Real-World Use Cases

Telebot Creator Documentation — Platform v7.1.2 · Telegram Bot API 10.1

This section demonstrates how to apply Telebot Creator's features and libraries in real-world scenarios. By combining workflows, advanced commands, and external integrations, you can create bots that solve practical problems and enhance user engagement.

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8.1 Referral System

Overview

A referral system tracks users who invite others to the bot and rewards them with points or other incentives. This use case involves:

1. Generating unique referral links.

2. Tracking referrals.

3. Rewarding users based on their referral count.

4. Displaying leaderboards for top referrers.

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Implementation

Step 1: Generate Unique Referral Links

In the /start command, include the user’s ID as a parameter to generate a referral link:

bot.sendMessage(f"Invite your friends using this link: t.me/{bot.username}?start={u}")

Step 2: Track Referrals

In the /start command, check if a referral ID is provided:

referrer_id = params
if referrer_id:
    referrer_points = libs.Resources.userRes("points", referrer_id)
    referrer_points.add(10)
    bot.sendMessage(f"User {referrer_id} has earned 10 points for referring you!")

Step 3: Reward Users

Track and display referral rewards dynamically:

user_points = libs.Resources.userRes("points", u)
bot.sendMessage(f"You have {user_points.value()} points!")

Step 4: Create a Leaderboard

Display the top referrers using libs.Resources:

top_referrers = libs.Resources.userRes("points").getAllData(5)
leaderboard = "\n".join([f"{i+1}. User {entry['user']}: {entry['value']} points" for i, entry in enumerate(top_referrers)])
bot.sendMessage(f"Top Referrers:\n{leaderboard}")

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8.2 Payment Automation Bot

Overview

This bot automates payment handling using the libs.Coinbase library. It can:

1. Generate payment requests.

2. Confirm payment status.

3. Notify users of successful payments.

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Implementation

Step 1: Set Up Coinbase Client

Configure the Coinbase client with your API keys:

libs.Coinbase.setKeys("your_api_key", "your_api_secret")
client = libs.Coinbase.post()

Step 2: Generate Payment Requests

Request payment for specific amounts:

payment_details = client.createCharge({
    "name": "Subscription Payment",
    "description": "Monthly subscription fee",
    "local_price": {"amount": "10.00", "currency": "USD"},
    "pricing_type": "fixed_price"
})
bot.sendMessage(f"Please make your payment here: {payment_details['hosted_url']}")

Step 3: Verify Payment Status

Check payment status using the charge ID:

charge_id = "charge_id_from_payment"
status = client.retrieveCharge(charge_id)['status']
if status == "CONFIRMED":
    bot.sendMessage("Payment confirmed! Thank you!")
else:
    bot.sendMessage(f"Payment status: {status}")

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8.3 Survey and Data Collection Bot

Overview

This bot collects user input for surveys or forms and stores the data in a CSV file for easy analysis.

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Implementation

Step 1: Collect User Responses

Ask users a series of questions:

bot.sendMessage("What is your name?")
Bot.handleNextCommand("get_name")

Store the responses:

name = msg
User.saveData("name", name)
bot.sendMessage("What is your email?")
Bot.handleNextCommand("get_email")

Step 2: Save Data to CSV

Save the collected data into a CSV file using libs.CSV:

csv_handler = libs.CSV.CSVHandler("survey_data.csv")
csv_handler.create_csv(["Name", "Email"])
csv_handler.add_row({"Name": User.getData("name"), "Email": User.getData("email")})
bot.sendMessage("Your responses have been saved. Thank you!")

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8.4 Crypto Airdrop Bot

Overview

This bot automates cryptocurrency distributions using libs.web3lib (supports all EVM chains: Ethereum, Polygon, Arbitrum, BSC, etc.).

Note: libs.Polygon, libs.ARB, libs.TTcoin, and libs.Tomochain are deprecated. Use libs.web3lib for all EVM blockchain operations.

---

Implementation

Step 1: Send Tokens

result = libs.web3lib.sendETHER(
    private_key="your_private_key",
    to="0xRecipientAddress",
    value=0.01,
    chain="polygon"
)
bot.sendMessage(f"Transaction sent: {result}")

Step 2: Automate Multiple Transfers

recipients = [
    {"address": "0xRecipient1", "amount": 0.01},
    {"address": "0xRecipient2", "amount": 0.015}
]

for recipient in recipients:
    result = libs.web3lib.sendETHER(
        private_key="your_private_key",
        to=recipient["address"],
        value=recipient["amount"],
        chain="polygon"
    )
    bot.sendMessage(f"Sent {recipient['amount']} to {recipient['address']}")

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8.5 Real-Time Notification Bot

Overview

This bot uses libs.Webhook to send real-time updates based on external events, such as sales or user actions.

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Implementation

Step 1: Generate Webhook URL

Generate a webhook URL for notifications:

webhook_url = libs.Webhook.getUrlFor("send_notification", user_id=12345)
bot.sendMessage(f"Webhook URL: {webhook_url}")

Step 2: Process Webhook Events

Handle incoming webhook events in a command:

bot.sendMessage("You have a new sale! Congratulations!")

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8.6 Event Management Bot

Overview

This bot manages events, allowing users to RSVP, receive reminders, and track attendance.

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Implementation

Step 1: RSVP System

Allow users to RSVP to an event:

bot.sendMessage("Would you like to attend the event? Reply with 'Yes' or 'No'.")
Bot.handleNextCommand("process_rsvp")

Store responses:

response = msg
if response.lower() == "yes":
    User.saveData("RSVP", "Yes")
    bot.sendMessage("Thank you for RSVPing!")
else:
    bot.sendMessage("Maybe next time!")

Step 2: Event Reminders

Send reminders using runCommandAfter:

Bot.runCommandAfter(3600, "send_event_reminder")

In the reminder command:

bot.sendMessage("Reminder: The event starts in 1 hour!")

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8.7 AI Chatbot

Overview

Build a GPT-powered conversational bot using libs.openai_lib or libs.gemini_lib.

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Implementation

Command: * (Wildcard — handles all user messages)

client = libs.openai_lib.OpenAIClient(api_key="YOUR_OPENAI_KEY")

# Get conversation history (or start fresh)
history = User.getData("chat_history")
if not history:
    history = []

# Add user message to history
history.append({"role": "user", "content": msg})

# Get AI response
response = client.create_chat_completion(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
    ] + history[-10:]  # Keep last 10 messages for context
)

ai_reply = response["choices"][0]["message"]["content"]

# Save updated history
history.append({"role": "assistant", "content": ai_reply})
User.saveData("chat_history", history[-20:])  # Keep last 20 messages

bot.sendMessage(ai_reply)

This creates a full conversational AI bot that remembers context across messages.

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8.8 Tips and Best Practices

1. Optimize Point Usage:

   • Combine commands where possible.

   • Use wildcards (*) for unstructured messages to reduce redundant commands.

2. Handle Large User Bases:

   • Use in-built broadcasting strategies.

   • Target active users only.

3. Secure Data:

   • Encrypt sensitive user data.

   • Use HTTPS webhooks for secure communication.

4. Use Error Handling:

   • Wrap external API calls in try-except blocks.

   • Log errors for debugging.

5. Use the Account Class:

   • Share data across all your bots with Account.saveData(name, data) and Account.getData(name) (up to 10MB per key).

   • Monitor active users with Account.getStats(time_frames=["24h", "7d"]).

   • Manage bots and commands programmatically — Account.create_bot, Account.clone_bot, Account.create_command, Account.export_bot / Account.import_commands, and block users with Account.blockUser / Account.unblockUser.