In this article, we’ll explore how to start building your own Telegram OSINT toolkit to gather information such as recent messages, group member lists, or basic profile lookups

Welcome back, aspiring cyberwarriors!

Today, a vast number of applications are published daily with the aid of artificial intelligence. However, OSINT tasks are often quite specific, and waiting for or searching for useful tools when you need to conduct an investigation is not very practical. Therefore, in this article, we’ll explore how to start building your own Telegram OSINT toolkit to gather information such as recent messages, group member lists, or basic profile lookups. Let’s get rolling!

Step #1: Understanding Telegram’s Data Landscape

To be an effective operative in the world of OSINT, you must first understand the terrain of the platform you are targeting. Telegram is a massive digital ecosystem containing billions of messages, channels, and user profiles, which makes it an indispensable resource for our work. While we have previously discussed basic intelligence gathering on this service, we now need to dive deeper into how Telegram organizes its data and how you can exploit those structures for automated collection.

You must first distinguish between the public and private parts of the platform. Public channels and groups are the “open books” of Telegram; they are accessible without any special invites and can be discovered easily using usernames, direct links, or the in-app search feature. Once you gain entry to a public space, every message, media file, and member list becomes visible to your tools, with no hidden archives to worry about. Conversely, private sections include one-on-one chats, secret conversations, and private groups that require an invite link or admin approval to join. These private areas are protected from the search function and generally remain off-limits for standard OSINT scraping.

When you begin writing your Python code, you will choose between two primary gateways to interact with this data: the Bot API and the MTProto API. The Bot API is the simpler, entry-level method based on standard HTTP requests, but it is severely restricted for deep intelligence work. Bots are often blind to message histories that existed before they joined a group and are choked by small file limits, such as a 20MB cap on downloads.

To gain powerful capabilities, you must use the MTProto API, which is the native protocol used by official Telegram clients. By utilizing Python libraries like Telethon, your script can act like a real user account rather than a limited bot. This grants you full access to entire message histories, the ability to handle massive files up to 2GB, and a direct connection to Telegram’s backbone for maximum data extraction.

However, do not mistake this access for a free-for-all, as Telegram maintains a defensive boundary of strict anti-spam rules. This protection displays as rate limits that vary based on how old your account is and how trustworthy you appear to their servers. If your script is too aggressive or your requests are too frequent, you risk being flagged or banned. As you move into the development stage of your OSINT tools, you must be careful with your requests.

Step #2: Environment Setup on Linux

Before you write a single line of code, you must prepare a workspace. For this project, we are utilizing Kali Linux and the Sublime Text editor, though you can use any environment where you feel most comfortable. Your first task is to open the terminal and create a dedicated directory for the project.

kali> mkdir Scripts/tg-osint

To prevent your project dependencies from conflicting with the rest of your system, you must activate a virtual environment. This keeps your local installation clean and ensures your OSINT toolkit has exactly what it needs to function without interference.

kali> python3 -m venv venv

kali> source venv/bin/activate

After isolating your environment, create folders for scripts, data, and logs to keep everything organized.
kali> mkdir -p modules output logs

Next, use the touch command to create your core files such as the main entry point, config, and utilities, along with .env and .gitignore files to keep sensitive data secure.

kali> touch main.py config.py utils.py client.py .env .gitignore

With your project structure ready, install the key Python libraries: Telethon for Telegram communication, Rich for clean terminal output, and Pillow with piexif for extracting EXIF metadata from images.

kali> pip install telethon python-dotenv rich aiosqlite pillow piexif click

Once installation is complete, create a requirements file to lock your setup and easily reproduce it on any machine.

pip freeze > requirements.txt

The final part of preparation involves securing your access to the Telegram backbone. You must navigate to the official Telegram API tools webpage and sign up to create a new application, which will grant you api_id and api_hash.

Once you have these, open your .env file and input your credentials, including your API ID, API hash, phone number, and a chosen session name, to authorize your connection to the servers.

API_ID=12345678

API_HASH=abcdef1234567890abcdef1234567890

PHONE=+1234567890

SESSION_NAME=hackers-arise-osint-session

Step #3: Establishing the Connection with Telethon

Once your environment is ready, you must bridge the gap between your local script and the Telegram backbone. Your first objective is to load the credentials from the .env file you prepared earlier. To accomplish this, you will build a configuration script that imports these values into your Python environment, ensuring your sensitive data remains isolated from your main logic.

With your credentials ready, you will construct a thin Telethon client module that serves as the core of your communication. This module initializes an asynchronous client instance. The start_client() function within this module handles the entire authentication handshake, including the retrieval of a login code sent to your device.

The moment you successfully authenticate, Telethon generates a necessary .session file in your directory, which functions similarly to a browser cookie. This file allows your script to skip the login flow in the future, giving you instant access to the target’s data on subsequent runs.

Before moving on to the actual data extraction, you must verify that your connection is stable and authorized. You will create a simple verification script, test_connection.py, to perform a quick handshake with the server and display your user identity.

To put your connection to the test, fire up your terminal and execute the following command.

kali> python test_connection.py

If everything is set up correctly, the terminal will display your account details and confirm that the client is connected.

Step #4: Developing Core OSINT Extraction Modules

Now that your client is working, you are ready to move into the heart of the task: building the specialized extraction modules. These scripts will reside in your modules directory, with each one designed to handle a specific category of intelligence gathering, from user profiles to hidden image metadata. To begin this phase of the development, you need to generate the necessary Python files for each module.

kali> touch modules/profile.py modules/members.py modules/messages.py modules/media.py

Your first objective is the user and channel profile lookup, a fundamental function that resolves a simple username or phone number into a full Telegram entity. A professional investigator needs clear data, so your workflow starts with a helper function, parse_last_seen, which translates Telegram’s raw status codes into human-readable intel, such as whether a target is currently online or was active within the last month.

This module is designed to extract names, bios, bot status, and even scam flags.

Also, to ensure the data is easy to analyze, you will use the Rich library to render these details into visually clean tables within your terminal.

Next, you move to group member scraping by developing the members.py module to extract the archives of public groups. Telegram allows you to fetch up to 200 members per request, so your script is built to handle data in chunks, processing them in a loop until your specified limit is reached or the list is exhausted. You must be aware of the limits: attempting to scrape private groups or massive channels with over 100,000 members will likely trigger a ChatAdminRequiredError.

To keep your investigation transparent, the script includes a live progress bar, allowing you to monitor the collection status in real-time.

The most valuable intelligence often hides within messages, where you can uncover the context of a target’s communications. So, the next module connects to a chat and iterates through the history to build a structured dataset of message IDs, sender information, and timestamps.

This tool will also record engagement metrics like view counts and identify high-value messages that have been pinned or forwarded. By capturing the metadata of forwarded messages, you can often trace the origin of a piece of intelligence back to its source.

The final piece of this toolkit focuses on media and hidden metadata. Using the Pillow and piexif libraries, our script will download image attachments and scan them for hidden EXIF data, such as device identifiers and GPS coordinates.

Because Telegram stores location data in the degrees-minutes-seconds (DMS) format, our module includes a conversion function to transform that data into decimal degrees. This allows us to generate direct Google Maps links.

Step #5: Data Storage and Persistent Infrastructure

As your datasets grow and your operation expands, relying solely on flat JSON files is a silly idea, because they are awkward to query and computationally expensive to reload. To handle intelligence professionally, we need to build a two-tier storage layer. We can utilize JSON for quick inspections and easy data portability, but we will implement SQLite for any information that requires repetitive filtering, complex joins, or cross-referencing across different targets.

All of your storage and logging logic will be consolidated within your utils.py file. We must first establish a logging system that records every move your script makes into a dedicated log file.

Your data persistence starts with setting up the database, where your collected data will be stored. Using the aiosqlite library, you’ll create an asynchronous connection to the database file and create the tables needed to organize the data.

We must also prepare specialized tables to store the specific relationships and communications we uncover during the scraping missions. The members table tracks the roster of individuals within a target group, recording their specific roles and usernames, while the messages table captures the actual content of communications, including message IDs, timestamps, and engagement metrics like view counts.

Step #6: Building the CLI Interface

Now that we have the extraction modules and storage system ready, we need one central tool to manage them. For this final step, we’ll use the Click library to build a command-line interface that handles command arguments, help messages, and input types. While some developers use the older argparse module, Click is often easier for beginners because it makes it simple to combine multiple commands into one clean and user-friendly tool.

At the foundation of our main.py script, we will define a root command group that acts as the primary entry point for our entire toolkit. Under this root group, we register specific subcommands for each of our tasks, such as fetching profiles, scraping members, or harvesting message histories.

Each command follows a similar process. When run, it starts an asynchronous workflow that sets up any needed storage, creates and starts a Telegram client session, and then calls the correct module to perform the task.

Step #7: Testing and Deployment

The first thing we usually do with a new tool is check its help screen. Our tool includes one too:

kali> python main.py –help

Once you confirm the interface is working, the next step is to run a profile lookup.

kali> python main.py profile –target @username

We successfully parsed the user’s ID, name, username, and other useful information. Everything looks good, so now it’s time to parse the list of members from the public group.

kali> python main.py members –target @somepublicgroup –max-members 5

Another useful feature is downloading messages:

kali> python main.py messages –target @somepublicgroup –limit 5

We now have well-formatted logs in a JSON file that we can use to analyze messages in detail, compare timestamps, and even import into other tools for further analysis.

Let’s test the last feature of the toolkit, the media module.

kali> python main.py media –target @somepublicgroup–limit 2 –output-dir output/media

The module downloaded the media and checked for EXIF data as shown above.

Summary

Searching for or waiting for someone else to release new tools is not very practical. I hope this article helped you see that many workflow tasks can be automated and that information from social media platforms can be collected and organized effectively with just a bit of programming knowledge.

If you find automation important for your work, consider checking our Python Basics training, where you will learn the essentials to start creating your own scripts for OSINT or security tasks. If you already understand programming and want to build more advanced security tools like DoS protection systems and Wi Fi sniffers, there is also the Advanced Python for Hackers training.

Source: HackersArise
Source Link: https://hackers-arise.com/python-for-hackers-building-a-custom-telegram-osint-toolkit-for-automated-intelligence-gathering/