Actor Inspector Agent

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Actor Inspector Agent

Try for free

Developed by

Jakub Kopecký

Maintained by Community

Agent Actor Inspector 🕵️‍♂️: An Apify Actor that rates others on docs 📝, inputs 🔍, code 💻, functionality ⚙️, performance ⏱️, and uniqueness 🌟. Config with actorId array, run, and review results. Helps devs improve, ensures quality, and guides users.

0.0 (0)

Pricing

Pay per event

Last modified

2 months ago

Automation

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.git .mise.toml .nvim.lua storage # The rest is copied from https://github.com/github/gitignore/blob/main/Python.gitignore # 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 # 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/latest/usage/project/#working-with-version-control .pdm.toml .pdm-python .pdm-build/ # 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/

.envrc # 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 # UV # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. #uv.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/latest/usage/project/#working-with-version-control .pdm.toml .pdm-python .pdm-build/ # 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/ .vscode/ # PyPI configuration file .pypirc # Added by Apify CLI storage node_modules

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.PHONY: clean install-dev lint type-check check-code format DIRS_WITH_CODE = src clean: rm -rf .venv .mypy_cache .pytest_cache .ruff_cache __pycache__ install-dev: cd $(DIRS_WITH_CODE) && uv sync --frozen && uv run pre-commit install && cd .. lint: uv run ruff check $(DIRS_WITH_CODE) type-check: uv run mypy $(DIRS_WITH_CODE) check-code: lint type-check format: uv run ruff check --fix $(DIRS_WITH_CODE) uv run ruff format $(DIRS_WITH_CODE) docker-build: docker build --tag dataset-query-engine --file .actor/Dockerfile . docker-run: echo "Stopping and removing the container" docker stop dataset-query-engine 2> /dev/null || true docker rm dataset-query-engine 2> /dev/null || true echo "Running the container" docker run --name dataset-query-engine -it -p 4321:4321 dataset-query-engine make build-run: make docker-build make docker-run pydantic-model: datamodel-codegen --input .actor/input_schema.json --output $(DIRS_WITH_CODE)/input_model.py --input-file-type jsonschema --field-constraints --enum-field-as-literal all pytest: uv run -C $(DIRS_WITH_CODE) pytest --with-integration --vcr-record=none

1[project] 2name = "agent-actor-inspector" 3version = "0.1.0" 4description = "Add your description here" 5readme = "README.md" 6requires-python = ">=3.10,<3.14" 7dependencies = [ 8 "apify>=2.3.0", 9 "crewai>=0.102.0", 10] 11 12[dependency-groups] 13dev = [ 14 "mypy>=1.14.1", 15 "pytest>=8.3.4", 16 "pytest-asyncio>=0.25.3", 17 "python-dotenv>=1.0.1", 18 "ruff>=0.9.4", 19 "types-requests>=2.32.0.20241016", 20] 21 22[tool.ruff] 23line-length = 120 24exclude = ["src/input_model.py"] 25 26[tool.ruff.lint] 27select = ["ALL"] 28ignore = [ 29 "ANN401", # Dynamically typed expressions (typing.Any) are disallowed in {filename} 30 "BLE001", # Do not catch blind exception 31 "C901", # `{name}` is too complex 32 "COM812", # This rule may cause conflicts when used with the formatter 33 "D100", # Missing docstring in public module 34 "D104", # Missing docstring in public package 35 "D107", # Missing docstring in `__init__` 36 "EM", # flake8-errmsg 37 "G004", # Logging statement uses f-string 38 "ISC001", # This rule may cause conflicts when used with the formatter 39 "FIX", # flake8-fixme 40 "PGH003", # Use specific rule codes when ignoring type issues 41 "PLR0911", # Too many return statements 42 "PLR0913", # Too many arguments in function definition 43 "PLR0915", # Too many statements 44 "PTH", # flake8-use-pathlib 45 "PYI034", # `__aenter__` methods in classes like `{name}` usually return `self` at runtime 46 "PYI036", # The second argument in `__aexit__` should be annotated with `object` or `BaseException | None` 47 "S102", # Use of `exec` detected 48 "S105", # Possible hardcoded password assigned to 49 "S106", # Possible hardcoded password assigned to argument: "{name}" 50 "S301", # `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue 51 "S303", # Use of insecure MD2, MD4, MD5, or SHA1 hash function 52 "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes 53 "TD002", # Missing author in TODO; try: `# TODO(<author_name>): ...` or `# TODO @<author_name>: ... 54 "TID252", # Prefer absolute imports over relative imports from parent modules 55 "TRY003", # Avoid specifying long messages outside the exception class 56 "FBT001", # Boolean-typed positional argument in function definition 57 "FBT002", # Boolean default positional argument in function definition 58 # 59 "D", 60] 61[tool.ruff.format] 62quote-style = "single" 63indent-style = "space" 64 65[tool.ruff.lint.per-file-ignores] 66"**/__init__.py" = ["F401"] 67"**/{scripts}/*" = ["D", "INP001", "PLR2004", "T20"] 68"**/{tests}/*" = ["D", "INP001", "PLR2004", "S101", "SLF001", "T20", "TRY301"] 69 70[tool.ruff.lint.flake8-quotes] 71docstring-quotes = "double" 72inline-quotes = "single" 73 74[tool.ruff.lint.pydocstyle] 75convention = "google" 76 77[tool.mypy] 78python_version = "3.12" 79files = ["scripts", "src", "tests"] 80check_untyped_defs = true 81disallow_incomplete_defs = true 82disallow_untyped_calls = true 83disallow_untyped_decorators = true 84disallow_untyped_defs = true 85no_implicit_optional = true 86warn_redundant_casts = true 87warn_return_any = true 88warn_unreachable = true 89warn_unused_ignores = true 90 91[tool.mypy-sortedcollections] 92ignore_missing_imports = true 93 94[tool.pytest.ini_options] 95asyncio_default_fixture_loop_scope = "function"

version = 1 revision = 1 requires-python = ">=3.10, <3.14" [[package]] name = "agent-actor-inspector" version = "0.1.0" source = { virtual = "." } dependencies = [ { name = "apify" }, { name = "crewai" }, ] [package.dev-dependencies] dev = [ { name = "mypy" }, { name = "pytest" }, { name = "pytest-asyncio" }, { name = "python-dotenv" }, { name = "ruff" }, { name = "types-requests" }, ] [package.metadata] requires-dist = [ { name = "apify", specifier = ">=2.3.0" }, { name = "crewai", specifier = ">=0.102.0" }, ] [package.metadata.requires-dev] dev = [ { name = "mypy", specifier = ">=1.14.1" }, { name = "pytest", specifier = ">=8.3.4" }, { name = "pytest-asyncio", specifier = ">=0.25.3" }, { name = "python-dotenv", specifier = ">=1.0.1" }, { name = "ruff", specifier = ">=0.9.4" }, { name = "types-requests", specifier = ">=2.32.0.20241016" }, ] [[package]] name = "aiohappyeyeballs" version = "2.4.6" source = { registry = "https://pypi.org/simple" } sdist = { url = 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FROM apify/actor-python:3.12 # Second, copy just requirements.txt into the Actor image, # since it should be the only file that affects the dependency install in the next step, # in order to speed up the build COPY pyproject.toml uv.lock ./ # Install the packages specified in requirements.txt, # Print the installed Python version, pip version # and all installed packages with their versions for debugging RUN echo "Python version:" \ && python --version \ && echo "Pip version:" \ && pip --version \ && echo "Installing uv:" \ && pip install --no-cache-dir uv \ && echo "Installing dependencies:" \ && uv sync --frozen --no-dev \ && echo "All installed Python packages:" \ && uv pip freeze # Next, copy the remaining files and directories with the source code. # Since we do this after installing the dependencies, quick build will be really fast # for most source file changes. COPY . ./ # Use compileall to ensure the runnability of the Actor Python code. #RUN python3 -m compileall -q . # create non-root user RUN useradd -m apify && \ chown -R apify:apify /usr/src/app USER apify # Specify how to launch the source code of your Actor. # By default, the "python3 -m ." command is run ENV PATH="/usr/src/app/.venv/bin:$PATH" CMD ["python3", "-m", "src"]

{ "actorSpecification": 1, "name": "actor-inspector-agent", "title": "Actor Inspector Agent", "description": "Actor Inspector Agent is an Apify Actor designed to evaluate and rate other Apify Actors based on criteria such as documentation quality, input clarity, code standards, functionality, performance, and uniqueness.", "version": "0.1", "buildTag": "latest", "environmentVariables": {}, "input": "./input_schema.json", "storages": { "dataset": "./dataset_schema.json" }, "dockerfile": "./Dockerfile" }

{ "actorSpecification": 1, "views": { "overview": { "title": "Overview", "transformation": { "fields": ["actorId", "response"] }, "display": { "component": "table", "properties": { "actorId": { "label": "Actor name", "format": "text" }, "response": { "label": "Actor evaluation", "format": "text" } } } } } }

{ "title": "Actor Inspector Agent", "type": "object", "schemaVersion": 1, "properties": { "actorName": { "title": "Actor name in the form of user-name/actor-name", "type": "string", "description": "Name of Actor to inspect.", "editor": "textfield", "prefill": "jakub.kopecky/agent-actor-inspector", "default": "jakub.kopecky/agent-actor-inspector" }, "pedantic": { "title": "Pedantic", "type": "boolean", "description": "If enabled, the analysis is very critical and pedantic.", "editor": "checkbox", "default": true }, "modelName": { "title": "OpenAI model", "type": "string", "description": "The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini, and the reasoning model and o3-mini.", "enum": ["gpt-4o", "gpt-4o-mini", "o3-mini"], "prefill": "gpt-4o", "default": "gpt-4o-mini" }, "debug": { "title": "Debug", "type": "boolean", "description": "If enabled, Actor provides detailed information with tool calls and reasoning.", "editor": "checkbox", "default": true } }, "required": ["actorName"] }

{ "actor-start-gb": { "eventTitle": "Actor start per 1 GB", "eventDescription": "Flat fee for starting an Actor run for each 1 GB of memory.", "eventPriceUsd": 0.05 }, "task-completed": { "eventTitle": "Flat fee for completing a task", "eventDescription": "Flat fee for completing a task", "eventPriceUsd": 0.95 } }

1import asyncio 2import logging 3 4from apify.log import ActorLogFormatter 5 6from .main import main 7 8handler = logging.StreamHandler() 9handler.setFormatter(ActorLogFormatter()) 10 11apify_client_logger = logging.getLogger('apify_client') 12apify_client_logger.setLevel(logging.ERROR) 13apify_client_logger.addHandler(handler) 14 15apify_logger = logging.getLogger('apify') 16apify_logger.setLevel(logging.INFO) 17apify_logger.addHandler(handler) 18 19# Execute the Actor entry point. 20asyncio.run(main())

1from crewai import Agent # type: ignore[import-untyped] 2 3from src.tools import ( 4 GetActorCodeTool, 5 GetActorInputSchemaTool, 6 GetActorPricingInfoTool, 7 GetActorReadmeTool, 8 SearchRelatedActorsTool, 9) 10 11from src.const import PEDANTIC_MESSAGE_BACKSTORY, PEDANTIC_MESSAGE, SUGGESTIONS_MESSAGE 12 13 14def create_actor_inspector_agent(llm: str, debug: bool = False, pedantic: bool = False) -> Agent: 15 """ 16 Agent for project management. 17 18 Args: 19 llm (str): LLM model name. 20 debug (bool): If True, the agent will operate in verbose mode for debugging purposes. 21 pedantic (bool): If True, the agent will be more pedantic. 22 23 Returns: 24 Agent: An instance of the Agent class configured for project management. 25 """ 26 return Agent( 27 role='Lead actor inspector', 28 goal=( 29 'Coordinate a comprehensive quality review of an Apify Actor by synthesizing detailed reports from ' 30 'specialized agents and delivering a final assessment with clear ratings and justifications.' 31 f'{PEDANTIC_MESSAGE if pedantic else ""}' 32 f'{SUGGESTIONS_MESSAGE}' 33 ), 34 backstory=( 35 "I'm a veteran project manager with a deep understanding of Apify Actors, skilled at orchestrating teams " 36 'of expert agents. My strength lies in distilling complex analyses into concise, actionable reports that ' 37 'drive improvement and ensure quality.' 38 f'{PEDANTIC_MESSAGE_BACKSTORY if pedantic else ""}' 39 ), 40 allow_delegation=True, 41 verbose=debug, 42 llm=llm, 43 ) 44 45 46def create_code_quality_agent(llm: str, debug: bool = False, pedantic: bool = False) -> Agent: 47 """ 48 Create an Agent instance configured for code quality inspection. 49 50 Args: 51 llm (str): LLM model name. 52 debug (bool): If True, the agent will operate in verbose mode for debugging purposes. 53 pedantic (bool): If True, the agent will be more pedantic. 54 Returns: 55 Agent: An instance of the Agent class configured for code quality inspection. 56 """ 57 tools = [GetActorCodeTool()] 58 return Agent( 59 role='Code quality specialist', 60 goal=( 61 'Deliver a precise evaluation of the code quality for a GitHub repository, focusing on tests, ' 62 'linting, code smells, security vulnerabilities, performance issues, and code style consistency.' 63 f'{PEDANTIC_MESSAGE if pedantic else ""}' 64 f'{SUGGESTIONS_MESSAGE}' 65 ), 66 backstory=( 67 "I'm a seasoned software engineer with over a decade of experience auditing codebases for startups " 68 'and enterprises alike. Armed with tools like GitHub repository analysis, I excel at identifying ' 69 'strengths and weaknesses in code quality, offering actionable insights to improve reliability, ' 70 'maintainability, and performance.' 71 f'{PEDANTIC_MESSAGE_BACKSTORY if pedantic else ""}' 72 ), 73 tools=tools, 74 verbose=debug, 75 llm=llm, 76 ) 77 78 79def create_actor_definition_quality_agent(llm: str, debug: bool = False, pedantic: bool = False) -> Agent: 80 """ 81 Create an Agent instance configured for Apify Actor quality inspection. 82 83 Args: 84 llm (str): LLM model name. 85 debug (bool): If True, the agent will operate in verbose mode for debugging purposes. 86 pedantic (bool): If True, the agent will be more pedantic. 87 88 Returns: 89 Agent: An instance of the Agent class configured for Apify Actor quality inspection. 90 """ 91 tools = [GetActorInputSchemaTool(), GetActorReadmeTool()] 92 return Agent( 93 role='Apify Actor definition evaluator', 94 goal=( 95 "Assess the quality of an Apify Actor's documentation and usability by analyzing its README clarity, " 96 'input properties, ease of use, example provision, and GitHub link visibility.' 97 f'{PEDANTIC_MESSAGE if pedantic else ""}' 98 f'{SUGGESTIONS_MESSAGE}' 99 ), 100 backstory=( 101 "I'm a meticulous Apify expert with years of experience reviewing Actors for usability and documentation " 102 'excellence. Using tools to fetch READMEs and input schemas, I ensure Actors are intuitive and ' 103 'well-documented, helping users adopt them seamlessly while meeting Apify Store standards.' 104 f'{PEDANTIC_MESSAGE_BACKSTORY if pedantic else ""}' 105 ), 106 tools=tools, 107 verbose=debug, 108 llm=llm, 109 ) 110 111 112def create_uniqueness_check_agent(llm: str, debug: bool = False, pedantic: bool = False) -> Agent: 113 """ 114 Create an Agent instance configured for code quality inspection. 115 116 Args: 117 llm (str): LLM model name. 118 debug (bool): If True, the agent will operate in verbose mode for debugging purposes. 119 pedantic (bool): If True, the agent will be more pedantic. 120 Returns: 121 Agent: An instance of the Agent class configured for code quality inspection. 122 """ 123 tools = [GetActorReadmeTool(), SearchRelatedActorsTool()] 124 return Agent( 125 role='Apify Actor uniqueness expert', 126 goal=( 127 'Compare an Actors functionality and uniqueness by reading its README and ' 128 'searching related Actors using keywords.\n' 129 'Provide a very short report with one of these grades:\n' 130 'GREAT (unique), GOOD (some similarity), BAD (similar to others).\n' 131 'Always explain (briefly!) functional differences.\n\n' 132 'Example output:\n' 133 'Actor: apify/instagram-scraper\n' 134 'Uniqueness score: GOOD\n' 135 'Explanation: There are some Instagram Actors offering similar functionality.\n' 136 ), 137 backstory=( 138 'I am an Apify expert familiar with the platform and its Actors.\n' 139 'My tools include retrieving Actor README and performing full-text searches to ' 140 'find related Actors. I need to execute search multiple times with different sets ' 141 'of keywords. I need to gather at least a couple of related Actors to provide a good comparison.' 142 f'{PEDANTIC_MESSAGE_BACKSTORY if pedantic else ""}' 143 ), 144 tools=tools, 145 verbose=debug, 146 llm=llm, 147 ) 148 149 150def create_pricing_check_agent(llm: str, debug: bool = False, pedantic: bool = False) -> Agent: 151 """ 152 Create an Agent instance configured for pricing comparison of Apify Actors. 153 154 Args: 155 llm (str): LLM model name. 156 debug (bool): If True, the agent will operate in verbose mode. 157 pedantic (bool): If True, the agent will be more pedantic. 158 159 Returns: 160 Agent: An instance of the Agent class configured for pricing analysis. 161 """ 162 tools = [GetActorPricingInfoTool(), SearchRelatedActorsTool()] 163 return Agent( 164 role='Apify pricing expert', 165 goal=( 166 "Compare an Actor's pricing by retrieving its pricing information and " 167 'searching for related Actors using keywords.\n' 168 'Apify pricing models:\n' 169 '- 5$ Free Plan: Use selected Actors free of charge, paying only platform usage costs.\n' 170 '- Rental Model: After a trial, pay a flat monthly fee; developers receive 80% of the fees.\n' 171 '- Pay-per-Result: Pay only for the results produced, with no extra usage fees.\n' 172 '- Pay-per-Event: Pay for each specific action or event.\n' 173 '- Pay-per-Usage: Pay based on the Apify platform usage generated when running the Actor.\n' 174 'Provide a very short report with one of these ratings:\n' 175 'GREAT (competitive pricing), GOOD (moderate), BAD (expensive).\n' 176 f'{PEDANTIC_MESSAGE if pedantic else ""}' 177 f'{SUGGESTIONS_MESSAGE}' 178 'Include a brief explanation;\n\n' 179 'Example output:\n' 180 'Actor: apify/xyz-actor\n' 181 'Pricing rating: GOOD\n' 182 'Explanation: The price per event is moderate compared to similar Actors.\n' 183 ), 184 backstory=( 185 'I am an Apify expert specialized in pricing analysis. My tools help retrieve pricing details and perform' 186 'full-text searches to find related Actors. I evaluate overall pricing competitiveness.\n' 187 'I am able to perform multiple searches with different sets of keywords.\n' 188 'I am able to compare different pricing models. For example, when an Actor is paid per platform usage, ' 189 "I need to retrieve Apify's pricing plans for the platform and compare them with other pricing models.\n" 190 f'{PEDANTIC_MESSAGE_BACKSTORY if pedantic else ""}' 191 ), 192 tools=tools, 193 verbose=debug, 194 llm=llm, 195 )

1REQUESTS_TIMEOUT_SECS: float = 10.0 2PEDANTIC_MESSAGE = 'Conduct an extremely thorough and detailed analysis with strict attention to every aspect. ' 3PEDANTIC_MESSAGE_BACKSTORY = ('I am a perfectionist who examines every detail with meticulous care,' 4 ' similar to a skilled detective.') 5 6SUGGESTIONS_MESSAGE = 'Provide a detailed description of issue with suggestions to fix it.'

1"""Main file of the Actor Inspector Agent for Apify Actors""" 2 3from __future__ import annotations 4 5import logging 6import math 7 8from apify import Actor 9from crewai import Crew, Task # type: ignore[import-untyped] 10 11from src.agents import ( 12 create_actor_definition_quality_agent, 13 create_actor_inspector_agent, 14 create_code_quality_agent, 15 create_pricing_check_agent, 16 create_uniqueness_check_agent, 17) 18from src.const import PEDANTIC_MESSAGE 19 20 21async def main() -> None: 22 """Main entry point for the Apify Actor. 23 24 This coroutine is executed using `asyncio.run()`, so it must remain an 25 asynchronous function for proper execution. 26 Asynchronous execution is required for communication with the Apify platform, 27 and it also enhances performance in 28 the field of web scraping significantly. 29 30 """ 31 async with Actor: 32 Actor.log.info('Starting Actor Inspector Agent') 33 count = math.ceil((Actor.get_env().get('memory_mbytes', 1024) or 1024) // 1024) 34 await Actor.charge(event_name='actor-start-gb', count=count) 35 Actor.log.info('Charged for Actor start %d GB', count) 36 37 actor_input = await Actor.get_input() or {} 38 Actor.log.info('Received input: %s', actor_input) 39 40 if not (actor_name := actor_input.get('actorName')): 41 await Actor.fail( 42 status_message='Missing the "actorName" attribute in the input!' 43 ' Please provide the name of the actor in the form of user-name/actor-name.', 44 ) 45 46 pedantic = actor_input.get('pedantic', True) 47 model_name = actor_input.get('modelName', 'gpt-4o-mini') 48 if debug := actor_input.get('debug', True): 49 Actor.log.setLevel(logging.DEBUG) 50 logger = logging.getLogger('apify') 51 logger.setLevel(logging.DEBUG) 52 53 inspector_agent = create_actor_inspector_agent(model_name, debug=debug, pedantic=pedantic) 54 code_quality_agent = create_code_quality_agent(model_name, debug=debug, pedantic=pedantic) 55 actor_quality_agent = create_actor_definition_quality_agent(model_name, debug=debug, pedantic=pedantic) 56 uniqueness_check_agent = create_uniqueness_check_agent(model_name, debug=debug, pedantic=pedantic) 57 pricing_check_agent = create_pricing_check_agent(model_name, debug=debug, pedantic=pedantic) 58 59 code_quality_task = Task( 60 description=( 61 f'Analyze the code quality of the Apify Actor "{actor_name}"\n' 62 'If code is not available, skip all code-related tools ' 63 'and explicitly state that the code cannot be evaluated, ' 64 'assigning an "N/A" grade. ' 65 f'{PEDANTIC_MESSAGE if pedantic else ""}' 66 'Evaluate the following criteria:\n' 67 '- **Tests**: Are tests present? Rate as "bad" (no tests), "good" ' 68 '(some tests, missing major functionality), or "great" (most ' 69 'key functionality tested). Explain briefly.\n' 70 '- **Linter**: Is a linter enabled? Rate as "bad" (not enabled), ' 71 '"good" (partially enabled), or "great" (fully enabled). Explain ' 72 'briefly.\n' 73 '- **Code smells**: Are there code smells (e.g., duplication)? ' 74 'Rate as "bad" (many), "good" (some), or "great" (none). Explain ' 75 'briefly.\n' 76 '- **Security**: Are there visible security vulnerabilities ' 77 '(e.g., outdated dependencies)? Rate as "bad" (many), "good" ' 78 '(some), or "great" (none). Explain briefly.\n' 79 '- **Performance**: Are there performance issues (e.g., ' 80 'inefficient loops)? Rate as "bad" (many), "good" (some), or ' 81 '"great" (none). Explain briefly.\n' 82 '- **Style**: Are there code style issues (e.g., inconsistent ' 83 'naming)? Rate as "bad" (many), "good" (some), or "great" (none). ' 84 'Explain briefly.\n' 85 ), 86 expected_output=( 87 'A structured report in markdown format with:\n' 88 '- A section for each criterion (Tests, Linter, Code smells, ' 89 'Security, Performance, Style).\n' 90 '- Each section includes a rating ("bad", "good", "great" or ' 91 '"N/A" if no URL) and a 1-2 sentence explanation.\n' 92 '- A brief list of suggestions for improvement if applicable.\n' 93 '- A brief overall summary (2-3 sentences) with suggestions for ' 94 'improvement if applicable.' 95 ), 96 agent=code_quality_agent, 97 ) 98 99 actor_quality_task = Task( 100 description=( 101 f'Assess the quality of the Apify Actor "{actor_name}" based on its ' 102 'documentation and usability. ' 103 f'{PEDANTIC_MESSAGE if pedantic else ""}' 104 'Evaluate the following criteria:\n' 105 '- **README clarity**: Is the README well-defined? Rate as "bad" ' 106 '(poorly defined), "good" (partially clear), or "great" (fully ' 107 'detailed). Explain briefly.\n' 108 '- **Input properties**: Are input properties clear and logical? ' 109 'Rate as "bad" (unclear), "good" (partially clear), or "great" ' 110 '(well-defined). Explain briefly.\n' 111 '- **Usability**: Is the Actor easy to use based on the README? ' 112 'Rate as "bad" (confusing), "good" (somewhat clear), or "great" ' 113 '(very intuitive). Explain briefly.\n' 114 '- **Examples**: Are usage examples provided? Rate as "bad" ' 115 '(none), "good" (some), or "great" (comprehensive). Explain ' 116 'briefly.\n' 117 '- **GitHub link**: Is the GitHub link in the README? Rate as ' 118 '"bad" (missing), "good" (present but not prominent), or "great" ' 119 '(clearly visible). Explain briefly.' 120 ), 121 expected_output=( 122 'A structured report in markdown format with:\n' 123 '- A section for each criterion (README clarity, Input ' 124 'properties, Usability, Examples, GitHub link).\n' 125 '- Each section includes a rating ("bad", "good", "great") and a ' 126 '1-2 sentence explanation.\n' 127 '- A brief list of suggestions for improvement if applicable.\n' 128 '- A brief overall summary (2-3 sentences) with suggestions for ' 129 'improvement.' 130 ), 131 agent=actor_quality_agent, 132 ) 133 134 uniqueness_task = Task( 135 description=( 136 f'Evaluate the uniqueness of the Apify Actor "{actor_name}" ' 137 'compared to similar actors. ' 138 f'{PEDANTIC_MESSAGE if pedantic else ""}' 139 'Assess the following criteria:\n' 140 '- **Comparison**: Is the Actor unique compared to peers? Rate ' 141 'as "bad" (very similar), "good" (somewhat unique), or "great" ' 142 '(highly distinct). Explain briefly.\n' 143 '- **Functionality**: Does it offer unique features? Rate as ' 144 '"bad" (none), "good" (some), or "great" (highly unique). Explain ' 145 'briefly.\n' 146 '- **Selling points**: Are there standout selling points? Rate ' 147 'as "bad" (none), "good" (some), or "great" (multiple). Explain ' 148 'briefly.' 149 ), 150 expected_output=( 151 'A structured report in markdown format with:\n' 152 '- A section for each criterion (Comparison, Functionality, ' 153 'Selling points).\n' 154 '- Each section includes a rating ("bad", "good", "great") and a ' 155 '1-2 sentence explanation.\n' 156 '- A brief overall summary (2-3 sentences) highlighting unique ' 157 'aspects and improvement ideas.' 158 ), 159 agent=uniqueness_check_agent, 160 ) 161 162 pricing_task = Task( 163 description=( 164 f'Analyze the pricing of the Apify Actor "{actor_name}" for ' 165 'competitiveness and sensibility. ' 166 f'{PEDANTIC_MESSAGE if pedantic else ""}' 167 'Evaluate the following criteria:\n' 168 '- **Competitiveness**: Is pricing competitive with similar ' 169 'Actors? Rate as "bad" (expensive), "good" (moderate), or "great" ' 170 '(highly competitive). Explain briefly.\n' 171 '- **Sensibility**: Does the pricing align with functionality? ' 172 'Rate as "bad" (not sensible), "good" (somewhat sensible), or ' 173 '"great" (very sensible). Explain briefly.\n' 174 '- **Transparency**: Are there hidden costs? Rate as "bad" ' 175 '(many), "good" (some), or "great" (none). Explain briefly.' 176 ), 177 expected_output=( 178 'A structured report in markdown format with:\n' 179 '- A section for each criterion (Competitiveness, Sensibility, ' 180 'Transparency).\n' 181 '- Each section includes a rating ("bad", "good", "great") and a ' 182 '1-2 sentence explanation.\n' 183 '- A brief list of suggestions for improvement if applicable.\n' 184 '- A brief overall summary (2-3 sentences) with pricing ' 185 'improvement suggestions.' 186 ), 187 agent=pricing_check_agent, 188 ) 189 190 final_task = Task( 191 description=( 192 f'Compile a final quality assessment for the Apify Actor ' 193 f'"{actor_name}". ' 194 'Include the Actor name and a brief summary of its purpose. ' 195 'Always Actor not Actor.' 196 f'{PEDANTIC_MESSAGE if pedantic else ""}' 197 'Summarize findings from previous tasks and assign an overall ' 198 'rating:\n' 199 '- **Code quality**: Summarize code quality findings. Rate as ' 200 '"bad", "good", or "great". Explain in 1-2 sentences.\n' 201 '- **Actor quality**: Summarize Actor quality findings. Rate as ' 202 '"bad", "good", or "great". Explain in 1-2 sentences.\n' 203 '- **Uniqueness**: Summarize uniqueness findings. Rate as "bad", ' 204 '"good", or "great". Explain in 1-2 sentences.\n' 205 '- **Pricing**: Summarize pricing findings. Rate as "bad", ' 206 '"good", or "great". Explain in 1-2 sentences.\n' 207 '- **Overall**: Provide a final rating ("bad", "good", "great") ' 208 'with a 2-3 sentence justification.' 209 ), 210 expected_output=( 211 'A concise final report in markdown format with:\n' 212 '- A header section including the Actor Name and a brief Summary ' 213 'of what the Actor does (2-3 sentences).\n' 214 '- A section for each category (Code quality, Actor quality, ' 215 'Uniqueness, Pricing, Suggestions, Overall).\n' 216 '- Each section includes a rating ("bad", "good", "great") and a ' 217 '1-2 sentence explanation.\n' 218 '- The Suggestions section provides a list of suggestions for improvement.\n' 219 '- The Overall section provides a final rating and a 2-3 sentence summary.' 220 ), 221 context=[actor_quality_task, code_quality_task, pricing_task, uniqueness_task], 222 agent=inspector_agent, 223 ) 224 225 # Create a one-man crew 226 # For more information, see https://docs.crewai.com/concepts/crews 227 crew = Crew( 228 agents=[ 229 actor_quality_agent, 230 code_quality_agent, 231 pricing_check_agent, 232 uniqueness_check_agent, 233 ], 234 tasks=[final_task], 235 manager_agent=inspector_agent, 236 ) 237 238 # Kick off the crew and get the response 239 Actor.log.info('Kicking off the crew...') 240 crew_output = crew.kickoff() 241 raw_response = crew_output.raw 242 Actor.log.debug('Raw response: %s', raw_response) 243 244 total_tokens = crew_output.token_usage.total_tokens 245 Actor.log.debug('Total tokens used: %d', total_tokens) 246 247 await Actor.push_data( 248 { 249 'actorId': actor_name, 250 'response': raw_response, 251 } 252 ) 253 Actor.log.info('Pushed the data into the dataset!') 254 await Actor.charge(event_name='task-completed', count=1) 255 Actor.log.info('Charged for task completed')

1"""This module defines Pydantic models for this project. 2 3These models are used mainly for the structured tool and LLM outputs. 4Resources: 5- https://docs.pydantic.dev/latest/concepts/models/ 6""" 7 8from __future__ import annotations 9 10from typing import Any 11 12from pydantic import BaseModel, ConfigDict, Field, RootModel 13from pydantic.alias_generators import to_camel 14 15config_to_camel = ConfigDict( 16 alias_generator=to_camel, 17 populate_by_name=True, 18 from_attributes=True, 19) 20 21 22class FinalTaskSection(BaseModel): 23 """Pydantic model for each section evaluated for the final task.""" 24 25 section: str = Field(..., description='The section title. For example "Code Quality" or "Uniqueness".') 26 grade: str = Field(..., description='The grade of the section. One of "BAD", "GOOD", "GREAT" or "N/A".') 27 reason: str = Field(..., description='The reason for the grade. For example, "The code quality is excellent".') 28 29 30class FinalTaskOutput(BaseModel): 31 """Pydantic model for the final task output.""" 32 33 report: list[FinalTaskSection] = Field(..., description='The list of sections evaluated for the final task.') 34 35 36class CodeQualityOutput(BaseModel): 37 contains_tests: bool 38 is_linter_enabled: bool 39 code_smells: bool 40 security_vulnerabilities: bool 41 performance_issues: bool 42 code_style_issues: bool 43 44 45class CodeFile(BaseModel): 46 """ 47 GithubRepoFile Pydantic model. 48 49 Attributes: 50 name: The name of the file. 51 content: The content of the file. 52 """ 53 54 name: str 55 content: str 56 57 58class CodeContext(BaseModel): 59 """ 60 GithubRepoContext Pydantic model. 61 62 Attributes: 63 tree: A dictionary representing the file tree of the repository. 64 files: A list of GithubRepoFile objects representing the files in the repository. 65 """ 66 67 tree: dict 68 files: list[CodeFile] 69 70 71class ActorInputProperty(BaseModel): 72 """Actor input property Pydantic model. 73 74 title: The title of the property. 75 description: The description of the property. 76 type: The type of the property. 77 default: The default or prefill value of the property. 78 """ 79 80 title: str 81 description: str 82 type: str 83 default: Any | None 84 85 86class ActorInputDefinition(BaseModel): 87 """Actor input definition Pydantic model. 88 89 Returned as a structured output by the `tool_get_actor_input_schema` tool. 90 91 title: The title of the input definition. 92 description: The description of the input definition. 93 properties: A dictionary of ActorInputProperty objects. 94 """ 95 96 title: str 97 description: str 98 properties: dict[str, ActorInputProperty] 99 100 101class Stats(BaseModel): 102 """Actor statistics total runs, users""" 103 104 total_runs: int | None = None 105 total_users30_days: int | None = None 106 public_actor_run_stats30_days: dict | None = None 107 model_config = config_to_camel 108 109 110# Nested model for 'pricingInfos' 111class ActorChargeEvent(BaseModel): 112 event_description: str 113 event_price_usd: float 114 event_title: str 115 model_config = config_to_camel 116 117 118class PricingPerEvent(BaseModel): 119 actor_charge_events: dict[str, ActorChargeEvent] | None = None 120 model_config = config_to_camel 121 122 123class PricingInfo(BaseModel): 124 """Pricing information: Pay per usage, Pay per results, Pay per event.""" 125 126 pricing_model: str 127 price_per_unit_usd: float | None = None 128 trial_minutes: int | None = None 129 apify_margin_percentage: float | None = None 130 minimal_max_total_charge_usd: float | None = None 131 pricing_per_event: PricingPerEvent | None = None 132 model_config = config_to_camel 133 134 135class ActorStore(BaseModel): 136 """Actor Store List Pydantic model.""" 137 138 name: str 139 username: str 140 title: str 141 description: str | None = None 142 stats: Stats | None = None 143 current_pricing_info: PricingInfo | None = None 144 url: str | None = None 145 total_stars: int | None = None 146 model_config = config_to_camel 147 148 149class ActorStoreList(RootModel): 150 """Actor Store List Pydantic model.""" 151 152 root: list[ActorStore]

1"""This module defines the tools used by the agent. 2 3Feel free to modify or add new tools to suit your specific needs. 4 5To learn how to create a new tool, see: 6- https://docs.crewai.com/concepts/tools 7""" 8 9from __future__ import annotations 10 11import datetime 12import logging 13from urllib.parse import urlparse 14 15import requests 16from apify_client import ApifyClient 17from crewai.tools import BaseTool # type: ignore[import-untyped] 18from pydantic import BaseModel, Field 19 20from src.const import REQUESTS_TIMEOUT_SECS 21from src.models import ActorInputDefinition, ActorInputProperty, ActorStoreList, CodeContext, CodeFile, PricingInfo 22from src.utils import ( 23 generate_file_tree, 24 get_actor_github_urls, 25 get_actor_latest_build, 26 get_actor_source_files, 27 get_apify_token, 28 github_repo_exists, 29) 30 31logger = logging.getLogger('apify') 32 33UITHUB_LINK = 'https://uithub.com/{repo_path}?accept=application/json&maxTokens={max_tokens}' 34 35IGNORE_FILES = [ 36 'license', 37 'package-lock.json', 38 'yarn.lock', 39 'readme.md', 40 'poetry.lock', 41 'requirements.txt', 42 'setup.py', 43 'uv.lock', 44] 45 46 47class GetActorReadmeInput(BaseModel): 48 """Input schema for GetActorReadme.""" 49 50 actor_name: str = Field(..., description='The name of the Apify Actor.') 51 52 53class GetActorReadmeTool(BaseTool): 54 name: str = 'get_actor_readme' 55 description: str = 'Fetch the README content of the specified Apify Actor.' 56 args_schema: type[BaseModel] = GetActorReadmeInput 57 58 def _run(self, actor_name: str) -> str: 59 """Execute the tool to get the README of an Apify Actor. 60 61 Args: 62 actor_name (str): The name of the Apify Actor. 63 Returns: 64 A string containing the README content. 65 """ 66 logger.info('Getting README for Actor %s', actor_name) 67 build = get_actor_latest_build(actor_name) 68 readme = build.get('actorDefinition', {}).get('readme') 69 if not readme: 70 raise ValueError(f'Failed to get the README for the Actor {actor_name}') 71 return str(readme) 72 73 74class GetActorInputSchemaInput(BaseModel): 75 """Input schema for GetActorInputSchemaTool.""" 76 77 actor_name: str = Field(..., description='The ID or name of the Apify Actor.') 78 79 80class GetActorInputSchemaTool(BaseTool): 81 name: str = 'get_actor_input_schema' 82 description: str = ( 83 'Retrieve the input schema of an Apify Actor. If no input schema is available, ' 84 'an appropriate message is returned.' 85 ) 86 args_schema: type[BaseModel] = GetActorInputSchemaInput 87 88 def _run(self, actor_name: str) -> ActorInputDefinition | str: 89 """Execute the tool to get the input schema of an Apify Actor. 90 91 Args: 92 actor_name (str): The ID or name of the Apify Actor. 93 94 Returns: 95 ActorInputDefinition: The structured input schema of the Actor 96 str: A message if the input schema is not available. 97 98 Raises: 99 ValueError: If the Actor's input schema cannot be retrieved. 100 """ 101 build = get_actor_latest_build(actor_name) 102 103 if not (actor_definition := build.get('actorDefinition')): 104 raise ValueError(f'Actor definition not found in the Actor build for Actor: {actor_name}') 105 106 if not (actor_input := actor_definition.get('input')): 107 return 'Actor input schema is not available.' 108 109 # Process properties 110 properties: dict[str, ActorInputProperty] = {} 111 for name, prop in actor_input.get('properties', {}).items(): 112 # Use prefill value if available, default value otherwise 113 prop['default'] = prop.get('prefill', prop.get('default')) 114 properties[name] = ActorInputProperty(**prop) 115 116 return ActorInputDefinition( 117 title=actor_definition.get('title'), 118 description=actor_definition.get('description'), 119 properties=properties, 120 ) 121 122 123class GetActorCodeInput(BaseModel): 124 """Input schema for GetCodeContextTool.""" 125 126 actor_name: str = Field(..., description='The ID or name of the Apify Actor.') 127 max_tokens: int = Field(120_000, description='Maximum number of tokens to retrieve. Default is 120,000.', gt=0) 128 129 130class GetActorCodeTool(BaseTool): 131 name: str = 'get_actor_code' 132 description: str = ( 133 'Retrieve source code for a specified Apify Actor, including the file tree and file contents. ' 134 'If no data is available from the Actor, the tool attempts to fetch code from GitHub.' 135 ) 136 args_schema: type[BaseModel] = GetActorCodeInput 137 138 @staticmethod 139 def _get_code_from_github(repo_urls: list[str], max_tokens: int) -> CodeContext | None: 140 """Get code context from GitHub repository.""" 141 142 for repo_url in repo_urls: 143 if not github_repo_exists(repo_url): 144 continue 145 146 parsed_url = urlparse(repo_url) 147 repo_path = parsed_url.path.strip('/').replace('.git', '').split('#')[0] 148 149 url = UITHUB_LINK.format(repo_path=repo_path, max_tokens=max_tokens) 150 response = requests.get(url, timeout=REQUESTS_TIMEOUT_SECS) 151 data = response.json() 152 153 tree = data['tree'] 154 filtered_repo_files = [ 155 CodeFile(name=name, content=file['content']) 156 for name, file in data.get('files', {}).items() 157 if not any(substring in name.lower() for substring in IGNORE_FILES) and file['type'] == 'content' 158 ] 159 return CodeContext(tree=tree, files=filtered_repo_files) 160 return None 161 162 @staticmethod 163 def _get_code_from_source(source_files: list[dict]) -> CodeContext: 164 """Get code from source files.""" 165 166 tree = generate_file_tree(source_files) 167 filtered_source_files = [ 168 CodeFile(name=file['name'], content=file['content']) 169 for file in source_files 170 if not any(substring in file['name'].lower() for substring in IGNORE_FILES) 171 ] 172 return CodeContext(tree=tree, files=filtered_source_files) 173 174 def _run(self, actor_name: str, max_tokens: int = 120_000) -> CodeContext | str: 175 """Execute the tool to retrieve the source code for Apify Actor. 176 177 Args: 178 actor_name (str): The ID or name of the Apify Actor. 179 max_tokens (int, optional): Maximum number of tokens to retrieve. Defaults to 120_000. 180 181 Returns: 182 CodeContext: Structured code context of the specified Actor. 183 str: An error message if code context cannot be retrieved at all. 184 """ 185 logger.info('Get code context for Actor %s, max_tokens=%s', actor_name, max_tokens) 186 # Try to get the source files 187 if source_files := get_actor_source_files(actor_name): 188 return self._get_code_from_source(source_files) 189 190 # Fall back to GitHub repository 191 repo_urls = get_actor_github_urls(actor_name) 192 logger.info('Falling back to GitHub URL for Actor %s, repo URLs: %s', actor_name, repo_urls) 193 194 if code_context := self._get_code_from_github(repo_urls, max_tokens): 195 return code_context 196 197 # If no data is available, return an error message 198 return f'Code for Actor {actor_name} is not available. Skip the tool and grade the code as N/A.' 199 200 201class SearchRelatedActorsInput(BaseModel): 202 """Input schema for SearchRelatedActors.""" 203 204 search: str = Field( 205 ..., 206 description='A string of keywords to search by. The search is performed across the title,' 207 'name, description, username, and README of an Actor.', 208 ) 209 limit: int = Field(10, description='The maximum number of Actors to return', gt=0, le=100) 210 offset: int = Field(0, description='The number of items to skip from the start of the results.', ge=0) 211 212 213class SearchRelatedActorsTool(BaseTool): 214 name: str = 'search_related_actors' 215 description: str = ( 216 'Discover available Actors using a full-text search with specified keywords.' 217 'The tool returns a list of Actors, including details such as name, description, run statistics,' 218 'and pricing information, number of stars, and URL.' 219 'Search with only few keywords, otherwise it will return empty results' 220 ) 221 args_schema: type[BaseModel] = SearchRelatedActorsInput 222 223 def _run(self, search: str, limit: int = 10, offset: int = 0) -> ActorStoreList | None: 224 """Execute the tool's logic to search related actors by keyword.""" 225 try: 226 logger.info('Search related Actors with key words: %s', search) 227 apify_client = ApifyClient(token=get_apify_token()) 228 search_results = apify_client.store().list(limit=limit, offset=offset, search=search).items 229 logger.info('Found %s Actors related with key words: %s', len(search_results), search) 230 return ActorStoreList.model_validate(search_results, strict=False) 231 except Exception as e: 232 logger.exception(f"Failed to search for Actors related to '{search}'") 233 raise ValueError(f"Failed to search for Actors related to '{search}': {e}") from None 234 235 236class GetActorPricingInfoInput(BaseModel): 237 """Input schema for GetActorPricingInformation.""" 238 239 actor_name: str = Field(..., description='The name of the Apify Actor.') 240 241 242class GetActorPricingInfoTool(BaseTool): 243 name: str = 'get_actor_pricing_information' 244 description: str = 'Fetch and return the pricing information of an Apify Actor.' 245 args_schema: type[BaseModel] = GetActorPricingInfoInput 246 247 def _run(self, actor_name: str) -> PricingInfo | None: 248 logger.info('Get pricing information for actor %s', actor_name) 249 apify_client = ApifyClient(token=get_apify_token()) 250 actor = apify_client.actor(actor_name).get() 251 if not actor: 252 raise ValueError(f'Actor {actor_name} not found.') 253 254 pricing_info = actor.get('pricingInfos') 255 if not pricing_info: 256 return PricingInfo(pricing_model='PAY_PER_PLATFORM_USAGE') 257 258 current_pricing = None 259 now = datetime.datetime.now(datetime.UTC) 260 for pricing_entry in pricing_info: 261 if pricing_entry.get('startedAt') > now: 262 break 263 current_pricing = pricing_entry 264 265 return PricingInfo.model_validate(current_pricing)

1import logging 2import os 3from typing import Any 4 5import requests 6from apify_client import ApifyClient 7 8from src.const import REQUESTS_TIMEOUT_SECS 9 10APIFY_API_GET_DEFAULT_BUILD = 'https://api.apify.com/v2/acts/{actor_id}/builds/default' 11 12logger = logging.getLogger('apify') 13 14 15def get_apify_token() -> str: 16 """ 17 Retrieve the Apify API token from environment variables. 18 19 Returns: 20 str: The Apify API token. 21 22 Raises: 23 ValueError: If the APIFY_TOKEN environment variable is not set. 24 """ 25 if not (token := os.getenv('APIFY_TOKEN')): 26 raise ValueError('APIFY_TOKEN environment variable is not set') 27 return token 28 29 30def get_actor_id(actor_name: str) -> str: 31 """ 32 Retrieve the Actor ID for a given Actor name. 33 34 Args: 35 actor_name (str): The name of the Actor. 36 37 Returns: 38 str: The ID of the Actor. 39 40 Raises: 41 ValueError: If the Actor is not found or the Actor ID cannot be retrieved. 42 """ 43 logger.debug('Get Actor ID for Actor %s', actor_name) 44 apify_client = ApifyClient(token=get_apify_token()) 45 if not (actor := apify_client.actor(actor_name).get()): 46 raise ValueError(f'Actor {actor_name} not found.') 47 48 if not (actor_id := actor.get('id')): 49 raise ValueError(f'Failed to get the Actor object ID for {actor_name}.') 50 51 return str(actor_id) 52 53 54def generate_file_tree(files: list[dict]) -> dict: 55 """ 56 Generate a file tree hierarchy from a list of file dictionaries. 57 58 Args: 59 files (list): List of dictionaries containing 'name' keys with file paths 60 61 Returns: 62 dict: Nested dictionary representing the file tree structure 63 """ 64 tree: dict = {} 65 66 for file_info in files: 67 # Split the path into components 68 path_parts = file_info['name'].split('/') 69 70 # Start with the root of our tree 71 current = tree 72 73 # Process each part of the path 74 for i, part in enumerate(path_parts): 75 # If it's the last part (the file), set it to None 76 if i == len(path_parts) - 1: 77 current[part] = None 78 # Otherwise, it's a directory 79 else: 80 # If the directory doesn't exist yet, create it 81 if part not in current: 82 current[part] = {} 83 # Move to the next level 84 current = current[part] 85 86 return tree 87 88 89def get_actor_github_urls(actor_name: str) -> list[str]: 90 """ 91 Retrieve the GitHub repository URLs associated with an Actor. 92 93 Args: 94 actor_name (str): The name of the Actor. 95 96 Returns: 97 list[str]: A list of GitHub repository URLs associated with the Actor. 98 99 Raises: 100 ValueError: If the Actor is not found or the Actor ID cannot be retrieved. 101 """ 102 logger.debug('Get GitHub URLs for Actor %s', actor_name) 103 apify_client = ApifyClient(token=get_apify_token()) 104 actor_id = get_actor_id(actor_name) 105 github_urls = [] 106 build = get_actor_latest_build(actor_id) 107 if github_repo_url := build.get('actVersion', {}).get('gitRepoUrl'): 108 github_urls.append(github_repo_url) 109 110 versions = apify_client.actor(actor_id).versions().list().items 111 github_urls.extend(version.get('gitRepoUrl') for version in versions if version.get('gitRepoUrl')) 112 113 return github_urls 114 115 116def github_repo_exists(repository_url: str) -> bool: 117 """ 118 Check if a GitHub repository exists. 119 120 Args: 121 repository_url (str): The URL of the GitHub repository. 122 123 Returns: 124 bool: True if the repository exists, False otherwise. 125 """ 126 verify_response = requests.get(repository_url, timeout=REQUESTS_TIMEOUT_SECS) 127 return bool(verify_response.status_code == requests.codes.ok) 128 129 130def get_actor_source_files(actor_name: str) -> list[dict]: 131 """ 132 Retrieve the source files for the latest version of an Actor from the default build tag. 133 134 Args: 135 actor_name (str): The name of the Actor. 136 137 Returns: 138 list[dict]: A list of dictionaries representing the source files of the Actor. 139 """ 140 logger.debug('Get source files for Actor %s', actor_name) 141 apify_client = ApifyClient(token=get_apify_token()) 142 actor_id = get_actor_id(actor_name) 143 versions = apify_client.actor(actor_id).versions().list().items 144 if latest_version := next((x for x in versions if x.get('buildTag') == 'latest'), None): 145 source_files = latest_version.get('sourceFiles', []) 146 return [file for file in source_files if file.get('format', '').lower() == 'text'] 147 return [] 148 149 150def get_actor_latest_build(actor_name: str) -> dict[str, Any]: 151 """Get the latest build of an Actor from the default build tag. 152 153 Args: 154 actor_name (str): Actor name from Apify store to run. 155 156 Returns: 157 dict: The latest build of the Actor. 158 159 Raises: 160 ValueError: If the Actor is not found or the build data is not found. 161 TypeError: If the build is not a dictionary. 162 """ 163 actor_obj_id = get_actor_id(actor_name) 164 url = APIFY_API_GET_DEFAULT_BUILD.format(actor_id=actor_obj_id) 165 166 logger.debug('Get latest build for Actor URL: %s', url) 167 response = requests.request('GET', url, timeout=REQUESTS_TIMEOUT_SECS) 168 169 build = response.json() 170 if not isinstance(build, dict): 171 msg = f'Failed to get the latest build of the Actor {actor_name}.' 172 raise TypeError(msg) 173 174 if (data := build.get('data')) is None: 175 msg = f'Failed to get the latest build data of the Actor {actor_name}.' 176 raise ValueError(msg) 177 178 if not isinstance(data, dict): 179 msg = f'Received invalid data for the latest build of the Actor {actor_name}.' 180 raise TypeError(msg) 181 182 return data

name: Deploy Actor to Apify on: push: branches: - main jobs: deploy-to-apify: runs-on: ubuntu-latest steps: - name: Checkout sources uses: actions/checkout@v4 - name: Set up Node.js uses: actions/setup-node@v4 with: node-version: 22 - name: Push to Apify uses: apify/push-actor-action@master with: token: ${{ secrets.APIFY_TOKEN }}

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.actor/actor.json

.actor/dataset_schema.json

.actor/input_schema.json

.actor/pay_per_event.json

docs/agent_actor_inspector.png

src/init.py

src/main.py

src/agents.py

src/const.py

src/main.py

src/models.py

src/py.typed

src/tools.py

src/utils.py

.github/workflows/deploy_actor.yml

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.dockerignore

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Makefile

pyproject.toml

uv.lock

.actor/Dockerfile

.actor/actor.json

.actor/dataset_schema.json

.actor/input_schema.json

.actor/pay_per_event.json

docs/agent_actor_inspector.png

src/__init__.py

src/__main__.py

src/agents.py

src/const.py

src/main.py

src/models.py

src/py.typed

src/tools.py

src/utils.py

.github/workflows/deploy_actor.yml

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src/main.py