Creating Filters

Hydrogram already provides lots of built-in filters to work with, but in case you can’t find a specific one for your needs or want to build a custom filter by yourself you can use filters.create().

Custom Filters

An example to demonstrate how custom filters work is to show how to create and use one for the CallbackQueryHandler. Note that callback queries updates are only received by bots as result of a user pressing an inline button attached to the bot’s message; create and authorize your bot, then send a message with an inline keyboard to yourself. This allows you to test your filter by pressing the inline button:

from hydrogram.types import InlineKeyboardMarkup, InlineKeyboardButton

await app.send_message(
    "username",  # Change this to your username or id
    "Hydrogram custom filter test",
        [[InlineKeyboardButton("Press me", "hydrogram")]]

Basic Filters

For this basic filter we will be using only the first parameter of create().

The heart of a filter is its callback function, which accepts three arguments (self, client, update) and returns either True, in case you want the update to pass the filter or False otherwise.

In this example we are matching the query data to “hydrogram”, which means that the filter will only allow callback queries containing “hydrogram” as data:

from hydrogram import filters

async def func(_, __, query):
    return == "hydrogram"

static_data_filter = filters.create(func)

The first two arguments of the callback function are unused here and because of this we named them using underscores.

Finally, the filter usage remains the same:

async def hydrogram_data(_, query):
    query.answer("it works!")

Filters with Arguments

A more flexible filter would be one that accepts “hydrogram” or any other string as argument at usage time. A dynamic filter like this will make use of named arguments for the create() method and the first argument of the callback function, which is a reference to the filter object itself holding the extra data passed via named arguments.

This is how a dynamic custom filter looks like:

from hydrogram import filters

def dynamic_data_filter(data):
    async def func(flt, _, query):
        return ==

    # "data" kwarg is accessed with "" above
    return filters.create(func, data=data)

And finally its usage:

async def hydrogram_data(_, query):
    query.answer("it works!")

Method Calls Inside Filters

The missing piece we haven’t covered yet is the second argument of a filter callback function, namely, the client argument. This is a reference to the Client instance that is running the filter and it is useful in case you would like to make some API calls before deciding whether the filter should allow the update or not:

async def func(_, client, query):
    # r = await client.some_api_method()
    # check response "r" and decide to return True or False