UUID Generator

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UUID Generator

Try for free

This Actor Generates Unique UID Eachtime in a dict, [{ "uuid": "unique-id-here" }]

Pricing

Pay per usage

Rating

0.0

(0)

Developer

Vivek

Maintained by Community

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

.actor/actor.json

{
    "actorSpecification": 1,
    "name": "my-actor",
    "title": "Empty Python project",
    "description": "Empty project in Python.",
    "version": "0.0",
    "buildTag": "latest",
    "meta": {
        "templateId": "python-empty",
        "generatedBy": "<FILL-IN-MODEL>"
    },
    "dockerfile": "../Dockerfile"
}

src/init.py

src/main.py

1import asyncio
2
3from .main import main
4
5# Execute the Actor entry point.
6asyncio.run(main())

src/main.py

1import uuid
2from apify import Actor
3
4async def main() -> None:
5    async with Actor:
6        Actor.log.info('Generating UUID4')
7
8        await Actor.push_data({
9            "uuid": str(uuid.uuid4())
10        })

src/py.typed

.dockerignore

.git
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*.egg-info/
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# Byte-compiled / optimized / DLL files
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ENV/
env.bak/
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# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
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#  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.
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#  configuration, etc. Can be commented out to share Workspace Settings within a team
.vscode

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#  to share Project Settings within a team
.zed

AGENTS.md

1# Apify Actors Development Guide
2
3Important: Before you begin, fill in the `generatedBy` property in the meta section of `.actor/actor.json`. Replace it with the model you're currently using. This helps Apify monitor and improve AGENTS.md for specific LLM models.
4
5## What are Apify Actors?
6
7- Actors are serverless programs that run in the cloud. They're inspired by the UNIX philosophy - programs that do one thing well and can be easily combined to build complex systems.
8- Actors are programs packaged as Docker images that run in isolated containers
9
10## Core Concepts
11
12- Accept well-defined JSON input
13- Perform isolated tasks (web scraping, automation, data processing)
14- Produce structured JSON output to datasets and/or store data in key-value stores
15- Can run from seconds to hours or even indefinitely
16- Persist state and can be restarted
17
18## Do
19
20- accept well-defined JSON input and produce structured JSON output
21- use Apify SDK (`apify`) for code running ON Apify platform
22- validate input early with proper error handling and fail gracefully
23- use CheerioCrawler for static HTML content (10x faster than browsers)
24- use PlaywrightCrawler only for JavaScript-heavy sites and dynamic content
25- use router pattern (createCheerioRouter/createPlaywrightRouter) for complex crawls
26- implement retry strategies with exponential backoff for failed requests
27- use proper concurrency settings (HTTP: 10-50, Browser: 1-5)
28- set sensible defaults in `.actor/input_schema.json` for all optional fields
29- set up output schema in `.actor/output_schema.json`
30- clean and validate data before pushing to dataset
31- use semantic CSS selectors and fallback strategies for missing elements
32- respect robots.txt, ToS, and implement rate limiting with delays
33- check which tools (cheerio/playwright/crawlee) are installed before applying guidance
34
35## Don't
36
37- do not rely on `Dataset.getInfo()` for final counts on Cloud platform
38- do not use browser crawlers when HTTP/Cheerio works (massive performance gains with HTTP)
39- do not hard code values that should be in input schema or environment variables
40- do not skip input validation or error handling
41- do not overload servers - use appropriate concurrency and delays
42- do not scrape prohibited content or ignore Terms of Service
43- do not store personal/sensitive data unless explicitly permitted
44- do not use deprecated options like `requestHandlerTimeoutMillis` on CheerioCrawler (v3.x)
45- do not use `additionalHttpHeaders` - use `preNavigationHooks` instead
46
47## Commands
48
49```bash
50# Local development
51apify run                              # Run Actor locally
52
53# Authentication & deployment
54apify login                            # Authenticate account
55apify push                             # Deploy to Apify platform
56
57# Help
58apify help                             # List all commands
59```
60
61## Safety and Permissions
62
63Allowed without prompt:
64
65- read files with `Actor.get_value()`
66- push data with `Actor.push_data()`
67- set values with `Actor.set_value()`
68- enqueue requests to RequestQueue
69- run locally with `apify run`
70
71Ask first:
72
73- npm/pip package installations
74- apify push (deployment to cloud)
75- proxy configuration changes (requires paid plan)
76- Dockerfile changes affecting builds
77- deleting datasets or key-value stores
78
79## Project Structure
80
81.actor/
82├── actor.json # Actor config: name, version, env vars, runtime settings
83├── input_schema.json # Input validation & Console form definition
84└── output_schema.json # Specifies where an Actor stores its output
85src/
86└── main.js # Actor entry point and orchestrator
87storage/ # Local storage (mirrors Cloud during development)
88├── datasets/ # Output items (JSON objects)
89├── key_value_stores/ # Files, config, INPUT
90└── request_queues/ # Pending crawl requests
91Dockerfile # Container image definition
92AGENTS.md # AI agent instructions (this file)
93
94## Actor Input Schema
95
96The input schema defines the input parameters for an Actor. It's a JSON object comprising various field types supported by the Apify platform.
97
98### Structure
99
100```json
101{
102    "title": "<INPUT-SCHEMA-TITLE>",
103    "type": "object",
104    "schemaVersion": 1,
105    "properties": {
106        /* define input fields here */
107    },
108    "required": []
109}
110```
111
112### Example
113
114```json
115{
116    "title": "E-commerce Product Scraper Input",
117    "type": "object",
118    "schemaVersion": 1,
119    "properties": {
120        "startUrls": {
121            "title": "Start URLs",
122            "type": "array",
123            "description": "URLs to start scraping from (category pages or product pages)",
124            "editor": "requestListSources",
125            "default": [{ "url": "https://example.com/category" }],
126            "prefill": [{ "url": "https://example.com/category" }]
127        },
128        "followVariants": {
129            "title": "Follow Product Variants",
130            "type": "boolean",
131            "description": "Whether to scrape product variants (different colors, sizes)",
132            "default": true
133        },
134        "maxRequestsPerCrawl": {
135            "title": "Max Requests per Crawl",
136            "type": "integer",
137            "description": "Maximum number of pages to scrape (0 = unlimited)",
138            "default": 1000,
139            "minimum": 0
140        },
141        "proxyConfiguration": {
142            "title": "Proxy Configuration",
143            "type": "object",
144            "description": "Proxy settings for anti-bot protection",
145            "editor": "proxy",
146            "default": { "useApifyProxy": false }
147        },
148        "locale": {
149            "title": "Locale",
150            "type": "string",
151            "description": "Language/country code for localized content",
152            "default": "cs",
153            "enum": ["cs", "en", "de", "sk"],
154            "enumTitles": ["Czech", "English", "German", "Slovak"]
155        }
156    },
157    "required": ["startUrls"]
158}
159```
160
161## Actor Output Schema
162
163The Actor output schema builds upon the schemas for the dataset and key-value store. It specifies where an Actor stores its output and defines templates for accessing that output. Apify Console uses these output definitions to display run results.
164
165### Structure
166
167```json
168{
169    "actorOutputSchemaVersion": 1,
170    "title": "<OUTPUT-SCHEMA-TITLE>",
171    "properties": {
172        /* define your outputs here */
173    }
174}
175```
176
177### Example
178
179```json
180{
181    "actorOutputSchemaVersion": 1,
182    "title": "Output schema of the files scraper",
183    "properties": {
184        "files": {
185            "type": "string",
186            "title": "Files",
187            "template": "{{links.apiDefaultKeyValueStoreUrl}}/keys"
188        },
189        "dataset": {
190            "type": "string",
191            "title": "Dataset",
192            "template": "{{links.apiDefaultDatasetUrl}}/items"
193        }
194    }
195}
196```
197
198### Output Schema Template Variables
199
200- `links` (object) - Contains quick links to most commonly used URLs
201- `links.publicRunUrl` (string) - Public run url in format `https://console.apify.com/view/runs/:runId`
202- `links.consoleRunUrl` (string) - Console run url in format `https://console.apify.com/actors/runs/:runId`
203- `links.apiRunUrl` (string) - API run url in format `https://api.apify.com/v2/actor-runs/:runId`
204- `links.apiDefaultDatasetUrl` (string) - API url of default dataset in format `https://api.apify.com/v2/datasets/:defaultDatasetId`
205- `links.apiDefaultKeyValueStoreUrl` (string) - API url of default key-value store in format `https://api.apify.com/v2/key-value-stores/:defaultKeyValueStoreId`
206- `links.containerRunUrl` (string) - URL of a webserver running inside the run in format `https://<containerId>.runs.apify.net/`
207- `run` (object) - Contains information about the run same as it is returned from the `GET Run` API endpoint
208- `run.defaultDatasetId` (string) - ID of the default dataset
209- `run.defaultKeyValueStoreId` (string) - ID of the default key-value store
210
211## Dataset Schema Specification
212
213The dataset schema defines how your Actor's output data is structured, transformed, and displayed in the Output tab in the Apify Console.
214
215### Example
216
217Consider an example Actor that calls `Actor.pushData()` to store data into dataset:
218
219```python
220# Dataset push example (Python)
221import asyncio
222from datetime import datetime
223from apify import Actor
224
225async def main():
226    await Actor.init()
227
228    # Actor code
229    await Actor.push_data({
230        'numericField': 10,
231        'pictureUrl': 'https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_92x30dp.png',
232        'linkUrl': 'https://google.com',
233        'textField': 'Google',
234        'booleanField': True,
235        'dateField': datetime.now().isoformat(),
236        'arrayField': ['#hello', '#world'],
237        'objectField': {},
238    })
239
240    # Exit successfully
241    await Actor.exit()
242
243if __name__ == '__main__':
244    asyncio.run(main())
245```
246
247To set up the Actor's output tab UI, reference a dataset schema file in `.actor/actor.json`:
248
249```json
250{
251    "actorSpecification": 1,
252    "name": "book-library-scraper",
253    "title": "Book Library Scraper",
254    "version": "1.0.0",
255    "storages": {
256        "dataset": "./dataset_schema.json"
257    }
258}
259```
260
261Then create the dataset schema in `.actor/dataset_schema.json`:
262
263```json
264{
265    "actorSpecification": 1,
266    "fields": {},
267    "views": {
268        "overview": {
269            "title": "Overview",
270            "transformation": {
271                "fields": [
272                    "pictureUrl",
273                    "linkUrl",
274                    "textField",
275                    "booleanField",
276                    "arrayField",
277                    "objectField",
278                    "dateField",
279                    "numericField"
280                ]
281            },
282            "display": {
283                "component": "table",
284                "properties": {
285                    "pictureUrl": {
286                        "label": "Image",
287                        "format": "image"
288                    },
289                    "linkUrl": {
290                        "label": "Link",
291                        "format": "link"
292                    },
293                    "textField": {
294                        "label": "Text",
295                        "format": "text"
296                    },
297                    "booleanField": {
298                        "label": "Boolean",
299                        "format": "boolean"
300                    },
301                    "arrayField": {
302                        "label": "Array",
303                        "format": "array"
304                    },
305                    "objectField": {
306                        "label": "Object",
307                        "format": "object"
308                    },
309                    "dateField": {
310                        "label": "Date",
311                        "format": "date"
312                    },
313                    "numericField": {
314                        "label": "Number",
315                        "format": "number"
316                    }
317                }
318            }
319        }
320    }
321}
322```
323
324### Structure
325
326```json
327{
328    "actorSpecification": 1,
329    "fields": {},
330    "views": {
331        "<VIEW_NAME>": {
332            "title": "string (required)",
333            "description": "string (optional)",
334            "transformation": {
335                "fields": ["string (required)"],
336                "unwind": ["string (optional)"],
337                "flatten": ["string (optional)"],
338                "omit": ["string (optional)"],
339                "limit": "integer (optional)",
340                "desc": "boolean (optional)"
341            },
342            "display": {
343                "component": "table (required)",
344                "properties": {
345                    "<FIELD_NAME>": {
346                        "label": "string (optional)",
347                        "format": "text|number|date|link|boolean|image|array|object (optional)"
348                    }
349                }
350            }
351        }
352    }
353}
354```
355
356**Dataset Schema Properties:**
357
358- `actorSpecification` (integer, required) - Specifies the version of dataset schema structure document (currently only version 1)
359- `fields` (JSONSchema object, required) - Schema of one dataset object (use JsonSchema Draft 2020-12 or compatible)
360- `views` (DatasetView object, required) - Object with API and UI views description
361
362**DatasetView Properties:**
363
364- `title` (string, required) - Visible in UI Output tab and API
365- `description` (string, optional) - Only available in API response
366- `transformation` (ViewTransformation object, required) - Data transformation applied when loading from Dataset API
367- `display` (ViewDisplay object, required) - Output tab UI visualization definition
368
369**ViewTransformation Properties:**
370
371- `fields` (string[], required) - Fields to present in output (order matches column order)
372- `unwind` (string[], optional) - Deconstructs nested children into parent object
373- `flatten` (string[], optional) - Transforms nested object into flat structure
374- `omit` (string[], optional) - Removes specified fields from output
375- `limit` (integer, optional) - Maximum number of results (default: all)
376- `desc` (boolean, optional) - Sort order (true = newest first)
377
378**ViewDisplay Properties:**
379
380- `component` (string, required) - Only `table` is available
381- `properties` (Object, optional) - Keys matching `transformation.fields` with ViewDisplayProperty values
382
383**ViewDisplayProperty Properties:**
384
385- `label` (string, optional) - Table column header
386- `format` (string, optional) - One of: `text`, `number`, `date`, `link`, `boolean`, `image`, `array`, `object`
387
388## Key-Value Store Schema Specification
389
390The key-value store schema organizes keys into logical groups called collections for easier data management.
391
392### Example
393
394Consider an example Actor that calls `Actor.setValue()` to save records into the key-value store:
395
396```python
397# Key-Value Store set example (Python)
398import asyncio
399from apify import Actor
400
401async def main():
402    await Actor.init()
403
404    # Actor code
405    await Actor.set_value('document-1', 'my text data', content_type='text/plain')
406
407    image_id = '123'          # example placeholder
408    image_buffer = b'...'     # bytes buffer with image data
409    await Actor.set_value(f'image-{image_id}', image_buffer, content_type='image/jpeg')
410
411    # Exit successfully
412    await Actor.exit()
413
414if __name__ == '__main__':
415    asyncio.run(main())
416```
417
418To configure the key-value store schema, reference a schema file in `.actor/actor.json`:
419
420```json
421{
422    "actorSpecification": 1,
423    "name": "data-collector",
424    "title": "Data Collector",
425    "version": "1.0.0",
426    "storages": {
427        "keyValueStore": "./key_value_store_schema.json"
428    }
429}
430```
431
432Then create the key-value store schema in `.actor/key_value_store_schema.json`:
433
434```json
435{
436    "actorKeyValueStoreSchemaVersion": 1,
437    "title": "Key-Value Store Schema",
438    "collections": {
439        "documents": {
440            "title": "Documents",
441            "description": "Text documents stored by the Actor",
442            "keyPrefix": "document-"
443        },
444        "images": {
445            "title": "Images",
446            "description": "Images stored by the Actor",
447            "keyPrefix": "image-",
448            "contentTypes": ["image/jpeg"]
449        }
450    }
451}
452```
453
454### Structure
455
456```json
457{
458    "actorKeyValueStoreSchemaVersion": 1,
459    "title": "string (required)",
460    "description": "string (optional)",
461    "collections": {
462        "<COLLECTION_NAME>": {
463            "title": "string (required)",
464            "description": "string (optional)",
465            "key": "string (conditional - use key OR keyPrefix)",
466            "keyPrefix": "string (conditional - use key OR keyPrefix)",
467            "contentTypes": ["string (optional)"],
468            "jsonSchema": "object (optional)"
469        }
470    }
471}
472```
473
474**Key-Value Store Schema Properties:**
475
476- `actorKeyValueStoreSchemaVersion` (integer, required) - Version of key-value store schema structure document (currently only version 1)
477- `title` (string, required) - Title of the schema
478- `description` (string, optional) - Description of the schema
479- `collections` (Object, required) - Object where each key is a collection ID and value is a Collection object
480
481**Collection Properties:**
482
483- `title` (string, required) - Collection title shown in UI tabs
484- `description` (string, optional) - Description appearing in UI tooltips
485- `key` (string, conditional\*) - Single specific key for this collection
486- `keyPrefix` (string, conditional\*) - Prefix for keys included in this collection
487- `contentTypes` (string[], optional) - Allowed content types for validation
488- `jsonSchema` (object, optional) - JSON Schema Draft 07 format for `application/json` content type validation
489
490\*Either `key` or `keyPrefix` must be specified for each collection, but not both.
491
492## Apify MCP Tools
493
494If MCP server is configured, use these tools for documentation:
495
496- `search-apify-docs` - Search documentation
497- `fetch-apify-docs` - Get full doc pages
498
499Otherwise, reference: `@https://mcp.apify.com/`
500
501## Resources
502
503- [docs.apify.com/llms.txt](https://docs.apify.com/llms.txt) - Quick reference
504- [docs.apify.com/llms-full.txt](https://docs.apify.com/llms-full.txt) - Complete docs
505- [crawlee.dev](https://crawlee.dev) - Crawlee documentation
506- [whitepaper.actor](https://raw.githubusercontent.com/apify/actor-whitepaper/refs/heads/master/README.md) - Complete Actor specification

Dockerfile

# First, specify the base Docker image.
# You can see the Docker images from Apify at https://hub.docker.com/r/apify/.
# You can also use any other image from Docker Hub.
FROM apify/actor-python:3.13

USER myuser

# 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 --chown=myuser:myuser requirements.txt ./

# 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 dependencies:" \
 && pip install -r requirements.txt \
 && echo "All installed Python packages:" \
 && 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 --chown=myuser:myuser . ./

# Use compileall to ensure the runnability of the Actor Python code.
RUN python3 -m compileall -q src/

# Specify how to launch the source code of your Actor.
# By default, the "python3 -m ." command is run
CMD ["python3", "-m", "src"]

requirements.txt

1# Feel free to add your Python dependencies below. For formatting guidelines, see:
2# https://pip.pypa.io/en/latest/reference/requirements-file-format/
3
4apify < 4.0.0

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UUID Generator

.actor/actor.json

src/__init__.py

src/__main__.py

src/main.py

src/py.typed

.dockerignore

.gitignore

AGENTS.md

Dockerfile

requirements.txt

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.actor/actor.json

src/__init__.py

src/__main__.py

src/main.py

src/py.typed

.dockerignore

.gitignore

AGENTS.md

Dockerfile

requirements.txt

src/init.py

src/main.py

src/init.py

src/main.py