Initial commit

.devcontainer/devcontainer.json

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+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/python
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "features": {
+    "ghcr.io/devcontainers-contrib/features/poetry:2": {}
+  },
+
+  "remoteEnv": {
+    "SPECKLE_TOKEN": "foobar"
+  },
+  "containerEnv": {
+    "SPECKLE_TOKEN": "asdfasdf"
+  },
+
+  // Features to add to the dev container. More info: https://containers.dev/features.
+  // "features": {},
+
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "forwardPorts": [],
+
+  // Use 'postCreateCommand' to run commands after the container is created.
+  "postCreateCommand": "cp .env.example .env && POETRY_VIRTUALENVS_IN_PROJECT=true poetry install --no-root",
+
+  // Configure tool-specific properties.
+  "customizations": {
+    "vscode": {
+      // Add the IDs of extensions you want installed when the container is created.
+      "extensions": [
+        "ms-python.vscode-pylance",
+        "ms-python.python",
+        "ms-python.black-formatter",
+        "streetsidesoftware.code-spell-checker",
+        "mikestead.dotenv"
+      ]
+    }
+  }
+
+  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // "remoteUser": "root"
+}

.env.example

@@ -0,0 +1 @@
+SPECKLE_TOKEN=mytoken

.github/dependabot.yml

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+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"

.github/workflows/main.yml

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+name: 'build and deploy Speckle functions'
+on:
+  workflow_dispatch:
+  push:
+    tags:
+      - '*'
+
+jobs:
+  publish-automate-function-version: # make sure the action works on a clean machine without building
+    env:
+      FUNCTION_SCHEMA_FILE_NAME: functionSchema.json
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4.1.1
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+      - name: Install and configure Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.3.2
+          virtualenvs-create: false
+          virtualenvs-in-project: false
+          installer-parallel: true
+      - name: Restore dependencies
+        run: poetry install --no-root
+      - name: Extract functionInputSchema
+        id: extract_schema
+        run: |
+          python main.py generate_schema ${HOME}/${{ env.FUNCTION_SCHEMA_FILE_NAME }}
+      - name: Speckle Automate Function - Build and Publish
+        uses: specklesystems/speckle-automate-github-composite-action@0.7.2
+        with:
+          speckle_automate_url: ${{ env.SPECKLE_AUTOMATE_URL || 'https://automate.speckle.dev' }} 
+          speckle_token: ${{ secrets.SPECKLE_FUNCTION_TOKEN }}
+          speckle_function_id: ${{ secrets.SPECKLE_FUNCTION_ID }}
+          speckle_function_input_schema_file_path: ${{ env.FUNCTION_SCHEMA_FILE_NAME }}
+          speckle_function_command: 'python -u main.py run'

.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/visualstudiocode,python,pycharm
+# Edit at https://www.toptal.com/developers/gitignore?templates=visualstudiocode,python,pycharm
+
+**/.env
+**/.envrc
+
+**/.tool-versions
+
+### PyCharm ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator-enh.xml
+.idea/**/markdown-navigator/
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+.idea/$CACHE_FILE$
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+.idea/codestream.xml
+
+# Azure Toolkit for IntelliJ plugin
+# https://plugins.jetbrains.com/plugin/8053-azure-toolkit-for-intellij
+.idea/**/azureSettings.xml
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/visualstudiocode,python,pycharm

.vscode/launch.json

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+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Speckle Automate function",
+      "type": "python",
+      "request": "launch",
+      "program": "main.py",
+      "console": "integratedTerminal",
+      "justMyCode": true,
+      "envFile": "${workspaceFolder}/.env",
+      "args": [
+        "run",
+        "{\"projectId\": \"843d07eb10\", \"modelId\": \"base design\", \"versionId\": \"2a32ccfee1\", \"speckleServerUrl\": \"https://latest.speckle.systems\"}",
+        // make sure to use camelCase for variable names
+        "{\"forbiddenSpeckleType\": \"Objects.Geometry.Brep\"}"
+      ]
+    }
+  ]
+}

.vscode/settings.json

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+{
+  "cSpell.words": [
+    "camelcase",
+    "pydantic",
+    "stringcase",
+    "typer"
+  ],
+  "python.defaultInterpreterPath": ".venv/bin/python"
+}

Dockerfile

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+# We use the official Python 3.11 image as our base image and will add our code to it. For more details, see https://hub.docker.com/_/python
+FROM python:3.11-slim
+
+# We install poetry to generate a list of dependencies which will be required by our application
+RUN pip install poetry
+
+# We set the working directory to be the /home/speckle directory; all of our files will be copied here.
+WORKDIR /home/speckle
+
+# Copy all of our code and assets from the local directory into the /home/speckle directory of the container.
+# We also ensure that the user 'speckle' owns these files, so it can access them
+# This assumes that the Dockerfile is in the same directory as the rest of the code
+COPY . /home/speckle
+
+# Using poetry, we generate a list of requirements, save them to requirements.txt, and then use pip to install them
+RUN poetry export --format requirements.txt --output /home/speckle/requirements.txt && pip install --requirement /home/speckle/requirements.txt

README.md

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+# Speckle Automate function template - Python
+
+This is a template repository for a Speckle Automate functions written in python
+using the [specklepy](https://pypi.org/project/specklepy/) SDK to interact with Speckle data.
+
+This template contains the full scaffolding required to publish a function to the automate environment.
+Also has some sane defaults for a development environment setups.
+
+## Getting started
+
+1. Use this template repository to create a new repository in your own / organization's profile.
+
+Register the function 
+
+### Add new dependencies
+
+To add new python package dependencies to the project, use:
+`$ poetry add pandas`
+
+### Change launch variables
+
+describe how the launch.json should be edited
+
+### Github Codespaces
+
+Create a new repo from this template, and use the create new code.
+
+### Using this Speckle Function
+
+1. [Create](https://automate.speckle.dev/) a new Speckle Automation.
+1. Select your Speckle Project and Speckle Model.
+1. Select the existing Speckle Function named [`Random comment on IFC beam`](https://automate.speckle.dev/functions/e110be8fad).
+1. Enter a phrase to use in the comment.
+1. Click `Create Automation`.
+
+## Getting Started with creating your own Speckle Function
+
+1. [Register](https://automate.speckle.dev/) your Function with [Speckle Automate](https://automate.speckle.dev/) and select the Python template.
+1. A new repository will be created in your GitHub account.
+1. Make changes to your Function in `main.py`. See below for the Developer Requirements, and instructions on how to test.
+1. To create a new version of your Function, create a new [GitHub release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) in your repository.
+
+## Developer Requirements
+
+1. Install the following:
+    - [Python 3](https://www.python.org/downloads/)
+    - [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer)
+1. Run `poetry shell && poetry install` to install the required Python packages.
+
+## Building and Testing
+
+The code can be tested locally by running `poetry run pytest`.
+
+### Building and running the Docker Container Image
+
+Running and testing your code on your own machine is a great way to develop your Function; the following instructions are a bit more in-depth and only required if you are having issues with your Function in GitHub Actions or on Speckle Automate.
+
+#### Building the Docker Container Image
+
+Your code is packaged by the GitHub Action into the format required by Speckle Automate. This is done by building a Docker Image, which is then run by Speckle Automate. You can attempt to build the Docker Image yourself to test the building process locally.
+
+To build the Docker Container Image, you will need to have [Docker](https://docs.docker.com/get-docker/) installed.
+
+Once you have Docker running on your local machine:
+
+1. Open a terminal
+1. Navigate to the directory in which you cloned this repository
+1. Run the following command:
+
+    ```bash
+    docker build -f ./Dockerfile -t speckle_automate_python_example .
+    ```
+
+#### Running the Docker Container Image
+
+Once the image has been built by the GitHub Action, it is sent to Speckle Automate. When Speckle Automate runs your Function as part of an Automation, it will run the Docker Container Image. You can test that your Docker Container Image runs correctly by running it locally.
+
+1. To then run the Docker Container Image, run the following command:
+
+    ```bash
+    docker run --rm speckle_automate_python_example \
+    python -u main.py run \
+    '{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}' \
+    '{}' \
+    yourSpeckleServerAuthenticationToken
+    ```
+
+Let's explain this in more detail:
+
+`docker run --rm speckle_automate_python_example` tells Docker to run the Docker Container Image that we built earlier. `speckle_automate_python_example` is the name of the Docker Container Image that we built earlier. The `--rm` flag tells docker to remove the container after it has finished running, this frees up space on your machine.
+
+The line `python -u main.py run` is the command that is run inside the Docker Container Image. The rest of the command is the arguments that are passed to the command. The arguments are:
+
+- `'{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}'` - the metadata that describes the automation and the function.
+- `{}` - the input parameters for the function that the Automation creator is able to set. Here they are blank, but you can add your own parameters to test your function.
+- `yourSpeckleServerAuthenticationToken` - the authentication token for the Speckle Server that the Automation can connect to. This is required to be able to interact with the Speckle Server, for example to get data from the Model.
+
+## Resources
+
+- [Learn](https://speckle.guide/dev/python.html) more about SpecklePy, and interacting with Speckle from Python.

flatten.py

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+"""Helper module for a simple speckle object tree flattening."""
+
+from collections.abc import Iterable
+
+from specklepy.objects import Base
+
+
+def flatten_base(base: Base) -> Iterable[Base]:
+    """Take a base and flatten it to an iterable of bases."""
+    if hasattr(base, "elements"):
+        for element in base["elements"]:
+            yield from flatten_base(element)
+    yield base

main.py

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+"""This module contains the business logic of the function.
+
+Use the automation_context module to wrap your function in an Autamate context helper
+"""
+
+from pydantic import Field
+from speckle_automate import (
+    AutomateBase,
+    AutomationContext,
+    execute_automate_function,
+)
+
+from flatten import flatten_base
+
+
+class FunctionInputs(AutomateBase):
+    """These are function author defined values.
+
+    Automate will make sure to supply them matching the types specified here.
+    Please use the pydantic model schema to define your inputs:
+    https://docs.pydantic.dev/latest/usage/models/
+    """
+
+    forbidden_speckle_type: str = Field(
+        title="Forbidden speckle type",
+        description=(
+            "If a object has the following speckle_type,"
+            " it will be marked with an error."
+        ),
+    )
+
+
+def automate_function(
+    automate_context: AutomationContext,
+    function_inputs: FunctionInputs,
+) -> None:
+    """This is an example Speckle Automate function.
+
+    Args:
+        automate_context: A context helper object, that carries relevant information
+            about the runtime context of this function.
+            It gives access to the Speckle project data, that triggered this run.
+            It also has conveniece methods attach result data to the Speckle model.
+        function_inputs: An instance object matching the defined schema.
+    """
+    # the context provides a conveniet way, to receive the triggering version
+    version_root_object = automate_context.receive_version()
+
+    objects_with_forbidden_speckle_type = [
+        b
+        for b in flatten_base(version_root_object)
+        if b.speckle_type == function_inputs.forbidden_speckle_type
+    ]
+    count = len(objects_with_forbidden_speckle_type)
+
+    if count > 0:
+        # this is how a run is marked with a failure cause
+        automate_context.attach_error_to_objects(
+            category="Forbidden speckle_type"
+            " ({function_inputs.forbidden_speckle_type})",
+            object_ids=[o.id for o in objects_with_forbidden_speckle_type if o.id],
+            message="This project should not contain the type: "
+            f"{function_inputs.forbidden_speckle_type}",
+        )
+        automate_context.mark_run_failed(
+            "Automation failed: "
+            f"Found {count} object that have one of the forbidden speckle types: "
+            f"{function_inputs.forbidden_speckle_type}"
+        )
+
+        # set the automation context view, to the original model / version view
+        # to show the offending objects
+        automate_context.set_context_view()
+
+    else:
+        automate_context.mark_run_success("No forbidden types found.")
+
+    # if the function generates file results, this is how it can be
+    # attached to the Speckle project / model
+    # automate_context.store_file_result("./report.pdf")
+
+
+def automate_function_without_inputs(automate_context: AutomationContext) -> None:
+    """A function example without inputs.
+
+    If your function does not need any input variables,
+     besides what the automation context provides,
+     the inputs argument can be omitted.
+    """
+    pass
+
+
+# make sure to call the function with the executor
+if __name__ == "__main__":
+    # NOTE: always pass in the automate function by its reference, do not invoke it!
+
+    # pass in the function reference with the inputs schema to the executor
+    execute_automate_function(automate_function, FunctionInputs)
+
+    # if the function has no arguments, the executor can handle it like so
+    # execute_automate_function(automate_function_without_inputs)

pyproject.toml

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+[tool.poetry]
+name = "speckle-automate-py"
+version = "0.1.0"
+description = "Example function for Speckle Automate using specklepy"
+authors = ["Gergő Jedlicska <gergo@jedlicska.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+specklepy = "2.17.11"
+
+[tool.poetry.group.dev.dependencies]
+black = "^23.3.0"
+mypy = "^1.3.0"
+ruff = "^0.0.271"
+pytest = "^7.4.2"
+# specklepy = {path = "../specklepy", develop = true}
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+select = [
+  "E",   # pycodestyle
+  "F",   # pyflakes
+  "UP",  # pyupgrade
+  "D",   # pydocstyle
+  "I",   # isort
+]
+
+[tool.ruff.pydocstyle]
+convention = "google"

tests/test_function.py

@@ -0,0 +1,158 @@
+"""Run integration tests with a speckle server."""
+import os
+import secrets
+import string
+
+import pytest
+from gql import gql
+from speckle_automate import (
+    AutomationRunData,
+    AutomationStatus,
+    run_function,
+)
+from specklepy.api import operations
+from specklepy.api.client import SpeckleClient
+from specklepy.objects.base import Base
+from specklepy.transports.server import ServerTransport
+
+from main import FunctionInputs, automate_function
+
+
+def crypto_random_string(length: int) -> str:
+    """Generate a semi crypto random string of a given length."""
+    alphabet = string.ascii_letters + string.digits
+    return "".join(secrets.choice(alphabet) for _ in range(length))
+
+
+def register_new_automation(
+    project_id: str,
+    model_id: str,
+    speckle_client: SpeckleClient,
+    automation_id: str,
+    automation_name: str,
+    automation_revision_id: str,
+):
+    """Register a new automation in the speckle server."""
+    query = gql(
+        """
+        mutation CreateAutomation(
+            $projectId: String! 
+            $modelId: String! 
+            $automationName: String!
+            $automationId: String! 
+            $automationRevisionId: String!
+        ) {
+                automationMutations {
+                    create(
+                        input: {
+                            projectId: $projectId
+                            modelId: $modelId
+                            automationName: $automationName 
+                            automationId: $automationId
+                            automationRevisionId: $automationRevisionId
+                        }
+                    )
+                }
+            }
+        """
+    )
+    params = {
+        "projectId": project_id,
+        "modelId": model_id,
+        "automationName": automation_name,
+        "automationId": automation_id,
+        "automationRevisionId": automation_revision_id,
+    }
+    speckle_client.httpclient.execute(query, params)
+
+
+@pytest.fixture()
+def speckle_token() -> str:
+    """Provide a speckle token for the test suite."""
+    env_var = "SPECKLE_TOKEN"
+    token = os.getenv(env_var)
+    if not token:
+        raise ValueError(f"Cannot run tests without a {env_var} environment variable")
+    return token
+
+
+@pytest.fixture()
+def speckle_server_url() -> str:
+    """Provide a speckle server url for the test suite, default to localhost."""
+    return os.getenv("SPECKLE_SERVER_URL", "http://127.0.0.1:3000")
+
+
+@pytest.fixture()
+def test_client(speckle_server_url: str, speckle_token: str) -> SpeckleClient:
+    """Initialize a SpeckleClient for testing."""
+    test_client = SpeckleClient(
+        speckle_server_url, speckle_server_url.startswith("https")
+    )
+    test_client.authenticate_with_token(speckle_token)
+    return test_client
+
+
+@pytest.fixture()
+def test_object() -> Base:
+    """Create a Base model for testing."""
+    root_object = Base()
+    root_object.foo = "bar"
+    return root_object
+
+
+@pytest.fixture()
+def automation_run_data(
+    test_object: Base, test_client: SpeckleClient, speckle_server_url: str
+) -> AutomationRunData:
+    """Set up an automation context for testing."""
+    project_id = test_client.stream.create("Automate function e2e test")
+    branch_name = "main"
+
+    model = test_client.branch.get(project_id, branch_name, commits_limit=1)
+    model_id: str = model.id
+
+    root_obj_id = operations.send(
+        test_object, [ServerTransport(project_id, test_client)]
+    )
+    version_id = test_client.commit.create(project_id, root_obj_id)
+
+    automation_name = crypto_random_string(10)
+    automation_id = crypto_random_string(10)
+    automation_revision_id = crypto_random_string(10)
+
+    register_new_automation(
+        project_id,
+        model_id,
+        test_client,
+        automation_id,
+        automation_name,
+        automation_revision_id,
+    )
+
+    automation_run_id = crypto_random_string(10)
+    function_id = crypto_random_string(10)
+    function_revision = crypto_random_string(10)
+    return AutomationRunData(
+        project_id=project_id,
+        model_id=model_id,
+        branch_name=branch_name,
+        version_id=version_id,
+        speckle_server_url=speckle_server_url,
+        automation_id=automation_id,
+        automation_revision_id=automation_revision_id,
+        automation_run_id=automation_run_id,
+        function_id=function_id,
+        function_revision=function_revision,
+    )
+
+
+def test_function_run(automation_run_data: AutomationRunData, speckle_token: str):
+    """Run an integration test for the automate function."""
+    automate_sdk = run_function(
+        automate_function,
+        automation_run_data,
+        speckle_token,
+        FunctionInputs(forbidden_speckle_type="Base"),
+    )
+
+    assert automate_sdk.run_status == AutomationStatus.FAILED