Features and Caveats

Jedi obviously supports autocompletion. It’s also possible to get it working in (your REPL (IPython, etc.)).

Static analysis is also possible by using the command jedi.names.

Jedi would in theory support refactoring, but we have never publicized it, because it’s not production ready. If you’re interested in helping out here, let me know. With the latest parser changes, it should be very easy to actually make it work.

General Features

  • Python 2.7 and 3.4+ support
  • Ignores syntax errors and wrong indentation
  • Can deal with complex module / function / class structures
  • Great Virtualenv support
  • Can infer function arguments from sphinx, epydoc and basic numpydoc docstrings, and PEP0484-style type hints (type hinting)
  • Stub files

Supported Python Features

Jedi supports many of the widely used Python features:

  • builtins
  • returns, yields, yield from
  • tuple assignments / array indexing / dictionary indexing / star unpacking
  • with-statement / exception handling
  • *args / **kwargs
  • decorators / lambdas / closures
  • generators / iterators
  • some descriptors: property / staticmethod / classmethod
  • some magic methods: __call__, __iter__, __next__, __get__, __getitem__, __init__
  • list.append(), set.add(), list.extend(), etc.
  • (nested) list comprehensions / ternary expressions
  • relative imports
  • getattr() / __getattr__ / __getattribute__
  • function annotations
  • class decorators (py3k feature, are being ignored too, until I find a use case, that doesn’t work with Jedi)
  • simple/usual sys.path modifications
  • isinstance checks for if/while/assert
  • namespace packages (includes pkgutil, pkg_resources and PEP420 namespaces)
  • Django / Flask / Buildout support

Not Supported

Not yet implemented:

  • manipulations of instances outside the instance variables without using methods

Will probably never be implemented:

  • metaclasses (how could an auto-completion ever support this)
  • setattr(), __import__()
  • writing to some dicts: globals(), locals(), object.__dict__


Slow Performance

Importing numpy can be quite slow sometimes, as well as loading the builtins the first time. If you want to speed things up, you could write import hooks in Jedi, which preload stuff. However, once loaded, this is not a problem anymore. The same is true for huge modules like PySide, wx, etc.


Security is an important issue for Jedi. Therefore no Python code is executed. As long as you write pure Python, everything is inferred statically. But: If you use builtin modules (c_builtin) there is no other option than to execute those modules. However: Execute isn’t that critical (as e.g. in pythoncomplete, which used to execute every import!), because it means one import and no more. So basically the only dangerous thing is using the import itself. If your c_builtin uses some strange initializations, it might be dangerous. But if it does you’re screwed anyways, because eventually you’re going to execute your code, which executes the import.


Here are some tips on how to use Jedi efficiently.

Type Hinting

If Jedi cannot detect the type of a function argument correctly (due to the dynamic nature of Python), you can help it by hinting the type using one of the following docstring/annotation syntax styles:

PEP-0484 style


function annotations

def myfunction(node: ProgramNode, foo: str) -> None:
    """Do something with a ``node``.

    node.| # complete here

assignment, for-loop and with-statement type hints (all Python versions). Note that the type hints must be on the same line as the statement

x = foo()  # type: int
x, y = 2, 3  # type: typing.Optional[int], typing.Union[int, str] # typing module is mostly supported
for key, value in foo.items():  # type: str, Employee  # note that Employee must be in scope
with foo() as f:  # type: int
    print(f + 3)

Most of the features in PEP-0484 are supported including the typing module (for Python < 3.5 you have to do pip install typing to use these), and forward references.

You can also use stub files.

Sphinx style


def myfunction(node, foo):
    """Do something with a ``node``.

    :type node: ProgramNode
    :param str foo: foo parameter description

    node.| # complete here



def myfunction(node):
    """Do something with a ``node``.

    @type node: ProgramNode

    node.| # complete here



In order to support the numpydoc format, you need to install the numpydoc package.

def foo(var1, var2, long_var_name='hi'):
    r"""A one-line summary that does not use variable names or the
    function name.


    var1 : array_like
        Array_like means all those objects -- lists, nested lists,
        etc. -- that can be converted to an array. We can also
        refer to variables like `var1`.
    var2 : int
        The type above can either refer to an actual Python type
        (e.g. ``int``), or describe the type of the variable in more
        detail, e.g. ``(N,) ndarray`` or ``array_like``.
    long_variable_name : {'hi', 'ho'}, optional
        Choices in brackets, default first when optional.


    var2.| # complete here

A little history

The Star Wars Jedi are awesome. My Jedi software tries to imitate a little bit of the precognition the Jedi have. There’s even an awesome scene of Monty Python Jedis :-).

But actually the name hasn’t so much to do with Star Wars. It’s part of my second name.

After I explained Guido van Rossum, how some parts of my auto-completion work, he said (we drank a beer or two):

“Oh, that worries me…”

When it’s finished, I hope he’ll like it :-)

I actually started Jedi, because there were no good solutions available for VIM. Most auto-completions just didn’t work well. The only good solution was PyCharm. But I like my good old VIM. Rope was never really intended to be an auto-completion (and also I really hate project folders for my Python scripts). It’s more of a refactoring suite. So I decided to do my own version of a completion, which would execute non-dangerous code. But I soon realized, that this wouldn’t work. So I built an extremely recursive thing which understands many of Python’s key features.

By the way, I really tried to program it as understandable as possible. But I think understanding it might need quite some time, because of its recursive nature.