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When your app spans multiple files, uses local utility modules, or depends on code from a private repository, you need a way to include that code in the remote environment where your runners execute. This page covers the mechanisms fal provides: app_files for local files and directories, local_python_modules for Python modules that should be serialized alongside your app, and clone_repository for pulling external Git repos at startup. These mechanisms work with the fal Runtime (pip requirements). They are not available when using a custom Docker container, where you should use COPY in your Dockerfile to include local files instead.
app_files and local_python_modules are only supported with the fal Runtime. If your app uses a custom ContainerImage, use COPY in your Dockerfile to include local files.

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Choosing the Right Mechanism

fal provides three ways to bring code into the remote environment, each suited to a different situation. Use app_files when you have a multi-file project and want the remote environment to mirror your local directory structure. Files are uploaded and placed at the same relative paths, so imports and file reads work identically to local development. This is the recommended approach for most projects. Use local_python_modules when you have a simple Python module that your app imports at the top level. The module is serialized (pickled) alongside your app class and made available on the remote Python path. This is lighter-weight than app_files but only works for importable Python packages. Use clone_repository when you need to pull code from an external Git repository at runner startup. The clone happens inside setup(), so the code is available before your first request.

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App Files

The app_files attribute includes local files and directories in the remote environment, mirroring your local file layout exactly. Imports and file paths work the same way they do on your machine. 
class MyApp(fal.App):
    app_files = [
        "utils/helper.py",
        "models",
        "checkpoint.pt",
    ]

    @fal.endpoint("/")
    def predict(self, input: MyInput) -> MyOutput:
        from utils.helper import process_data
        from models.classifier import MyModel

        result = process_data(input)
        model = MyModel.load_checkpoint("checkpoint.pt")
        return model.predict(result)
If your local project looks like this: 
project/
├── my_fal_app.py
├── utils/
│   └── helper.py
└── models/
    └── classifier.py
Then on the runner, from utils.helper import process_data and from models.classifier import MyModel work exactly as they do locally. Files are placed relative to your app file location.

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Context Directory

By default, all file paths are resolved relative to the directory containing your fal app file. You can change this base directory with app_files_context_dir, which is useful for monorepos or when you need to include files from a parent directory: 
class MyApp(fal.App):
    app_files_context_dir = "../"
    app_files = [
        "src/data_processing",
        "src/models",
        "weights",
        "utils",
    ]
All paths must be relative and within the context directory. Absolute paths and paths that escape the context directory (e.g., ../../outside) are rejected. Included files are read-only on the runner.

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Ignoring Files

Use app_files_ignore to exclude files using regex patterns. fal excludes .pyc, __pycache__/, .git/, and .DS_Store by default. 
class MyApp(fal.App):
    app_files = ["my_project/"]
    app_files_ignore = [
        r"\.pyc$",
        r"__pycache__/",
        r"\.git/",
        r"\.env$",
        r"test_.*\.py$",
    ]
Only include the files your app actually needs. Smaller deployments mean faster uploads and faster runner startup.

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Local Python Modules

The local_python_modules attribute ships a Python module alongside your app by serializing it into the deployment payload. This is a simpler mechanism than app_files - it adds the specified modules to the remote Python path so they can be imported directly. Use it when you have a small utility module that your app imports at the top level. 
from mymodule import myfunction

class MyApp(fal.App):
    local_python_modules = ["mymodule"]

    @fal.endpoint("/")
    def predict(self, input: MyInput) -> MyOutput:
        myfunction(input)
        ...
For projects with multiple files, directories, or non-Python assets (configs, weights), app_files is the better choice because it preserves the full directory structure and supports ignore patterns.

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Git Repositories

Use clone_repository to pull code from a Git repository at runner startup. The clone happens inside setup(), so the repository is available before any requests are processed. Pin to a specific commit hash for reproducibility. 
from fal.toolkit import clone_repository

class MyApp(fal.App):
    def setup(self):
        path = clone_repository(
            "https://github.com/myorg/myrepo",
            commit_hash="1418c53bcfaf4efc1034207dcb39d093d5fff645",
            include_to_path=True,
        )

        import myproject
        ...
Setting include_to_path=True adds the cloned directory to PYTHONPATH, so you can import modules from the repository directly. For private repositories, include a personal access token in the URL: https://YOUR_TOKEN@github.com/myorg/private-repo.git.