# The Full API

Important

While you have access to this, you should always use the friendly methods listed on Client Reference unless you have a better reason not to, like a method not existing or you wanting more control.

## Introduction

The TelegramClient doesn’t offer a method for every single request the Telegram API supports. However, it’s very simple to call or invoke any request defined in Telegram’s API.

This section will teach you how to use what Telethon calls the TL reference. The linked page contains a list and a way to search through all types generated from the definition of Telegram’s API (in `.tl` file format, hence the name). These types include requests and constructors.

Note

The reason to keep both https://tl.telethon.dev and this documentation alive is that the former allows instant search results as you type, and a “Copy import” button. If you like namespaces, you can also do `from telethon.tl import types, functions`. Both work.

Telegram makes these `.tl` files public, which other implementations, such as Telethon, can also use to generate code. These files are versioned under what’s called “layers”. `.tl` files consist of thousands of definitions, and newer layers often add, change, or remove them. Each definition refers to either a Remote Procedure Call (RPC) function, or a type (which the TL reference calls “constructors”, as they construct particular type instances).

As such, the TL reference is a good place to go to learn about all possible requests, types, and what they look like. If you’re curious about what’s been changed between layers, you can refer to the TL diff site.

## Navigating the TL reference

### Functions

“Functions” is the term used for the Remote Procedure Calls (RPC) that can be sent to Telegram to ask it to perform something (e.g. “send message”). These requests have an associated return type. These can be invoked (“called”):

    client = TelegramClient(...)
    function_instance = SomeRequest(...)

    # Invoke the request
    returned_type = await client(function_instance)

Whenever you find the type for a function in the TL reference, the page will contain the following information:

- What type of account can use the method. This information is regenerated from time to time (by attempting to invoke the function under both account types and finding out where it fails). Some requests can only be used by bot accounts, others by user accounts, and others by both.

- The TL definition. This helps you get a feel for the what the function looks like. This is not Python code. It just contains the definition in a concise manner.

- “Copy import” button. Does what it says: it will copy the necessary Python code to import the function to your system’s clipboard for easy access.

- Returns. The returned type. When you invoke the function, this is what the result will be. It also includes which of the constructors can be returned inline, to save you a click.

- Parameters. The parameters accepted by the function, including their type, whether they expect a list, and whether they’re optional.

- Known RPC errors. A best-effort list of known errors the request may cause. This list is not complete and may be out of date, but should provide an overview of what could go wrong.

- Example. Autogenerated example, showcasing how you may want to call it. Bear in mind that this is autogenerated. It may be spitting out non-sense. The goal of this example is not to show you everything you can do with the request, only to give you a feel for what it looks like to use it.

It is very important to click through the links and navigate to get the full picture. A specific page will show you what the specific function returns and needs as input parameters. But it may reference other types, so you need to navigate to those to learn what those contain or need.

### Types

“Types” as understood by TL are not actually generated in Telethon. They would be the “abstract base class” of the constructors, but since Python is duck-typed, there is hardly any need to generate mostly unnecessary code. The page for a type contains:

- Constructors. Every type will have one or more constructors. These constructors are generated and can be immported and used.

- Requests returning this type. A helpful way to find out “what requests can return this?”. This is how you may learn what request you need to use to obtain a particular instance of a type.

- Requests accepting this type as input. A helpful way to find out “what requests can use this type as one of their input parameters?”. This is how you may learn where a type is used.

- Other types containing this type. A helpful way to find out “where else does this type appear?”. This is how you can walk back through nested objects.

### Constructors

Constructors are used to create instances of a particular type, and are also returned when invoking requests. You will have to create instances yourself when invoking requests that need a particular type as input. The page for a constructor contains:

- Belongs to. The parent type. This is a link back to the types page for the specific constructor. It also contains the sibling constructors inline, to save you a click.

- Members. Both the input parameters and fields the constructor contains.

## Using the TL reference

After you’ve found a request you want to send, a good start would be to simply copy and paste the autogenerated example into your script. Then you can simply tweak it to your needs.

If you want to do it from scratch, first, make sure to import the request into your code (either using the “Copy import” button near the top, or by manually spelling out the package under `telethon.tl.functions.*`).

Then, start reading the parameters one by one. If the parameter cannot be omitted, you will need to specify it, so make sure to spell it out as an input parameter when constructing the request instance. Let’s look at PingRequest for example. First, we copy the import:

    from telethon.tl.functions import PingRequest

Then, we look at the parameters:

> ping_id - long

A single parameter, and it’s a long (a integer number with a large range of values). It doesn’t say it can be omitted, so we must provide it, like so:

    PingRequest(
        ping_id=48641868471
    )

(In this case, the ping ID is a random number. You often have to guess what the parameter needs just by looking at the name.)

Now that we have our request, we can invoke it:

    response = await client(PingRequest(
        ping_id=48641868471
    ))

To find out what `response` looks like, we can do as the autogenerated example suggests and “stringify” the result as a pretty-printed string:

    print(result.stringify())

This will print out the following:

    Pong(
        msg_id=781875678118,
        ping_id=48641868471
    )

Which is a very easy way to get a feel for a response. You should nearly always print the stringified result, at least once, when trying out requests, to get a feel for what the response may look like.

But of course, you don’t need to do that. Without writing any code, you could have navigated through the “Returns” link to learn `PingRequest` returns a `Pong`, which only has one constructor, and the constructor has two members, `msg_id` and `ping_id`.

If you wanted to create your own `Pong`, you would use both members as input parameters:

    my_pong = Pong(
        msg_id=781875678118,
        ping_id=48641868471
    )

(Yes, constructing object instances can use the same code that `.stringify` would return!)

And if you wanted to access the `msg_id` member, you would simply access it like any other attribute access in Python:

    print(response.msg_id)

## Example walkthrough

Say `client.send_message()` didn’t exist, we could use the search to look for “message”. There we would find SendMessageRequest, which we can work with.

Every request is a Python class, and has the parameters needed for you to invoke it. You can also call `help(request)` for information on what input parameters it takes. Remember to “Copy import to the clipboard”, or your script won’t be aware of this class! Now we have:

    from telethon.tl.functions.messages import SendMessageRequest

If you’re going to use a lot of these, you may do:

    from telethon.tl import types, functions
    # We now have access to 'functions.messages.SendMessageRequest'

We see that this request must take at least two parameters, a `peer` of type InputPeer, and a `message` which is just a Python `str`ing.

How can we retrieve this InputPeer? We have two options. We manually construct one, for instance:

    from telethon.tl.types import InputPeerUser

    peer = InputPeerUser(user_id, user_hash)

Or we call `client.get_input_entity()`:

    import telethon

    async def main():
        peer = await client.get_input_entity('someone')

    client.loop.run_until_complete(main())

Note

Remember that `await` must occur inside an `async def`. Every full API example assumes you already know and do this.

When you’re going to invoke an API method, most require you to pass an InputUser, InputChat, or so on, this is why using `client.get_input_entity()` is more straightforward (and often immediate, if you’ve seen the user before, know their ID, etc.). If you also need to have information about the whole user, use `client.get_entity()` instead:

    entity = await client.get_entity('someone')

In the later case, when you use the entity, the library will cast it to its “input” version for you. If you already have the complete user and want to cache its input version so the library doesn’t have to do this every time its used, simply call `telethon.utils.get_input_peer`:

    from telethon import utils
    peer = utils.get_input_peer(entity)

Note

Since `v0.16.2` this is further simplified. The `Request` itself will call `client.get_input_entity` for you when required, but it’s good to remember what’s happening.

After this small parenthesis about `client.get_entity` versus `client.get_input_entity()`, we have everything we need. To invoke our request we do:

    result = await client(SendMessageRequest(peer, 'Hello there!'))

Message sent! Of course, this is only an example. There are over 250 methods available as of layer 80, and you can use every single of them as you wish. Remember to use the right types! To sum up:

    result = await client(SendMessageRequest(
        await client.get_input_entity('username'), 'Hello there!'
    ))

This can further be simplified to:

    result = await client(SendMessageRequest('username', 'Hello there!'))
    # Or even
    result = await client(SendMessageRequest(PeerChannel(id), 'Hello there!'))

Note

Note that some requests have a “hash” parameter. This is not your `api_hash`! It likely isn’t your self-user `.access_hash` either.

It’s a special hash used by Telegram to only send a difference of new data that you don’t already have with that request, so you can leave it to 0, and it should work (which means no hash is known yet).

For those requests having a “limit” parameter, you can often set it to zero to signify “return default amount”. This won’t work for all of them though, for instance, in “messages.search” it will actually return 0 items.

## Requests in Parallel

The library will automatically merge outgoing requests into a single container. Telegram’s API supports sending multiple requests in a single container, which is faster because it has less overhead and the server can run them without waiting for others. You can also force using a container manually:

    async def main():

        # Letting the library do it behind the scenes
        await asyncio.wait([
            client.send_message('me', 'Hello'),
            client.send_message('me', ','),
            client.send_message('me', 'World'),
            client.send_message('me', '.')
        ])

        # Manually invoking many requests at once
        await client([
            SendMessageRequest('me', 'Hello'),
            SendMessageRequest('me', ', '),
            SendMessageRequest('me', 'World'),
            SendMessageRequest('me', '.')
        ])

Note that you cannot guarantee the order in which they are run. Try running the above code more than one time. You will see the order in which the messages arrive is different.

If you use the raw API (the first option), you can use `ordered` to tell the server that it should run the requests sequentially. This will still be faster than going one by one, since the server knows all requests directly:

    await client([
        SendMessageRequest('me', 'Hello'),
        SendMessageRequest('me', ', '),
        SendMessageRequest('me', 'World'),
        SendMessageRequest('me', '.')
    ], ordered=True)

If any of the requests fails with a Telegram error (not connection errors or any other unexpected events), the library will raise `telethon.errors.common.MultiError`. You can `except` this and still access the successful results:

    from telethon.errors import MultiError

    try:
        await client([
            SendMessageRequest('me', 'Hello'),
            SendMessageRequest('me', ''),
            SendMessageRequest('me', 'World')
        ], ordered=True)
    except MultiError as e:
        # The first and third requests worked.
        first = e.results[0]
        third = e.results[2]
        # The second request failed.
        second = e.exceptions[1]