exponential backoff
            wait_time = min(wait_time * (2 ** retries), 300)
            await asyncio.sleep(wait_time)
            retries += 1

    raise Exception("Max retries exceeded")

Production Best Practices

1. Data Pipeline Design

import sqlite3
from datetime import datetime

def save_message(message):
    """Idempotent message storage"""
    conn = sqlite3.connect('messages.db')
    cursor = conn.cursor()

    # Create table if not exists
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS messages (
            chat_id INTEGER,
            message_id INTEGER,
            date TIMESTAMP,
            text TEXT,
            views INTEGER,
            PRIMARY KEY (chat_id, message_id)
        )
    ''')

    # Insert or ignore (prevents duplicates)
    cursor.execute('''
        INSERT OR IGNORE INTO messages 
        (chat_id, message_id, date, text, views)
        VALUES (?, ?, ?, ?, ?)
    ''', (
        message.chat_id,
        message.id,
        message.date,
        message.text,
        message.views
    ))

    conn.commit()
    conn.close()

2. Incremental Updates

def get_last_message_id():
    """Resume from last processed message"""
    try:
        conn = sqlite3.connect('messages.db')
        cursor = conn.cursor()
        cursor.execute('SELECT MAX(message_id) FROM messages WHERE chat_id = ?', (chat_id,))
        result = cursor.fetchone()
        return result[0] if result[0] else 0
    finally:
        conn.close()

async def incremental_fetch(client, channel):
    """Only fetch new messages since last run"""
    last_id = get_last_message_id()

    async for message in client.iter_messages(channel, min_id=last_id):
        save_message(message)

3. Reliability & Error Handling

import logging
from typing import Optional

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('telegram_monitor.log'),
        logging.StreamHandler()
    ]
)

async def safe_message_processor(message):
    """Process message with comprehensive error handling"""
    try:
        # Attempt processing
        await process_message(message)
        logging.info(f"✅ Processed message {message.id}")

    except FloodWaitError as e:
        logging.warning(f"⏳ Rate limited: {e.seconds}s")
        raise  # Re-raise to trigger retry logic

    except Exception as e:
        logging.error(f"❌ Error processing {message.id}: {e}")
        # Log to error tracking (Sentry, etc.)
        # Continue processing other messages

4. Compliance & Privacy

def respect_privacy(channel):
    """Check if scraping is appropriate"""
    # Respect channel privacy settings
    if channel.restricted:
        logging.warning("Channel has restrictions, aborting")
        return False

    # Only process public channels or channels you admin
    if not channel.broadcast and not channel.creator:
        logging.warning("Not authorized to scrape this channel")
        return False

    return True

5. Monitoring & Observability

import time
from prometheus_client import Counter, Histogram

messages_processed = Counter(
    'telegram_messages_processed_total',
    'Total messages processed',
    ['channel']
)

processing_time = Histogram(
    'telegram_message_processing_seconds',
    'Time spent processing messages'
)

@processing_time.time()
async def monitored_process(message):
    """Process with metrics"""
    await process_message(message)
    messages_processed.labels(channel=message.chat_id).inc()

API Comparison & Decision Guide

When to Use MTProto (Telethon/Pyrogram)

Choose MTProto if you need:

• ✅ Complete historical data (messages from before your bot joined)

• ✅ Rich user data and advanced features

• ✅ More control over message handling

• ✅ Ability to act as a user account

Be prepared for:

• ⚠️ Complex authentication (phone verification, session management)

• ⚠️ Rate-limiting challenges (FLOOD_WAIT errors)

• ⚠️ More code complexity

Best for: Research projects, data archiving, analytics platforms, comprehensive monitoring systems

When to Use Bot API

Choose Bot API if you need:

• ✅ Simple real-time monitoring only

• ✅ Easy HTTP-based integration

• ✅ Webhook support for serverless deployment

• ✅ Quick setup with minimal code

Be prepared for:

• ⚠️ No access to historical messages

• ⚠️ Bot must be channel admin

• ⚠️ Limited to messages posted after bot joins

Best for: Real-time alerts, notification systems, simple monitoring, serverless applications

Quick Decision Matrix

Complete Production Example

Here's a production-ready implementation combining best practices:

import asyncio
import logging
import sqlite3
from datetime import datetime
from telethon import TelegramClient, events
from telethon.errors import FloodWaitError, SessionPasswordNeededError
from telethon.tl.functions.messages import ImportChatInviteRequest

# Configuration
API_ID = 12345678
API_HASH = "your_api_hash"
CHANNEL_URL = "t.me/+DKcwQbX3QRphMjFk"
DB_PATH = "telegram_archive.db"

# Logging setup
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('telegram_monitor.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

class TelegramArchiver:
    def __init__(self, api_id, api_hash, db_path):
        self.client = TelegramClient('archiver_session', api_id, api_hash)
        self.db_path = db_path
        self.setup_database()

    def setup_database(self):
        """Initialize database schema"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        cursor.execute('''
            CREATE TABLE IF NOT EXISTS messages (
                chat_id INTEGER,
                message_id INTEGER,
                date TIMESTAMP,
                text TEXT,
                views INTEGER,
                forwards INTEGER,
                author TEXT,
                has_media BOOLEAN,
                media_type TEXT,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                PRIMARY KEY (chat_id, message_id)
            )
        ''')

        cursor.execute('''
            CREATE INDEX IF NOT EXISTS idx_date 
            ON messages(date)
        ''')

        cursor.execute('''
            CREATE INDEX IF NOT EXISTS idx_chat_id 
            ON messages(chat_id)
        ''')

        conn.commit()
        conn.close()
        logger.info("✅ Database initialized")

    def save_message(self, message):
        """Save message to database (idempotent)"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()

            cursor.execute('''
                INSERT OR REPLACE INTO messages 
                (chat_id, message_id, date, text, views, forwards, 
                 author, has_media, media_type)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
            ''', (
                message.chat_id,
                message.id,
                message.date,
                message.text or '',
                message.views or 0,
                message.forwards or 0,
                message.post_author or '',
                message.media is not None,
                type(message.media).__name__ if message.media else None
            ))

            conn.commit()
            return True
        except Exception as e:
            logger.error(f"❌ Error saving message {message.id}: {e}")
            return False
        finally:
            conn.close()

    def get_last_message_id(self, chat_id):
        """Get last processed message ID for incremental updates"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            cursor.execute(
                'SELECT MAX(message_id) FROM messages WHERE chat_id = ?',
                (chat_id,)
            )
            result = cursor.fetchone()
            return result[0] if result[0] else 0
        finally:
            conn.close()

    async def backfill_history(self, channel_url):
        """Fetch complete channel history"""
        try:
            await self.client.start()
            logger.info("✅ Client started")

            # Join channel if needed
            if '+' in channel_url:
                invite_hash = channel_url.split('+')[1]
                try:
                    await self.client(ImportChatInviteRequest(invite_hash))
                    logger.info("✅ Joined channel")
                except Exception as e:
                    logger.info(f"Already in channel or error: {e}")

            # Get channel entity
            entity = await self.client.get_entity(channel_url)
            chat_id = entity.id

            # Get last processed message
            last_id = self.get_last_message_id(chat_id)
            logger.info(f"📊 Last processed message ID: {last_id}")

            # Fetch messages
            count = 0
            async for message in self.client.iter_messages(
                entity,
                reverse=True,
                min_id=last_id
            ):
                if self.save_message(message):
                    count += 1

                    if count % 100 == 0:
                        logger.info(f"📥 Processed {count} messages...")
                        await asyncio.sleep(1)  # Rate limiting

            logger.info(f"✅ Backfill complete. Total messages: {count}")

        except FloodWaitError as e:
            logger.warning(f"⏳ Rate limited: waiting {e.seconds}s")
            await asyncio.sleep(e.seconds)
            return await self.backfill_history(channel_url)

        except Exception as e:
            logger.error(f"❌ Backfill error: {e}")
            raise

    async def start_monitoring(self, channel_url):
        """Start real-time monitoring"""
        await self.client.start()

        entity = await self.client.get_entity(channel_url)

        @self.client.on(events.NewMessage(chats=entity))
        async def handler(event):
            message = event.message
            if self.save_message(message):
                logger.info(f"📨 New message {message.id}: {message.text[:50] if message.text else '[Media]'}")

        @self.client.on(events.MessageEdited(chats=entity))
        async def edit_handler(event):
            message = event.message
            if self.save_message(message):
                logger.info(f"✏️ Edited message {message.id}")

        logger.info("🔄 Real-time monitoring started")
        await self.client.run_until_disconnected()

async def main():
    archiver = TelegramArchiver(API_ID, API_HASH, DB_PATH)

    # First, backfill historical data
    logger.info("📥 Starting historical backfill...")
    await archiver.backfill_history(CHANNEL_URL)

    # Then start real-time monitoring
    logger.info("🔄 Starting real-time monitoring...")
    await archiver.start_monitoring(CHANNEL_URL)

if __name__ == '__main__':
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        logger.info("👋 Shutting down gracefully...")

Conclusion & Key Takeaways

Retrieving data from Telegram channels requires understanding the trade-offs between different API approaches:

For Complete Historical Access:

• Use MTProto (Telethon/Pyrogram)

• Accept the complexity of user authentication

• Implement robust error handling for rate limits

• Essential for research, archiving, and comprehensive analytics

For Simple Real-time Monitoring:

• Use Bot API with webhooks

• Keep it simple with HTTP requests

• Perfect for alerts and notifications

• Ideal for serverless deployments

Production Considerations:

1. Always persist state (last message ID, offset) for resumability

2. Implement idempotent storage to handle duplicate messages

3. Respect rate limits with exponential backoff

4. Monitor your systems with logging and metrics

5. Consider privacy and comply with Telegram's Terms of Service

Common Pitfalls to Avoid:

• ❌ Not handling FloodWaitError (will get your account rate limited)

• ❌ Assuming Bot API can access history (it cannot!)

• ❌ Not persisting authentication sessions (wastes API calls)

• ❌ Ignoring message duplicates (leads to data corruption)

• ❌ Scraping private channels without permission (violates ToS)

Additional Resources

Reference Implementation:

• TelegramChannelMessageScraper - Production-ready open-source implementation

Official Documentation:

• Telegram MTProto API

• messages.getHistory Method

• Telegram Bot API

• Bot API Updates

Python Libraries:

• Telethon Documentation

• Pyrogram Documentation

Tools & Platforms:

• n8n Telegram Integration