Zillow Agent Active Listings Scraper

Under maintenance

Pricing

$9.99/month + usage

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Zillow Agent Active Listings Scraper

Under maintenance

Try for free

Extract active property listings from any Zillow agent, including price, beds, baths, and address. Ideal for real estate tracking, agent portfolio analysis, and Zillow data automation.

Pricing

$9.99/month + usage

Rating

0.0

(0)

Developer

Zillow Scraper

Maintained by Community

Actor stats

Bookmarked

Total users

Monthly active users

a day ago

Last modified

.actor/actor.json

{
    "actorSpecification": 1,
    "name": "zillow-agent-scraper",
    "title": "Zillow Agent Data Scraper",
    "description": "Scrape agent data from Zillow including listings, reviews, details and search results.",
    "version": "0.0",
    "buildTag": "latest",
    "meta": {
      "templateId": "python-start"
    },
    "input": "./input_schema.json",
    "dockerfile": "./Dockerfile"
  }

.actor/dataset_schema.json

{
    "actorSpecification": 1,
    "fields": {},
    "views": {
        "overview": {
            "title": "Overview",
            "transformation": {
                "fields": ["title", "url", "h1s", "h2s", "h3s"]
            },
            "display": {
                "component": "table",
                "properties": {
                    "title": {
                        "label": "Title",
                        "format": "text"
                    },
                    "url": {
                        "label": "URL",
                        "format": "link"
                    },
                    "h1s": {
                        "label": "H1s",
                        "format": "array"
                    },
                    "h2s": {
                        "label": "H2s",
                        "format": "array"
                    },
                    "h3s": {
                        "label": "H3s",
                        "format": "array"
                    }
                }
            }
        }
    }
}

.actor/input_schema.json

{
    "title": "Zillow Agent Data Scraper",
    "type": "object",
    "schemaVersion": 1,
    "properties": {
      "zuid": {
        "title": "Zillow User ID (ZUID) ",
        "type": "string",
        "description": "Enter Zillow User ID ",
        "editor": "textfield",
        "prefill": "X1-ZUytn1phpg9b7t_5i26a"
      },
      "page_number": {
        "title": "Page Number (default: 1)",
        "type": "integer",
        "description": "Enter page number (default: 1)",
        "editor": "number",
        "prefill": 1
      }
    }
  }

.actor/output_schema.json

{
    "actorOutputSchemaVersion": 1,
    "title": "Output schema of the files scraper",
    "properties": {
        "overview": {
            "type": "string",
            "title": "Overview",
            "template": "{{links.apiDefaultDatasetUrl}}/items?view=overview"
        }
    }
}

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 os
2import httpx
3import asyncio
4from apify import Actor
5
6API_URL = "https://g5fqnmzzck.execute-api.us-east-1.amazonaws.com/default/zillow_agent_data"
7
8async def main():
9    async with Actor:
10        # Get input
11        actor_input = await Actor.get_input() or {}
12        data_type = "find_ActiveListings"
13        
14        if not data_type:
15            raise ValueError("Data type is required!")
16
17        # Get API key from environment
18        api_key = os.getenv("API_KEY")
19        if not api_key:
20            raise ValueError("Missing API_KEY in environment variables!")
21
22        # Prepare parameters based on data type
23        params = {"data_type": data_type}
24        
25        if data_type in ["find_ActiveListings", "find_RentalListings", "find_SoldListings", "find_reviews"]:
26            zuid = actor_input.get("zuid", "X1-ZUytn1phpg9b7t_5i26a")
27            if not zuid:
28                raise ValueError(f"ZUID is required for {data_type}!")
29            params["zuid"] = zuid
30            params["page_number"] = actor_input.get("page_number", 1)
31            
32        elif data_type == "find_details":
33            username = actor_input.get("username", "kkladakis1")
34            if not username:
35                raise ValueError("Username is required for agent details!")
36            params["username"] = username
37            
38        elif data_type == "find_search":
39            name = actor_input.get("name", "Towfik Ahmed")
40            if not name:
41                raise ValueError("Agent name is required for search!")
42            params["name"] = name
43            params["location"] = actor_input.get("location", "New York")
44            params["page_number"] = actor_input.get("page_number", 1)
45
46        # Make API request
47        headers = {"x-api-key": api_key}
48        
49        async with httpx.AsyncClient(timeout=60.0) as client:
50            response = await client.get(API_URL, headers=headers, params=params)
51            
52            if response.status_code != 200:
53                raise Exception(f"API returned {response.status_code}: {response.text}")
54                
55            data = response.json()
56            
57            # FIX: Directly push the API response without any wrapper
58            # If data is a list, push each item individually
59            if isinstance(data, list):
60                for item in data:
61                    await Actor.push_data(item)
62            else:
63                # If it's a dictionary, push directly
64                await Actor.push_data(data)
65
66if __name__ == "__main__":
67    asyncio.run(main())

src/py.typed

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AGENTS.md

1# Apify Actors Development Guide
2
3Important: Before you begin, fill in the model 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- read files with `Actor.get_value()`
65- push data with `Actor.push_data()`
66- set values with `Actor.set_value()`
67- enqueue requests to RequestQueue
68- run locally with `apify run`
69
70Ask first:
71- npm/pip package installations
72- apify push (deployment to cloud)
73- proxy configuration changes (requires paid plan)
74- Dockerfile changes affecting builds
75- deleting datasets or key-value stores
76
77## Project Structure
78
79.actor/
80├── actor.json # Actor config: name, version, env vars, runtime settings
81├── input_schema.json # Input validation & Console form definition
82└── output_schema.json # Specifies where an Actor stores its output
83src/
84└── main.js # Actor entry point and orchestrator
85storage/ # Local storage (mirrors Cloud during development)
86├── datasets/ # Output items (JSON objects)
87├── key_value_stores/ # Files, config, INPUT
88└── request_queues/ # Pending crawl requests
89Dockerfile # Container image definition
90AGENTS.md # AI agent instructions (this file)
91
92## Actor Input Schema
93
94The input schema defines the input parameters for an Actor. It's a JSON object comprising various field types supported by the Apify platform.
95
96### Structure
97
98```json
99{
100    "title": "<INPUT-SCHEMA-TITLE>",
101    "type": "object",
102    "schemaVersion": 1,
103    "properties": { /* define input fields here */ },
104    "required": []
105}
106```
107
108### Example
109
110```json
111{
112  "title": "E-commerce Product Scraper Input",
113  "type": "object",
114  "schemaVersion": 1,
115  "properties": {
116    "startUrls": {
117      "title": "Start URLs",
118      "type": "array",
119      "description": "URLs to start scraping from (category pages or product pages)",
120      "editor": "requestListSources",
121      "default": [{"url": "https://example.com/category"}],
122      "prefill": [{"url": "https://example.com/category"}]
123    },
124    "followVariants": {
125      "title": "Follow Product Variants",
126      "type": "boolean",
127      "description": "Whether to scrape product variants (different colors, sizes)",
128      "default": true
129    },
130    "maxRequestsPerCrawl": {
131      "title": "Max Requests per Crawl",
132      "type": "integer",
133      "description": "Maximum number of pages to scrape (0 = unlimited)",
134      "default": 1000,
135      "minimum": 0
136    },
137    "proxyConfiguration": {
138      "title": "Proxy Configuration",
139      "type": "object",
140      "description": "Proxy settings for anti-bot protection",
141      "editor": "proxy",
142      "default": {"useApifyProxy": false}
143    },
144    "locale": {
145      "title": "Locale",
146      "type": "string",
147      "description": "Language/country code for localized content",
148      "default": "cs",
149      "enum": ["cs", "en", "de", "sk"],
150      "enumTitles": ["Czech", "English", "German", "Slovak"]
151    }
152  },
153  "required": ["startUrls"]
154}
155```
156
157## Actor Output Schema
158
159The 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.
160
161### Structure
162
163```json
164{
165    "actorOutputSchemaVersion": 1,
166    "title": "<OUTPUT-SCHEMA-TITLE>",
167    "properties": { /* define your outputs here */ }
168}
169```
170
171### Example
172
173```json
174{
175    "actorOutputSchemaVersion": 1,
176    "title": "Output schema of the files scraper",
177    "properties": {
178        "files": {
179            "type": "string",
180            "title": "Files",
181            "template": "{{links.apiDefaultKeyValueStoreUrl}}/keys"
182        },
183        "dataset": {
184            "type": "string",
185            "title": "Dataset",
186            "template": "{{links.apiDefaultDatasetUrl}}/items"
187        }
188    }
189}
190```
191
192### Output Schema Template Variables
193
194- `links` (object) - Contains quick links to most commonly used URLs
195- `links.publicRunUrl` (string) - Public run url in format `https://console.apify.com/view/runs/:runId`
196- `links.consoleRunUrl` (string) - Console run url in format `https://console.apify.com/actors/runs/:runId`
197- `links.apiRunUrl` (string) - API run url in format `https://api.apify.com/v2/actor-runs/:runId`
198- `links.apiDefaultDatasetUrl` (string) - API url of default dataset in format `https://api.apify.com/v2/datasets/:defaultDatasetId`
199- `links.apiDefaultKeyValueStoreUrl` (string) - API url of default key-value store in format `https://api.apify.com/v2/key-value-stores/:defaultKeyValueStoreId`
200- `links.containerRunUrl` (string) - URL of a webserver running inside the run in format `https://<containerId>.runs.apify.net/`
201- `run` (object) - Contains information about the run same as it is returned from the `GET Run` API endpoint
202- `run.defaultDatasetId` (string) - ID of the default dataset
203- `run.defaultKeyValueStoreId` (string) - ID of the default key-value store
204
205## Dataset Schema Specification
206
207The dataset schema defines how your Actor's output data is structured, transformed, and displayed in the Output tab in the Apify Console.
208
209### Example
210
211Consider an example Actor that calls `Actor.pushData()` to store data into dataset:
212
213```python
214# Dataset push example (Python)
215import asyncio
216from datetime import datetime
217from apify import Actor
218
219async def main():
220    await Actor.init()
221
222    # Actor code
223    await Actor.push_data({
224        'numericField': 10,
225        'pictureUrl': 'https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_92x30dp.png',
226        'linkUrl': 'https://google.com',
227        'textField': 'Google',
228        'booleanField': True,
229        'dateField': datetime.now().isoformat(),
230        'arrayField': ['#hello', '#world'],
231        'objectField': {},
232    })
233
234    # Exit successfully
235    await Actor.exit()
236
237if __name__ == '__main__':
238    asyncio.run(main())
239```
240
241To set up the Actor's output tab UI, reference a dataset schema file in `.actor/actor.json`:
242
243```json
244{
245    "actorSpecification": 1,
246    "name": "book-library-scraper",
247    "title": "Book Library Scraper",
248    "version": "1.0.0",
249    "storages": {
250        "dataset": "./dataset_schema.json"
251    }
252}
253```
254
255Then create the dataset schema in `.actor/dataset_schema.json`:
256
257```json
258{
259    "actorSpecification": 1,
260    "fields": {},
261    "views": {
262        "overview": {
263            "title": "Overview",
264            "transformation": {
265                "fields": [
266                    "pictureUrl",
267                    "linkUrl",
268                    "textField",
269                    "booleanField",
270                    "arrayField",
271                    "objectField",
272                    "dateField",
273                    "numericField"
274                ]
275            },
276            "display": {
277                "component": "table",
278                "properties": {
279                    "pictureUrl": {
280                        "label": "Image",
281                        "format": "image"
282                    },
283                    "linkUrl": {
284                        "label": "Link",
285                        "format": "link"
286                    },
287                    "textField": {
288                        "label": "Text",
289                        "format": "text"
290                    },
291                    "booleanField": {
292                        "label": "Boolean",
293                        "format": "boolean"
294                    },
295                    "arrayField": {
296                        "label": "Array",
297                        "format": "array"
298                    },
299                    "objectField": {
300                        "label": "Object",
301                        "format": "object"
302                    },
303                    "dateField": {
304                        "label": "Date",
305                        "format": "date"
306                    },
307                    "numericField": {
308                        "label": "Number",
309                        "format": "number"
310                    }
311                }
312            }
313        }
314    }
315}
316```
317
318### Structure
319
320```json
321{
322    "actorSpecification": 1,
323    "fields": {},
324    "views": {
325        "<VIEW_NAME>": {
326            "title": "string (required)",
327            "description": "string (optional)",
328            "transformation": {
329                "fields": ["string (required)"],
330                "unwind": ["string (optional)"],
331                "flatten": ["string (optional)"],
332                "omit": ["string (optional)"],
333                "limit": "integer (optional)",
334                "desc": "boolean (optional)"
335            },
336            "display": {
337                "component": "table (required)",
338                "properties": {
339                    "<FIELD_NAME>": {
340                        "label": "string (optional)",
341                        "format": "text|number|date|link|boolean|image|array|object (optional)"
342                    }
343                }
344            }
345        }
346    }
347}
348```
349
350**Dataset Schema Properties:**
351
352- `actorSpecification` (integer, required) - Specifies the version of dataset schema structure document (currently only version 1)
353- `fields` (JSONSchema object, required) - Schema of one dataset object (use JsonSchema Draft 2020-12 or compatible)
354- `views` (DatasetView object, required) - Object with API and UI views description
355
356**DatasetView Properties:**
357
358- `title` (string, required) - Visible in UI Output tab and API
359- `description` (string, optional) - Only available in API response
360- `transformation` (ViewTransformation object, required) - Data transformation applied when loading from Dataset API
361- `display` (ViewDisplay object, required) - Output tab UI visualization definition
362
363**ViewTransformation Properties:**
364
365- `fields` (string[], required) - Fields to present in output (order matches column order)
366- `unwind` (string[], optional) - Deconstructs nested children into parent object
367- `flatten` (string[], optional) - Transforms nested object into flat structure
368- `omit` (string[], optional) - Removes specified fields from output
369- `limit` (integer, optional) - Maximum number of results (default: all)
370- `desc` (boolean, optional) - Sort order (true = newest first)
371
372**ViewDisplay Properties:**
373
374- `component` (string, required) - Only `table` is available
375- `properties` (Object, optional) - Keys matching `transformation.fields` with ViewDisplayProperty values
376
377**ViewDisplayProperty Properties:**
378
379- `label` (string, optional) - Table column header
380- `format` (string, optional) - One of: `text`, `number`, `date`, `link`, `boolean`, `image`, `array`, `object`
381
382## Key-Value Store Schema Specification
383
384The key-value store schema organizes keys into logical groups called collections for easier data management.
385
386### Example
387
388Consider an example Actor that calls `Actor.setValue()` to save records into the key-value store:
389
390```python
391# Key-Value Store set example (Python)
392import asyncio
393from apify import Actor
394
395async def main():
396    await Actor.init()
397
398    # Actor code
399    await Actor.set_value('document-1', 'my text data', content_type='text/plain')
400
401    image_id = '123'          # example placeholder
402    image_buffer = b'...'     # bytes buffer with image data
403    await Actor.set_value(f'image-{image_id}', image_buffer, content_type='image/jpeg')
404
405    # Exit successfully
406    await Actor.exit()
407
408if __name__ == '__main__':
409    asyncio.run(main())
410```
411
412To configure the key-value store schema, reference a schema file in `.actor/actor.json`:
413
414```json
415{
416    "actorSpecification": 1,
417    "name": "data-collector",
418    "title": "Data Collector",
419    "version": "1.0.0",
420    "storages": {
421        "keyValueStore": "./key_value_store_schema.json"
422    }
423}
424```
425
426Then create the key-value store schema in `.actor/key_value_store_schema.json`:
427
428```json
429{
430    "actorKeyValueStoreSchemaVersion": 1,
431    "title": "Key-Value Store Schema",
432    "collections": {
433        "documents": {
434            "title": "Documents",
435            "description": "Text documents stored by the Actor",
436            "keyPrefix": "document-"
437        },
438        "images": {
439            "title": "Images",
440            "description": "Images stored by the Actor",
441            "keyPrefix": "image-",
442            "contentTypes": ["image/jpeg"]
443        }
444    }
445}
446```
447
448### Structure
449
450```json
451{
452    "actorKeyValueStoreSchemaVersion": 1,
453    "title": "string (required)",
454    "description": "string (optional)",
455    "collections": {
456        "<COLLECTION_NAME>": {
457            "title": "string (required)",
458            "description": "string (optional)",
459            "key": "string (conditional - use key OR keyPrefix)",
460            "keyPrefix": "string (conditional - use key OR keyPrefix)",
461            "contentTypes": ["string (optional)"],
462            "jsonSchema": "object (optional)"
463        }
464    }
465}
466```
467
468**Key-Value Store Schema Properties:**
469
470- `actorKeyValueStoreSchemaVersion` (integer, required) - Version of key-value store schema structure document (currently only version 1)
471- `title` (string, required) - Title of the schema
472- `description` (string, optional) - Description of the schema
473- `collections` (Object, required) - Object where each key is a collection ID and value is a Collection object
474
475**Collection Properties:**
476
477- `title` (string, required) - Collection title shown in UI tabs
478- `description` (string, optional) - Description appearing in UI tooltips
479- `key` (string, conditional*) - Single specific key for this collection
480- `keyPrefix` (string, conditional*) - Prefix for keys included in this collection
481- `contentTypes` (string[], optional) - Allowed content types for validation
482- `jsonSchema` (object, optional) - JSON Schema Draft 07 format for `application/json` content type validation
483
484*Either `key` or `keyPrefix` must be specified for each collection, but not both.
485
486## Apify MCP Tools
487
488If MCP server is configured, use these tools for documentation:
489- `search-apify-docs` - Search documentation
490- `fetch-apify-docs` - Get full doc pages
491
492Otherwise, reference: `@https://mcp.apify.com/`
493
494## Resources
495
496- [docs.apify.com/llms.txt](https://docs.apify.com/llms.txt) - Quick reference
497- [docs.apify.com/llms-full.txt](https://docs.apify.com/llms-full.txt) - Complete docs
498- [crawlee.dev](https://crawlee.dev) - Crawlee documentation
499- [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 src" 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
5httpx~=0.24.0
6crawlee[beautifulsoup]

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.actor/actor.json

.actor/dataset_schema.json

.actor/input_schema.json

.actor/output_schema.json

src/__init__.py

src/__main__.py

src/main.py

src/py.typed

.dockerignore

.gitignore

AGENTS.md

Dockerfile

requirements.txt

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Zillow Agent Rental Listings Scraper

Zillow Agent Data Insights

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Zillow Agent Profile Intelligence

Zillow Agent Sold Listings Scraper

Zillow Agent Sales History

Zillow Search by URL Fetcher

.actor/actor.json

.actor/dataset_schema.json

.actor/input_schema.json

.actor/output_schema.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