Guesslang documentation

Guesslang detects the programming language of a given source code.

It supports 30 programming languages and detects the correct programming language with more than 90% accuracy.

Guesslang is an open source deep learning software that have been trained with over a million source code files.

You can use Guesslang as a command line interface tool or as a Python module:

from guesslang import Guess

guess = Guess()

# Guess the language from code
language = guess.language_name("""
    % Quick sort

    -module (recursion).
    -export ([qsort/1]).

    qsort([]) -> [];
    qsort([Pivot|T]) ->
           qsort([X || X <- T, X < Pivot])
           ++ [Pivot] ++
           qsort([X || X <- T, X >= Pivot]).

print(language)  # --> Erlang

Guesslang supports 30 of the most popular programming languages:

Batchfile C C# C++ CSS
CoffeeScript Erlang Go HTML Haskell
Java JavaScript Jupyter Notebook Lua Markdown
Matlab Objective-C PHP Perl PowerShell
Python R Ruby Rust SQL
Scala Shell Swift TeX TypeScript

Guesslang is used by cool projects like the guessing game GG or the Slack bot Pasta.

It is also used by the Chameledit, web-editor to automagically highlight source code:

— Chameledit in action.

Table of contents

Install Guesslang

Guesslang requires Python 3.6 or later.

Install from Pypi

You can run the following command to install Guesslang on your system:

pip install guesslang

Install from source code

To install Guesslang from source code, just download the source code from, then run this command:

pip install .


Python package

Guesslang Python library helps you detect the programming language of a given text within your Python program. The Python classes are fully documentation here: Guesslang package.

Command line tool

On a terminal emulator, you can detect the programming language of a source code file by running guesslang /path/to/file.

As well, you can detect the programming language of a source code provided through the standard input using a pipeline like some-command | guesslang.


  • Detect the programming language of /bin/which software

    guesslang /bin/which
    # ⟶ Programming language: Shell
  • Detect the programming language of a source code stored in a file

    echo "
      class Array
        def quick_sort
          return self if length <= 1
          pivot = self[0]
          less, greatereq = self[1..-1].partition { |x| x < pivot }
          less.quick_sort + [pivot] + greatereq.quick_sort
    " > /tmp/quicksort
    guesslang /tmp/quicksort
    # ⟶ Programming language: Ruby
  • Execute a command that generates source code then detect the programming language on the fly:

    echo '
      Array.prototype.quick_sort = function () {
         if (this.length < 2) { return this; }
         var pivot = this[Math.round(this.length / 2)];
         return this.filter(x => x <  pivot)
                    .concat(this.filter(x => x == pivot))
                    .concat(this.filter(x => x >  pivot).quick_sort());
    ' | guesslang
    # ⟶ Programming language: JavaScript

With Guesslang command line tool you can also show the detection probabilities for a given source code and even train your custom programming language detection model.

Run guesslang --help to see all the available options.

How does Guesslang guess?

Deep learning Model

Guesslang uses a deep learning Tensorflow model built with more than 1,000,000 unique source code files, from over 100,000 different projects.

Guesslang model is a Deep Neural Network classifier combined with Linear classifier. The model’s hyperparameters have been fine tuned to have both the best performances and the best generalization.


Having a data set with a very large number of diverse examples is essential to correctly train a model.

For Guesslang we built a large dataset using:

  • 1 080 000 unique source code files
  • randomly picked from 101 871 public open source Github repositories.

This large dataset built with GuesslangTools, is used to train, evaluate and test Guesslang’s deep learning model.

To avoid overfitting, each repositories is strictly associated with only one of the 3 aforementioned tasks. Therefore files from a repository assigned to the training task can only be used to train the model and cannot be used to evaluate nor test it.

The training and evaluation steps are done in a loop, as shown by the following loss curve.


— Loss curve, less is better.

🟧 training, 🟦 evaluation.

The test in the other hand is done after the last training and evaluation steps to ensure that the final model performs well.


Guesslang deep learning model performs very well. It was tested with 12,000 different source code files and correctly guessed the programming language of 93.82% of them.

Most of the misclassifications come from few languages that are compatible with each other, like C/C++ or JavaScript/TypeScript.

That phenomenon is shown by the following confusion matrix:


— Lines: actual languages. Columns: guessed languages.

🟥 JavaScript compatible cluster. 🟩 C compatible cluster.

🟧 Command line & Lua cluster. 🟦 Other languages….


As said earlier, Guesslang may misclassify source code from languages that are really close to each other like C/C++ and JavaScript/TypeScript.

This limitation was expected because a valid C source code is almost always a valid C++ code, and a valid JavaScript source code is always a valid TypeScript code.

In addition to that, Guesslang may not guess the correct programming languages of very small code snippets. They don’t provide enough insights for accurate language classification.

For example, print("Hello world") is a valid statement in several programming languages like Python, Scala, Ruby, Lua, Perl, etc…