textX meta-models

textX meta-model is a Python object that knows about all classes that can be instantiated while parsing the input. A meta-model is built from the grammar by the functions metamodel_from_file or metamodel_from_str in the textx.metamodel module.

from textx import metamodel_from_file
my_metamodel = metamodel_from_file('my_grammar.tx')

Each rule from the grammar will result in a Python class kept in the meta-model. Besides, meta-model knows how to parse the input strings and convert them to model.

Parsing the input and creating the model is done by model_from_file and model_from_str methods of the meta-model object:

my_model = my_metamodel.model_from_file('some_input.md')

When parsing a model file or string a new parser is cloned for each model. This parser can be accessed via the model attribute _tx_parser.

Custom classes

For each grammar rule a Python class with the same name is created dynamically. These classes are instantiated during the parsing of the input string/file to create a graph of python objects, a.k.a. model or Abstract-Syntax Tree (AST).

Most of the time dynamically created classes will be sufficient, but sometimes you will want to use your own classes instead. To do so use parameter classes during the meta-model instantiation. This parameter is a list of your classes that should be named the same as the rules from the grammar which they represent.

from textx import metamodel_from_str

grammar = '''
  entities+=Entity    // each model has one or more entities

  'entity' name=ID '{'
    attributes+=Attribute     // each entity has one or more attributes

  name=ID ':' type=[Entity]   // type is a reference to an entity. There are
                              // built-in entities registered on the meta-model
                              // for primitive types (integer, string)

class Entity:
  def __init__(self, parent, name, attributes):
    self.parent = parent
    self.name = name
    self.attributes = attributes

# Use our Entity class. "Attribute" class will be created dynamically.
entity_mm = metamodel_from_str(grammar, classes=[Entity])

Now entity_mm can be used to parse the input models where our Entity class will be instantiated to represent each Entity rule from the grammar.

When passing a list of classes (as shown in the example above), you need to have rules for all of these classes in your grammar (else, you get an exception). Alternatively, you can also pass a callable (instead of a list of classes) to return user classes given a rule name. In that case, only rule names found in the grammar are used to query user classes. See unittest.


Constructor of the user-defined classes should accept all attributes defined by the corresponding rule from the grammar. In the previous example, we have provided name and attributes attributes from the Entity rule. If the class is a child in a parent-child relationship (see the next section), then the parent constructor parameter should also be given.

Classes that use __slots__ are supported. Also, initialization of custom classes is postponed during model loading and done after reference resolving but before object processors call (see Using the scope provider to modify a model) to ensure that immutable objects (e.g. using attr frozen feature), that can't be changed after initialization, are also supported.

Parent-child relationships

There is often an intrinsic parent-child relationship between object in the model. In the previous example, each Attribute instance will always be a child of some Entity object.

textX gives automatic support for these relationships by providing the parent attribute on each child object.

When you navigate model each child instance will have a parent attribute.


Always provide the parent parameter in user-defined classes for each class that is a child in a parent-child relationship.


To specify static semantics of the language textX uses a concept called processor. Processors are python callables that can modify the model elements during model parsing/instantiation or do some additional checks that are not possible to do by the grammar alone.

There are two types of processors:

  • model processors - are callables that are called at the end of the parsing when the whole model is instantiated. These processors accept the model and meta-model as parameters.
  • object processors - are registered for particular classes (grammar rules) and are called when the objects of the given class is instantiated.

Processors can modify model/objects or raise exception (TextXSemanticError) if some constraint is not met. User code that calls the model instantiation/parsing can catch and handle this exception.

Model processors

To register a model processor call register_model_processor on the meta-model instance.

from textx import metamodel_from_file

# Model processor is a callable that will accept meta-model and model as its
# parameters.
def check_some_semantics(model, metamodel):
  ... Do some check on the model and raise TextXSemanticError if the semantics
  ... rules are violated.

my_metamodel = metamodel_from_file('mygrammar.tx')

# Register model processor on the meta-model instance

# Parse the model. check_some_semantics will be called automatically after
# a successful parse to do further checks. If the rules are not met,
# an instance of TextXSemanticError will be raised.

Object processors

The purpose of the object processors is to validate or alter the object being constructed. They are registered per class/rule.

Let's do some additional checks for the above Entity DSL example.

def entity_obj_processor(entity):
  Check that Ethe ntity names are capitalized. This could also be specified
  in the grammar using regex match but we will do that check here just
  as an example.

  if entity.name != entity.name.capitalize():
    raise TextXSemanticError('Entity name "%s" must be capitalized.' %
                            entity.name, **get_location(entity))

def attribute_obj_processor(attribute):
  Obj. processors can also introduce changes in the objects they process.
  Here we set "primitive" attribute based on the Entity they refer to.
  attribute.primitive = attribute.type.name in ['integer', 'string']

# Object processors are registered by defining a map between a rule name
# and the callable that will process the instances of that rule/class.
obj_processors = {
    'Entity': entity_obj_processor,
    'Attribute': attribute_obj_processor,

# This map/dict is registered on a meta-model by the "register_obj_processors"
# call.

# Parse the model. At each successful parse of Entity or Attribute, the registered
# processor will be called and the semantics error will be raised if the
# check does not pass.

For another example of the usage of an object processor that modifies the objects, see object processor move_command_processor robot example.

If object processor returns a value that value will be used instead of the original object. This can be used to implement e.g. expression evaluators or on-the-fly model interpretation. For more information


For more information please take a look at the tests.

Object processors decorated with textx.textxerror_wrap will transform any exceptions not derived from TextXError to a TextXError (including line/column and filename information). This can be useful, if object processors transform values using non-textx libraries (like datetime) and you wish to get the location in the model file, where errors occur while transforming the data (see these tests).

Built-in objects

Often, you will need objects that should be a part of each model and you do not want users to specify them in every model they create. The most notable example are primitive types (e.g. integer, string, bool).

Let's provide integer and string Entities to our Entity meta-model in order to simplify the model creation so that the users can use the names of these two entities as the Attribute types.

class Entity:
    def __init__(self, parent, name, attributes):
        self.parent = parent
        self.name = name
        self.attributes = attributes

entity_builtins = {
        'integer': Entity(None, 'integer', []),
        'string': Entity(None, 'string', [])
entity_mm = metamodel_from_file(
  classes=[Entity]            # Register Entity user class,
  builtins=entity_builtins    # Register integer and string built-in objs

Now an integer and string Attribute types can be used. See model and Entitiy example for more.

Creating your own base type

Match rules by default return Python string type. Built-in match rules (i.e. BASETYPEs) on the other hand return Python base types.

You can use object processors to create your type by specifying match rule in the grammar and object processor for that rule that will create an object of a proper Python type.


  'begin' some_number=MyFloat 'end'

In this example MyFloat rule is a match rule and by default will return Python string, so attribute some_number will be of string type. To change that, register object processor for MyFloat rule:

mm = metamodel_from_str(grammar)
mm.register_obj_processors({'MyFloat': lambda x: float(x)}))

Now, MyFloat will always be converted to Python float type.

Using match filters you can override built-in rule's conversions like this:

mm = metamodel_from_str(grammar)
mm.register_obj_processors({'INT': lambda x: float(x)}))

In this example we use built-in rule INT that returns Python int type. Registering object processor with the key INT we can change default behaviour and convert what is matched by this rule to some other object (float in this case).

Auto-initialization of the attributes

Each object that is recognized in the input string will be instantiated and its attributes will be set to the values parsed from the input. In the event that a defined attribute is optional, it will nevertheless be created on the instance and set to the default value.

Here is a list of the default values for each base textX type:

  • ID - empty string - ''
  • INT - int - 0
  • FLOAT - float - 0.0 (also 0 is possible)
  • STRICTFLOAT - float - 0.0 (0. or .0 or 0e1, but not 0, which is an INT)
  • BOOL - bool - False
  • STRING - empty string - ''

Each attribute with zero or more multiplicity (*=) that does not match any object from the input will be initialized to an empty list.

An attribute declared with one or more multiplicity (+=) must match at least one object from the input and will therefore be transformed to python list containing all matched objects.

The drawback of this auto-initialization system is that we can't be sure if the attribute was missing from the input or was matched, but the given value was the same as the default value.

In some applications it is important to distinguish between those two situations. For that purpose, there is a parameter auto_init_attributes of the meta-model constructor that is True by default, but can be set to False to prevent auto-initialization from taking place.

If auto-initialization is disabled, then each optional attribute that was not matched on the input will be set to None. This is true for the plain assignments (=). An optional assignment (?=) will always be False if the RHS object is not matched in the input. The multiplicity assignments (*= and +=) will always be python lists.

Optional model parameter definitions

A meta-model can define optional model parameters. Such definitions are stored in model_param_defs and define optional parameters, which can be specified while loading/creating a model through model_from_str or model_from_file. Details: see tx_model_params.

metamodel.model_param_defs can be queried (like a dict) to retrieve possible extra parameters and their descriptions for a meta-model. It is also used to restrict the additional parameters passed to model_from_str or model_from_file.

Default parameters are:

  • project_root: this model parameter is used by the GlobalRepo to set a project directory, where all file patterns not referring to an absolute file position are looked up.

An example of a custom model parameter definition used to control the behavior of an object processor is given in test_reference_to_nontextx_attribute.py, (test_object_processor_with_optional_parameter_*; specifying a parameter while loading) and test_reference_to_nontextx_attribute.py (defining the parameter in the metamodel).