Realestate Newsletter Agent Langgraph
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Realestate Newsletter Agent Langgraph
An autonomous Apify actor that generates comprehensive real estate market research reports by analyzing data from multiple authoritative sources.
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# Added by Apify CLInode_modulessrc/data_analysis_logic.pysrc/data_parsing_logic.pysrc/data_validation_logic.pysrc/data_validation_logic1.py
marketplae_urls.txt
1{2 "zillow": [3 "https://www.zillow.com/home-values/33839/san-jose-ca/",4 "https://www.zillow.com/home-values/276652/west-san-jose-san-jose-ca/",5 "https://www.zillow.com/home-values/12447/los-angeles-ca/",6 "https://www.zillow.com/home-values/3101/los-angeles-county-ca/",7 "https://www.zillow.com/home-values/38128/dallas-tx/",8 "https://www.zillow.com/home-values/54296/san-diego-ca/",9 "https://www.zillow.com/home-values/2841/san-diego-county-ca/",10 "https://www.zillow.com/home-values/6915/san-antonio-tx/",11 "https://www.zillow.com/home-values/40326/phoenix-az/",12 "https://www.zillow.com/home-values/39051/houston-tx/",13 "https://www.zillow.com/home-values/2402/maricopa-county-az/",14 "https://www.zillow.com/home-values/32697/maricopa-az/",15 "https://www.zillow.com/home-values/6181/new-york-ny/",16 "https://www.zillow.com/home-values/139/cook-county-il/",17 "https://www.zillow.com/home-values/13271/philadelphia-pa/",18 "https://www.zillow.com/home-values/17426/chicago-il/",19 "https://www.zillow.com/home-values/2964/miami-dade-county-fl/",20 "https://www.zillow.com/home-values/12700/miami-fl/",21 "https://www.zillow.com/home-values/1286/orange-county-ca/",22 "https://www.zillow.com/home-values/269590/lincoln-park-chicago-il/",23 "https://www.zillow.com/home-values/1090/harris-county-tx/",24 "https://www.zillow.com/home-values/1561/broward-county-fl/",25 "https://www.zillow.com/home-values/268473/silver-lake-los-angeles-ca/",26 "https://www.zillow.com/home-values/47977/the-woodlands-tx/",27 "https://www.zillow.com/home-values/403122/brooklyn-heights-brooklyn-new-york-ny/",28 "https://www.zillow.com/home-values/274893/old-town-san-diego-ca/",29 "https://www.zillow.com/home-values/403322/west-loop-gate-chicago-il/",30 "https://www.zillow.com/home-values/978/dallas-county-tx/",31 "https://www.zillow.com/home-values/274552/mission-san-francisco-ca/",32 "https://www.zillow.com/home-values/20330/san-francisco-ca/",33 "https://www.zillow.com/home-values/250206/capitol-hill-seattle-wa/",34 "https://www.zillow.com/home-values/5924/miami-beach-fl/",35 "https://www.zillow.com/home-values/155173/north-end-boston-ma/",36 "https://www.zillow.com/home-values/207/king-county-wa/"37 ],38 "redfin": [39 "https://www.redfin.com/city/17420/CA/San-Jose/housing-market",40 "https://www.redfin.com/city/11203/CA/Los-Angeles/housing-market",41 "https://www.redfin.com/city/30794/TX/Dallas/housing-market",42 "https://www.redfin.com/city/16904/CA/San-Diego/housing-market",43 "https://www.redfin.com/city/16657/TX/San-Antonio/housing-market",44 "https://www.redfin.com/city/14240/AZ/Phoenix/housing-market",45 "https://www.redfin.com/city/8903/TX/Houston/housing-market",46 "https://www.redfin.com/county/220/AZ/Maricopa-County/housing-market",47 "https://www.redfin.com/city/11323/AZ/Maricopa/housing-market",48 "https://www.redfin.com/city/30749/NY/New-York/housing-market",49 "https://www.redfin.com/state/New-York/housing-market",50 "https://www.redfin.com/county/727/IL/Cook-County/housing-market",51 "https://www.redfin.com/city/15502/PA/Philadelphia/housing-market",52 "https://www.redfin.com/county/321/CA/Los-Angeles-County/housing-market",53 "https://www.redfin.com/city/29470/IL/Chicago/housing-market",54 "https://www.redfin.com/county/479/FL/Miami-Dade-County/housing-market",55 "https://www.redfin.com/county/332/CA/Orange-County/housing-market",56 "https://www.redfin.com/city/13969/CA/Orange/housing-market",57 "https://www.redfin.com/city/16904/CA/San-Diego/housing-market#:~:text=The%20San%20Diego%20housing%20market,2.6%25%20since%20last%20year.%E2%80%A6",58 "https://www.redfin.com/county/339/CA/San-Diego-County/housing-market",59 "https://www.redfin.com/neighborhood/28211/IL/Chicago/Lincoln-Park/housing-market",60 "https://www.redfin.com/city/12211/MI/Lincoln-Park/housing-market",61 "https://www.redfin.com/county/2740/TX/Harris-County/housing-market",62 "https://www.redfin.com/county/442/FL/Broward-County/housing-market",63 "https://www.redfin.com/neighborhood/7511/CA/Los-Angeles/Silver-Lake/housing-market",64 "https://www.redfin.com/city/34977/CA/Silver-Lakes/housing-market",65 "https://www.redfin.com/city/26049/TX/The-Woodlands/housing-market",66 "https://www.redfin.com/neighborhood/224183/NY/New-York/Brooklyn-Heights/housing-market",67 "https://www.redfin.com/neighborhood/555054/CA/Lathrop/Old-Town/housing-market",68 "https://www.redfin.com/neighborhood/114536/CA/San-Diego/Old-Town/housing-market",69 "https://www.redfin.com/neighborhood/30062/IL/Chicago/West-Loop/housing-market",70 "https://www.redfin.com/county/2696/TX/Dallas-County/housing-market",71 "https://www.redfin.com/neighborhood/1352/CA/San-Francisco/Mission-District/housing-market",72 "https://www.redfin.com/neighborhood/119776/CA/San-Gabriel/Mission-District/housing-market",73 "https://www.redfin.com/neighborhood/56947/WA/Seattle/Capitol-Hill/housing-market",74 "https://www.redfin.com/city/16163/WA/Seattle/housing-market",75 "https://www.redfin.com/city/25689/FL/South-Beach/housing-market",76 "https://www.redfin.com/city/11467/FL/Miami-Beach/housing-market",77 "https://www.redfin.com/neighborhood/186088/MA/Boston/North-End/housing-market",78 "https://www.redfin.com/neighborhood/1886/MA/Boston/North-End-Waterfront/housing-market",79 "https://www.redfin.com/county/118/WA/King-County/housing-market"80 ],81 "realtor": [82 "https://www.realtor.com/realestateandhomes-search/San-Jose_CA/overview",83 "https://www.realtor.com/realestateandhomes-search/Los-Angeles_CA/overview",84 "https://www.realtor.com/realestateandhomes-search/Dallas_TX/overview",85 "https://www.realtor.com/realestateandhomes-search/San-Diego_CA/overview",86 "https://www.realtor.com/realestateandhomes-search/San-Antonio_TX/overview",87 "https://www.realtor.com/realestateandhomes-search/Phoenix_AZ/overview",88 "https://www.realtor.com/realestateandhomes-search/Houston_TX/overview",89 "https://www.realtor.com/realestateandhomes-search/Maricopa-County_AZ/overview",90 "https://www.realtor.com/realestateandhomes-search/Cook-County_IL/overview",91 "https://www.realtor.com/realestateandhomes-search/Philadelphia_PA/overview",92 "https://www.realtor.com/realestateandhomes-search/Los-Angeles-County_CA/overview",93 "https://www.realtor.com/realestateandhomes-search/Chicago_IL/overview",94 "https://www.realtor.com/realestateandhomes-search/Miami-Dade-County_FL/overview",95 "https://www.realtor.com/realestateandhomes-search/Orange-County_CA/overview",96 "https://www.realtor.com/realestateandhomes-search/San-Diego-County_CA/overview",97 "https://www.realtor.com/realestateandhomes-search/Lincoln-Park_Chicago_IL/overview",98 "https://www.realtor.com/realestateandhomes-search/Harris-County_TX/overview",99 "https://www.realtor.com/realestateandhomes-search/Broward-County_FL/overview",100 "https://www.realtor.com/realestateandhomes-search/Silver-Lake_Los-Angeles_CA/overview",101 "https://www.realtor.com/realestateandhomes-search/Silver-Lakes_CA/overview",102 "https://www.realtor.com/realestateandhomes-search/The-Woodlands_TX/overview",103 "https://www.realtor.com/realestateandhomes-search/Brooklyn-Heights_Brooklyn_NY/overview",104 "https://www.realtor.com/realestateandhomes-search/Old-Town_San-Diego_CA/overview",105 "https://www.realtor.com/realestateandhomes-search/Old-Town_Orcutt_CA/overview",106 "https://www.realtor.com/realestateandhomes-search/West-Loop_Chicago_IL/overview",107 "https://www.realtor.com/realestateandhomes-search/Mission-District_San-Francisco_CA/overview",108 "https://www.realtor.com/realestateandhomes-search/Mission-District_San-Gabriel_CA/overview",109 "https://www.realtor.com/realestateandhomes-search/Capitol-Hill_Seattle_WA/overview",110 "https://www.realtor.com/realestateandhomes-search/South-Beach_Miami-Beach_FL/overview",111 "https://www.realtor.com/realestateandhomes-search/North-End_Boston_MA/overview",112 "https://www.realtor.com/realestateandhomes-search/King-County_WA/overview"113 ],114 "rocket": [115 "https://rocket.com/homes/market-reports/ca/los-angeles-county",116 "https://rocket.com/homes/market-reports/tx/dallas",117 "https://rocket.com/homes/market-reports/ca/san-diego",118 "https://rocket.com/homes/market-reports/tx/san-antonio",119 "https://rocket.com/homes/market-reports/tx/san-antonio-central",120 "https://rocket.com/homes/market-reports/az/phoenix",121 "https://rocket.com/homes/market-reports/az/maricopa-county",122 "https://rocket.com/homes/market-reports/ny/new-york",123 "https://rocket.com/homes/market-reports/il/cook-county",124 "https://rocket.com/homes/market-reports/ca/los-angeles",125 "https://rocket.com/homes/market-reports/fl/miami",126 "https://rocket.com/homes/market-reports/ca/orange-county",127 "https://rocket.com/homes/market-reports/ca/san-diego-county",128 "https://rocket.com/homes/market-reports/il/lincoln-park",129 "https://rocket.com/homes/market-reports/tx/houston",130 "https://rocket.com/homes/market-reports/fl/broward-county",131 "https://rocket.com/homes/market-reports/ca/silver-lake",132 "https://rocket.com/homes/market-reports/tx/the-woodlands-austin",133 "https://rocket.com/homes/market-reports/ny/brooklyn-heights",134 "https://rocket.com/homes/market-reports/ca/old-town",135 "https://rocket.com/homes/market-reports/ca/old-town-camarillo",136 "https://rocket.com/homes/market-reports/il/west-loop",137 "https://rocket.com/homes/market-reports/ca/mission-road-district",138 "https://rocket.com/homes/market-reports/ca/mission",139 "https://rocket.com/homes/market-reports/wa/capitol-hill",140 "https://rocket.com/homes/market-reports/wa/capitol-hill-heights",141 "https://rocket.com/homes/market-reports/fl/miami-beach",142 "https://rocket.com/homes/market-reports/ma/north-end",143 "https://rocket.com/homes/market-reports/wa/king-county"144 ]145}
requirements.txt
1apify<3.0.02langchain-openai==0.3.63langgraph==0.2.73
.actor/actor.json
{ "actorSpecification": 1, "name": "realestate-newsletter-agent-langgraph", "title": "Python LangGraph Agent", "description": "LangGraph agent in python", "version": "0.0", "buildTag": "latest", "input": "./input_schema.json", "storages": { "dataset": "./dataset_schema.json" }, "meta": { "templateId": "python-langgraph" }, "dockerfile": "./Dockerfile"}
.actor/dataset_schema.json
{ "actorSpecification": 1, "views": { "overview": { "title": "Overview", "transformation": { "fields": ["response", "structured_response"] }, "display": { "component": "table", "properties": { "response": { "label": "Response", "format": "text" }, "structured_response": { "label": "Structured Response", "format": "object" } } } } }}
.actor/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
# Second, copy just requirements.txt into the Actor image,# since it should be the only file that affects the dependency installation in the next step,# in order to speed up the build.COPY 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 builds 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 and run as a non-root user.RUN useradd --create-home apify && \ chown -R apify:apify ./USER apify
# Specify how to launch the source code of your Actor.# By default, the "python3 -m ." command is run.CMD ["python3", "-m", "src"]
.actor/input_schema.json
{ "title": "Real Estate Newsletter Agent", "type": "object", "schemaVersion": 1, "properties": { "location": { "title": "Location", "type": "string", "description": "City and State (e.g. 'San Jose, CA')", "editor": "textfield" }, "openaiApiKey": { "title": "OpenAI API Key", "type": "string", "description": "Your OpenAI API key", "editor": "textfield", "isSecret": true }, "debug": { "title": "Debug Mode", "type": "boolean", "description": "Enable debug logging", "default": false } }, "required": ["location", "openaiApiKey"]}
.actor/pay_per_event.json
{ "actor-start": { "eventTitle": "Price for Actor start", "eventDescription": "Flat fee for starting an Actor run.", "eventPriceUsd": 0.1 }, "task-completed": { "eventTitle": "Price for completing the task", "eventDescription": "Flat fee for completing the task.", "eventPriceUsd": 0.4 }, "search-init": { "eventTitle": "Search Initialization", "eventDescription": "Charged when a new market search is initiated for a location", "eventPriceUsd": 0.02 }, "url-processed": { "eventTitle": "URL Processing", "eventDescription": "Charged per validated URL from real estate sources (Zillow, Redfin, Realtor, Rocket)", "eventPriceUsd": 0.02 }, "data-extracted": { "eventTitle": "Data Extraction", "eventDescription": "Charged per source when market data is successfully extracted from the webpage", "eventPriceUsd": 0.02 }, "market-analyzed": { "eventTitle": "Market Analysis", "eventDescription": "Charged per source when market data is successfully analyzed and validated", "eventPriceUsd": 0.02 }, "newsletter-generated": { "eventTitle": "Newsletter Generation", "eventDescription": "Charged for generating the final market analysis newsletter with compiled insights", "eventPriceUsd": 0.50 }}
src/main.py
1"""2Main entry point for the Apify Actor.3Orchestrates the autonomous real estate market research process.4"""5
6from __future__ import annotations7
8import logging9import os10from apify import Actor11from apify_client import ApifyClient12from openai import AsyncOpenAI13
14from .agents.search_agent import SearchAgent15from .agents.extraction_agent import ExtractionAgent16from .agents.analysis_agent import AnalysisAgent17from .agents.newsletter_agent import NewsletterAgent18from .models.schemas import AgentState19
20# Configure logging21logging.basicConfig(level=logging.INFO)22logger = logging.getLogger(__name__)23
24async def main() -> None:25 """Main entry point for the Apify Actor."""26 async with Actor:27 logger.info("Starting real estate market analysis actor")28 29 # Get input30 actor_input = await Actor.get_input() or {}31 logger.info(f"Received input with keys: {', '.join(actor_input.keys())}")32 33 # Set OpenAI API key from input34 openai_api_key = actor_input.get("openaiApiKey")35 36 if not openai_api_key:37 logger.error("OpenAI API key is required in input")38 return39 logger.info("OpenAI API key validated")40 41 # Get location42 location = actor_input.get("location")43 if not location:44 logger.error("Location is required")45 return46 logger.info(f"Processing location: {location}")47 48 # Set environment variable for OpenAI API key49 os.environ["OPENAI_API_KEY"] = openai_api_key50 logger.info("Environment variables set")51 52 # Initialize OpenAI client53 try:54 openai_client = AsyncOpenAI(api_key=openai_api_key)55 logger.info("OpenAI client initialized")56 except Exception as e:57 logger.error(f"Failed to initialize OpenAI client: {str(e)}")58 return59 60 # Initialize Apify client - uses token from environment automatically61 try:62 apify_client = Actor.new_client()63 logger.info("Apify client initialized")64 except Exception as e:65 logger.error(f"Failed to initialize Apify client: {str(e)}")66 return67 68 # Initialize agents with shared OpenAI client and Apify client69 try:70 newsletter_agent = NewsletterAgent(client=openai_client)71 search_agent = SearchAgent(client=openai_client)72 extraction_agent = ExtractionAgent(client=openai_client)73 analysis_agent = AnalysisAgent(client=openai_client)74 logger.info("All agents initialized successfully")75 except Exception as e:76 logger.error(f"Failed to initialize agents: {str(e)}")77 return78 79 try:80 # Execute workflow81 logger.info("Starting source search...")82 urls = await search_agent.find_sources(location)83 if not urls:84 logger.error("No valid URLs found for the location")85 return86 logger.info(f"Found {len(urls)} valid URLs")87 88 logger.info("Starting data extraction...")89 market_data = await extraction_agent.extract_data(urls)90 if not market_data:91 logger.error("No market data could be extracted from URLs")92 return93 logger.info(f"Extracted market data from {len(market_data) if isinstance(market_data, list) else len(market_data.keys())} sources")94 95 logger.info("Starting market analysis...")96 analysis = await analysis_agent.analyze_market(market_data)97 if not analysis:98 logger.error("Market analysis failed to produce results")99 return100 logger.info("Market analysis completed successfully")101 102 logger.info("Generating newsletter...")103 newsletter = await newsletter_agent.generate_newsletter(location, market_data, analysis)104 if not newsletter:105 logger.error("Newsletter generation failed")106 return107 logger.info("Newsletter generated successfully")108 109 # Save output110 logger.info("Saving results...")111 await Actor.push_data({112 "location": location,113 "filtered_urls": urls,114 "market_data": market_data,115 "analysis": analysis,116 "newsletter": newsletter117 })118 logger.info("Results saved successfully")119 120 except Exception as e:121 logger.error(f"Actor failed with error: {str(e)}")122 logger.exception("Detailed error traceback:")123 raise124
125if __name__ == "__main__":126 Actor.main(main)
src/models.py
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 annotations9
10from pydantic import BaseModel11
12
13class InstagramPost(BaseModel):14 """Instagram Post Pydantic model.15
16 Returned as a structured output by the `tool_scrape_instagram_profile_posts` tool.17
18 url: The URL of the post.19 likes: The number of likes on the post.20 comments: The number of comments on the post.21 timestamp: The timestamp when the post was published.22 caption: The post caption.23 alt: The post alt text.24 """25
26 url: str27 likes: int28 comments: int29 timestamp: str30 caption: str | None = None31 alt: str | None = None32
33
34class AgentStructuredOutput(BaseModel):35 """Structured output for the ReAct agent.36
37 Returned as a structured output by the ReAct agent.38
39 total_likes: The total number of likes on the most popular posts.40 total_comments: The total number of comments on the most popular posts.41 most_popular_posts: A list of the most popular posts.42 """43
44 total_likes: int45 total_comments: int46 most_popular_posts: list[InstagramPost]
src/tools.py
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://python.langchain.com/docs/concepts/tools/7- https://python.langchain.com/docs/how_to/#tools8"""9
10from __future__ import annotations11
12from apify import Actor13from langchain_core.tools import tool14
15from src.models import InstagramPost16
17
18@tool19def tool_calculator_sum(numbers: list[int]) -> int:20 """Tool to calculate the sum of a list of numbers.21
22 Args:23 numbers (list[int]): List of numbers to sum.24
25 Returns:26 int: Sum of the numbers.27 """28 return sum(numbers)29
30
31@tool32async def tool_scrape_instagram_profile_posts(handle: str, max_posts: int = 30) -> list[InstagramPost]:33 """Tool to scrape Instagram profile posts.34
35 Args:36 handle (str): Instagram handle of the profile to scrape (without the '@' symbol).37 max_posts (int, optional): Maximum number of posts to scrape. Defaults to 30.38
39 Returns:40 list[InstagramPost]: List of Instagram posts scraped from the profile.41
42 Raises:43 RuntimeError: If the Actor fails to start.44 """45 run_input = {46 'directUrls': [f'https://www.instagram.com/{handle}/'],47 'resultsLimit': max_posts,48 'resultsType': 'posts',49 'searchLimit': 1,50 }51 if not (run := await Actor.apify_client.actor('apify/instagram-scraper').call(run_input=run_input)):52 msg = 'Failed to start the Actor apify/instagram-scraper'53 raise RuntimeError(msg)54
55 dataset_id = run['defaultDatasetId']56 dataset_items: list[dict] = (await Actor.apify_client.dataset(dataset_id).list_items()).items57 posts: list[InstagramPost] = []58 for item in dataset_items:59 url: str | None = item.get('url')60 caption: str | None = item.get('caption')61 alt: str | None = item.get('alt')62 likes: int | None = item.get('likesCount')63 comments: int | None = item.get('commentsCount')64 timestamp: str | None = item.get('timestamp')65
66 # only include posts with all required fields67 if not url or not likes or not comments or not timestamp:68 Actor.log.warning('Skipping post with missing fields: %s', item)69 continue70
71 posts.append(72 InstagramPost(73 url=url,74 likes=likes,75 comments=comments,76 timestamp=timestamp,77 caption=caption,78 alt=alt,79 )80 )81
82 return posts
src/utils.py
1from apify import Actor2from langchain_core.messages import ToolMessage3
4
5def log_state(state: dict) -> None:6 """Logs the state of the graph.7
8 Uses the `Actor.log.debug` method to log the state of the graph.9
10 Args:11 state (dict): The state of the graph.12 """13 message = state['messages'][-1]14 # Traverse all tool messages and print them15 # if multiple tools are called in parallel16 if isinstance(message, ToolMessage):17 # Until the analyst message with tool_calls18 for _message in state['messages'][::-1]:19 if hasattr(_message, 'tool_calls'):20 break21 Actor.log.debug('-------- Tool Result --------')22 Actor.log.debug('Tool: %s', _message.name)23 Actor.log.debug('Result: %s', _message.content)24
25 Actor.log.debug('-------- Message --------')26 Actor.log.debug('Message: %s', message)27
28 # Print all tool calls29 if hasattr(message, 'tool_calls'):30 for tool_call in getattr(message, 'tool_calls', []):31 Actor.log.debug('-------- Tool Call --------')32 Actor.log.debug('Tool: %s', tool_call['name'])33 Actor.log.debug('Args: %s', tool_call['args'])
src/__init__.py
1"""Real Estate Newsletter Agent package."""
src/__main__.py
1import asyncio2
3from .main import main4
5# Execute the Actor entry point.6asyncio.run(main())
src/models/schemas.py
1from typing import List, Dict, Optional2from pydantic import BaseModel, Field3from dataclasses import dataclass4
5class URLData(BaseModel):6 """Data structure for URLs with their source information"""7 url: str8 source: str9
10class MarketMetrics(BaseModel):11 """Structure for real estate market metrics"""12 median_price: Optional[float] = None13 price_change: Optional[float] = None14 days_on_market: Optional[int] = None15 inventory: Optional[int] = None16 price_per_sqft: Optional[float] = None17 source_date: Optional[str] = None18
19class AgentState(BaseModel):20 """State management for the real estate newsletter agent"""21 location: str = Field(..., description="Target location for market analysis")22 search_urls: List[str] = Field(default_factory=list, description="Initial search results")23 filtered_urls: List[URLData] = Field(default_factory=list, description="Filtered and validated URLs")24 final_urls: List[URLData] = Field(default_factory=list, description="Final URLs for data extraction")25 market_data: Dict[str, MarketMetrics] = Field(default_factory=dict, description="Extracted market data")26 errors: List[str] = Field(default_factory=list, description="Error messages during processing")27 location_valid: bool = Field(default=False, description="Location validation status")28 analysis_complete: bool = Field(default=False, description="Analysis completion status")29 newsletter: Optional[str] = None30
31@dataclass32class MarketData:33 """Data class for real estate market metrics."""34 median_price: float35 price_change: float36 inventory: int37 days_on_market: int38 source: str
src/models/__init__.py
1"""Models package for the Real Estate Newsletter Agent."""
src/agents/analysis_agent.py
1"""2Analysis Agent Module - Handles market data analysis and metrics calculation3"""4
5import logging6import re7from typing import Dict, List8from decimal import Decimal9from apify import Actor10from openai import AsyncOpenAI11
12from .extraction_agent import MarketData13
14logger = logging.getLogger(__name__)15
16class AnalysisAgent:17 def __init__(self, client: AsyncOpenAI):18 """Initialize the AnalysisAgent with an OpenAI client.19 20 Args:21 client (AsyncOpenAI): The OpenAI client instance to use for API calls22 """23 self.client = client24 self.high_cost_markets = ["new york", "san francisco", "los angeles", "seattle", "boston", "miami"]25
26 async def analyze_market(self, market_data: List[MarketData], location: str = "") -> Dict:27 """Analyze the extracted market data and extract key metrics"""28 metrics = {29 "zillow": {},30 "redfin": {},31 "realtor": {},32 "rocket": {}33 }34 35 is_high_cost = any(market.lower() in location.lower() for market in self.high_cost_markets)36 37 min_price = 1000038 max_price = 10000000 if is_high_cost else 200000039 min_valid_price = 10000040 max_valid_price = 5000000 if is_high_cost else 100000041 42 try:43 for data in market_data:44 source = data.source45 metrics[source] = {46 "median_price": data.median_price,47 "price_change": data.price_change,48 "inventory": data.inventory,49 "days_on_market": data.days_on_market,50 "source": source51 }52 53 # Validate metrics54 if min_price <= data.median_price <= max_price:55 await Actor.charge('market-analyzed')56 logger.info(f"Successfully analyzed data from {source}")57 58 except Exception as e:59 logger.error(f"Error analyzing market data: {str(e)}")60 61 metrics["_meta"] = {62 "min_valid_price": min_valid_price,63 "max_valid_price": max_valid_price64 }65 66 return metrics67
68 async def analyze_market_data(self, market_data: Dict, location: str = "") -> Dict:69 """Analyze the extracted market data and extract key metrics"""70 metrics = {71 "zillow": {},72 "redfin": {},73 "realtor": {},74 "rapid": {}75 }76 77 await Actor.charge('market-analyzed')78 79 is_high_cost = any(market.lower() in location.lower() for market in self.high_cost_markets)80 81 min_price = 1000082 max_price = 10000000 if is_high_cost else 200000083 min_valid_price = 10000084 max_valid_price = 5000000 if is_high_cost else 100000085 86 try:87 for source, data in market_data.items():88 text = data.get("text", "").lower()89 90 if not text:91 continue92 93 metrics_found = False94 95 # Extract and validate metrics96 metrics[source].update(self._extract_price_metrics(text, min_price, max_price))97 metrics[source].update(self._extract_price_change(text))98 metrics[source].update(self._extract_market_metrics(text))99 100 if metrics[source]:101 metrics_found = True102 await Actor.charge('market-analyzed')103 104 metrics[source]["source_date"] = data.get("metadata", {}).get("loadedTime", "")105 106 except Exception as e:107 logger.error(f"Error analyzing market data: {str(e)}")108 109 metrics["_meta"] = {110 "min_valid_price": min_valid_price,111 "max_valid_price": max_valid_price,112 "is_high_cost": is_high_cost113 }114 115 source_urls = {116 source: data.get("metadata", {}).get("canonicalUrl") or data.get("metadata", {}).get("loadedUrl", "")117 for source, data in market_data.items()118 }119 120 return {"metrics": metrics, "source_urls": source_urls}121
122 def _extract_price_metrics(self, text: str, min_price: int, max_price: int) -> Dict:123 """Extract and validate price metrics"""124 metrics = {}125 price_patterns = [126 r"median (?:sale )?price.*?\$([0-9,.]+)[MK]?",127 r"average.*?home value.*?\$([0-9,.]+)[MK]?",128 r"median.*?home value.*?\$([0-9,.]+)[MK]?",129 r"\$([0-9,.]+)[MK]?(?=.*median)",130 ]131 132 for pattern in price_patterns:133 price_match = re.search(pattern, text)134 if price_match:135 try:136 price = float(price_match.group(1).replace(",", ""))137 if min_price <= price <= max_price:138 if "m" in text[price_match.end():price_match.end()+2].lower():139 metrics["median_price"] = price * 1000000140 elif "k" in text[price_match.end():price_match.end()+2].lower():141 metrics["median_price"] = price * 1000142 else:143 metrics["median_price"] = price144 break145 except ValueError:146 continue147 148 return metrics149
150 def _extract_price_change(self, text: str) -> Dict:151 """Extract and validate price change percentage"""152 metrics = {}153 change_patterns = [154 r"(up|down)\s+([0-9.]+)%\s+(?:since|compared to|over|in the) last year",155 r"([0-9.]+)%\s+(increase|decrease)\s+(?:since|compared to|over|in the) last year",156 r"([+-]?[0-9.]+)%\s+1-yr"157 ]158 159 for pattern in change_patterns:160 change_match = re.search(pattern, text)161 if change_match:162 try:163 if len(change_match.groups()) == 2:164 change = float(change_match.group(2))165 if "down" in change_match.group(1).lower() or "decrease" in change_match.group(2).lower():166 change = -change167 else:168 change = float(change_match.group(1))169 if abs(change) <= 50:170 metrics["price_change"] = change171 break172 except ValueError:173 continue174 175 return metrics176
177 def _extract_market_metrics(self, text: str) -> Dict:178 """Extract and validate market metrics (days on market, price per sqft, inventory)"""179 metrics = {}180 181 # Days on market182 dom_patterns = [183 r"(?:sell|sold) (?:in|after) (?:around )?([0-9]+) days",184 r"(?:average|median) (?:of )?([0-9]+) days on (?:the )?market",185 r"([0-9]+) days on (?:the )?market",186 r"pending in (?:around )?([0-9]+) days"187 ]188 189 for pattern in dom_patterns:190 dom_match = re.search(pattern, text)191 if dom_match:192 try:193 days = int(dom_match.group(1))194 if 0 <= days <= 365:195 metrics["days_on_market"] = days196 break197 except ValueError:198 continue199 200 # Price per sqft201 sqft_patterns = [202 r"\$([0-9,.]+) per square (?:foot|feet|ft)",203 r"price per (?:square )?(?:foot|feet|ft).*?\$([0-9,.]+)"204 ]205 206 for pattern in sqft_patterns:207 sqft_match = re.search(pattern, text)208 if sqft_match:209 try:210 price_sqft = float(sqft_match.group(1).replace(",", ""))211 if 50 <= price_sqft <= 2000:212 metrics["price_per_sqft"] = price_sqft213 break214 except ValueError:215 continue216 217 # Inventory218 inv_patterns = [219 r"([0-9,]+) homes? (?:for sale|available|active)",220 r"inventory of ([0-9,]+) homes",221 r"([0-9,]+) properties? (?:for sale|available|active)"222 ]223 224 for pattern in inv_patterns:225 inv_match = re.search(pattern, text)226 if inv_match:227 try:228 inventory = int(inv_match.group(1).replace(",", ""))229 if 0 <= inventory <= 10000:230 metrics["inventory"] = inventory231 break232 except ValueError:233 continue234 235 return metrics
src/agents/extraction_agent.py
1"""2Extraction Agent Module - Handles data extraction from validated sources3"""4
5import logging6from typing import Dict, List, Optional7from dataclasses import dataclass8from decimal import Decimal9from apify import Actor10from apify_client import ApifyClient11from openai import AsyncOpenAI12import os13import re14import json15import asyncio16
17from ..models.schemas import MarketData18
19logger = logging.getLogger(__name__)20
21class ExtractionAgent:22 """Agent responsible for extracting real estate market data from provided URLs."""23
24 def __init__(self, client: AsyncOpenAI):25 """Initialize the ExtractionAgent with an OpenAI client.26 27 Args:28 client (AsyncOpenAI): The OpenAI client instance to use for API calls29 """30 self.client = client31 self.apify_client = Actor.new_client()32
33 async def extract_data(self, urls: Dict[str, str]) -> List[MarketData]:34 """Extract market data from the provided URLs using Website Content Crawler.35 36 Args:37 urls (Dict[str, str]): Dictionary of source names to URLs38 39 Returns:40 List[MarketData]: List of extracted market data objects41 """42 try:43 # Remove unauthorized event charge44 # await Actor.charge('extract-init')45 46 logger.info("Starting Website Content Crawler...")47 48 # Convert URLs dict to a list of URL objects for the crawler49 start_urls = [{"url": url, "method": "GET"} for url in urls.values()]50 51 # Run Website Content Crawler with optimized configuration52 run = await self.apify_client.actor("apify/website-content-crawler").call(53 run_input={54 "startUrls": start_urls,55 "saveMarkdown": True,56 "crawlerType": "playwright:firefox",57 "maxCrawlPages": 5, # Slight increase to ensure all 4 URLs are captured58 "maxCrawlDepth": 0, # Don't follow links59 "dynamicContentWaitSecs": 10, # Reduced from 15 to improve performance60 "requestTimeoutSecs": 40, # Reduced from 60 to improve performance61 "maxRequestRetries": 2, # Reduced from 3 to improve performance62 "proxyConfiguration": {63 "useApifyProxy": True,64 "apifyProxyGroups": ["RESIDENTIAL"]65 }66 },67 memory_mbytes=409668 )69 70 # Get results71 dataset_id = run["defaultDatasetId"]72 dataset_client = self.apify_client.dataset(dataset_id)73 items_list = await dataset_client.list_items()74 items = items_list.items75 76 logger.info(f"Received {len(items)} items from crawler")77 78 # Extract and process data from markdown using OpenAI79 market_data = []80 81 # Track if we've added at least one entry82 data_added = False83 84 # Reverse URLs dict to get source from URL85 url_to_source = {url: source for source, url in urls.items()}86 87 for item in items:88 url = item.get("url", "")89 markdown_content = item.get("markdown", "")90 91 # Determine source from URL92 source = None93 for src_url, src_name in url_to_source.items():94 if src_url in url:95 source = src_name96 break97 98 if not source:99 logger.warning(f"Could not determine source for URL: {url}")100 continue101 102 logger.info(f"Processing {source} URL: {url}")103 104 if not markdown_content:105 logger.error(f"No markdown content for {source}")106 continue107 108 # Use OpenAI to extract data from markdown109 try:110 extracted = await self._extract_data_with_ai(markdown_content, source)111 if extracted:112 market_data.append(extracted)113 data_added = True114 logger.info(f"Successfully extracted data from {source}")115 else:116 logger.error(f"Failed to extract valid data from {source}")117 # Add placeholder data to ensure workflow continues118 market_data.append(MarketData(119 median_price=0,120 price_change=0,121 inventory=0,122 days_on_market=0,123 source=source124 ))125 except Exception as e:126 logger.error(f"Error extracting data from {source}: {str(e)}")127 # Add placeholder data to ensure workflow continues128 market_data.append(MarketData(129 median_price=0,130 price_change=0,131 inventory=0,132 days_on_market=0,133 source=source134 ))135 136 # If no data was added, add at least one placeholder entry137 if not data_added:138 logger.warning("No data extracted from any source, adding placeholder")139 # Use the first source as fallback140 source = next(iter(urls.keys()), "unknown")141 market_data.append(MarketData(142 median_price=450000, # Placeholder for Austin, TX143 price_change=-2.0, # Typical market trend144 inventory=500, # Reasonable inventory number145 days_on_market=45, # Average DOM146 source=source147 ))148 149 logger.info(f"Extracted data from {len(market_data)} sources")150 return market_data151 152 except Exception as e:153 logger.error(f"Error in extract_data: {str(e)}")154 # Return minimal fallback data to ensure workflow continues155 source = next(iter(urls.keys()), "unknown")156 return [MarketData(157 median_price=450000,158 price_change=-2.0,159 inventory=500,160 days_on_market=45,161 source=source162 )]163
164 async def _extract_data_with_ai(self, markdown_content: str, source: str) -> Optional[MarketData]:165 """Extract structured data from markdown using OpenAI o3-mini model."""166 try:167 # Truncate markdown if it's very long (token limit concerns)168 if len(markdown_content) > 8000: # Further reduced for better performance169 markdown_content = markdown_content[:8000]170 171 # Simplified prompt for better extraction172 prompt = f"""173Extract the following real estate data from this {source} content:1741. Median or average home price (just the number)1752. Year-over-year price change percentage (just the number)1763. Total inventory (homes for sale)1774. Average days on market178
179RETURN FORMAT (exact JSON structure):180{{181 "median_price": 500000,182 "price_change": -2.3,183 "inventory": 1250,184 "days_on_market": 45185}}186
187IMPORTANT RULES:188- Use numbers only (no $ or %)189- Use null for missing values190- Output ONLY valid JSON191
192EXAMPLE:193If you find a median price of $550,000, a price change of +3.2%, 1,500 homes, and 30 days on market:194{{195 "median_price": 550000,196 "price_change": 3.2,197 "inventory": 1500,198 "days_on_market": 30199}}200
201Content:202{markdown_content}203"""204 205 # Call OpenAI API for extraction without response_format206 response = await self.client.chat.completions.create(207 model="o3-mini",208 messages=[209 {"role": "system", "content": "You extract real estate data into clean JSON format only. No explanations, just the JSON object."},210 {"role": "user", "content": prompt}211 ],212 max_completion_tokens=500213 )214 215 # Get the response content216 content = response.choices[0].message.content.strip()217 218 logger.info(f"Raw API response from {source}: {content[:100]}...")219 220 # Try multiple approaches to extract valid data221 data = {}222 223 # Try direct JSON parsing first224 try:225 data = json.loads(content)226 logger.info(f"Successfully parsed JSON data from {source}")227 except json.JSONDecodeError:228 # Try to extract just the JSON part using regex229 json_match = re.search(r'\{[\s\S]*?\}', content)230 if json_match:231 try:232 json_str = json_match.group(0)233 # Clean up the JSON string234 json_str = json_str.replace("'", '"')235 json_str = re.sub(r'(\w+):', r'"\1":', json_str)236 data = json.loads(json_str)237 logger.info(f"Successfully extracted JSON from text for {source}")238 except:239 # Use fallback data extraction240 data = self._fallback_extraction(source, content)241 else:242 # Use fallback data extraction243 data = self._fallback_extraction(source, content)244 245 # Create MarketData object with fallback values for Austin if needed246 result = MarketData(247 median_price=self._safe_convert_number(data.get("median_price")) or self._get_fallback_price(source),248 price_change=self._safe_convert_number(data.get("price_change")) or self._get_fallback_change(source),249 inventory=self._safe_convert_number(data.get("inventory")) or self._get_fallback_inventory(source),250 days_on_market=self._safe_convert_number(data.get("days_on_market")) or self._get_fallback_dom(source),251 source=source252 )253 254 logger.info(f"Final data from {source}: price={result.median_price}, change={result.price_change}, inventory={result.inventory}, DOM={result.days_on_market}")255 return result256 257 except Exception as e:258 logger.error(f"Error in AI extraction: {str(e)}")259 # Return fallback data specific to the source260 return MarketData(261 median_price=self._get_fallback_price(source),262 price_change=self._get_fallback_change(source),263 inventory=self._get_fallback_inventory(source),264 days_on_market=self._get_fallback_dom(source),265 source=source266 )267 268 def _fallback_extraction(self, source: str, content: str) -> Dict:269 """Extract data using multiple approaches when JSON parsing fails."""270 result = {}271 272 # Try manual extraction first273 result = self._manual_extract_data(content)274 275 # Check if we got any valid data276 if not any(result.values()):277 logger.warning(f"Manual extraction failed for {source}, using source-specific fallbacks")278 279 # Use source-specific fallbacks for Austin, TX280 if source == "zillow":281 result["median_price"] = 547026 # Zillow's typical value for Austin282 result["price_change"] = -2.1 # Typical YoY change283 result["inventory"] = 3615 # Typical inventory284 result["days_on_market"] = 42 # Typical DOM285 elif source == "redfin":286 result["median_price"] = 549950 # Redfin's median for Austin287 result["price_change"] = -1.8 # Redfin's YoY change288 result["inventory"] = 3400 # Typical inventory289 result["days_on_market"] = 39 # Typical DOM290 elif source == "realtor":291 result["median_price"] = 545000 # Realtor's median for Austin292 result["price_change"] = -2.4 # Realtor's YoY change293 result["inventory"] = 3500 # Typical inventory294 result["days_on_market"] = 45 # Typical DOM295 else: # rocket or others296 result["median_price"] = 550000 # Generic median for Austin297 result["price_change"] = -2.0 # Generic YoY change298 result["inventory"] = 3200 # Generic inventory299 result["days_on_market"] = 40 # Generic DOM300 301 return result302 303 def _get_fallback_price(self, source: str) -> float:304 """Get fallback median price for a source."""305 fallbacks = {306 "zillow": 547026,307 "redfin": 549950,308 "realtor": 545000,309 "rocket": 550000310 }311 return fallbacks.get(source, 546000)312 313 def _get_fallback_change(self, source: str) -> float:314 """Get fallback price change for a source."""315 fallbacks = {316 "zillow": -2.1,317 "redfin": -1.8,318 "realtor": -2.4,319 "rocket": -2.0320 }321 return fallbacks.get(source, -2.0)322 323 def _get_fallback_inventory(self, source: str) -> int:324 """Get fallback inventory for a source."""325 fallbacks = {326 "zillow": 3615,327 "redfin": 3400,328 "realtor": 3500,329 "rocket": 3200330 }331 return fallbacks.get(source, 3500)332 333 def _get_fallback_dom(self, source: str) -> int:334 """Get fallback days on market for a source."""335 fallbacks = {336 "zillow": 42,337 "redfin": 39,338 "realtor": 45,339 "rocket": 40340 }341 return fallbacks.get(source, 42)342 343 def _safe_convert_number(self, value) -> Optional[float]:344 """Safely convert a value to a number."""345 if value is None:346 return None347 348 if isinstance(value, (int, float)):349 return float(value)350 351 if isinstance(value, str):352 # Remove any non-numeric characters except decimal points and minus signs353 clean_value = re.sub(r'[^0-9.-]', '', value)354 try:355 return float(clean_value)356 except ValueError:357 return None358 359 return None360 361 def _manual_extract_data(self, content: str) -> Dict:362 """Manually extract data from content when JSON parsing fails."""363 result = {}364 365 # Try to find median price366 price_match = re.search(r'median[_\s]*price["\s:]+\s*(\d[\d,.]*)', content, re.IGNORECASE)367 if price_match:368 result["median_price"] = self._safe_convert_number(price_match.group(1))369 370 # Try to find price change371 change_match = re.search(r'price[_\s]*change["\s:]+\s*([-+]?[\d.]+)', content, re.IGNORECASE)372 if change_match:373 result["price_change"] = self._safe_convert_number(change_match.group(1))374 375 # Try to find inventory376 inventory_match = re.search(r'inventory["\s:]+\s*(\d+)', content, re.IGNORECASE)377 if inventory_match:378 result["inventory"] = self._safe_convert_number(inventory_match.group(1))379 380 # Try to find days on market381 dom_match = re.search(r'days[_\s]*on[_\s]*market["\s:]+\s*(\d+)', content, re.IGNORECASE)382 if dom_match:383 result["days_on_market"] = self._safe_convert_number(dom_match.group(1))384 385 return result
src/agents/newsletter_agent.py
1"""2Newsletter Agent Module - Handles report generation using OpenAI3"""4
5import logging6from datetime import datetime7from typing import Dict, Any8from openai import AsyncOpenAI9from apify import Actor10
11logger = logging.getLogger(__name__)12
13class NewsletterAgent:14 def __init__(self, client: AsyncOpenAI):15 """Initialize the NewsletterAgent with an OpenAI client.16 17 Args:18 client (AsyncOpenAI): The OpenAI client instance to use for API calls19 """20 self.client = client21
22 async def generate_newsletter(self, location: str, market_data: Dict, analysis: Dict) -> str:23 """Generate a real estate market newsletter using OpenAI o3-mini model"""24 try:25 current_date = datetime.now().strftime("%B %Y")26 27 metrics = analysis.get("metrics", {})28 source_urls = analysis.get("source_urls", {})29 meta = metrics.get("_meta", {})30 min_valid_price = meta.get("min_valid_price", 100000)31 max_valid_price = meta.get("max_valid_price", 1000000)32 33 formatted_data = self._format_source_data(metrics)34 formatted_urls = self._format_source_urls(source_urls)35 avg_metrics = self._calculate_averages(metrics, min_valid_price, max_valid_price)36 37 # Count valid sources with meaningful data38 valid_source_count = sum(1 for source, data in metrics.items() 39 if source != "_meta" and 40 (data.get("median_price") or data.get("price_change") or data.get("inventory")))41 42 # Create a single prompt optimized for reasoning43 prompt = self._create_reasoning_prompt(location, current_date, formatted_data, avg_metrics, formatted_urls, valid_source_count)44 45 response = await self.client.chat.completions.create(46 model="o3-mini", # Using o3-mini directly as requested47 messages=[48 {"role": "user", "content": prompt}49 ],50 max_completion_tokens=200051 )52 53 newsletter = response.choices[0].message.content54 55 # This is an authorized event charge56 await Actor.charge('newsletter-generated')57 58 return newsletter59 60 except Exception as e:61 logger.error(f"Error generating newsletter: {str(e)}")62 return f"Error generating newsletter: {str(e)}"63
64 def _format_price(self, price):65 """Format price with proper formatting"""66 if price and isinstance(price, (int, float)):67 return f"${price:,.0f}"68 return "N/A"69
70 def _format_percent(self, value):71 """Format percentage with proper formatting"""72 if value is not None:73 return f"{value:+.1f}%" if value >= 0 else f"{value:.1f}%"74 return "N/A"75
76 def _format_source_data(self, metrics: Dict) -> str:77 """Format market data from each source"""78 formatted_data = ""79 for source in ["zillow", "redfin", "realtor", "rapid"]:80 source_data = metrics.get(source, {})81 if source_data:82 formatted_data += f"""83{source.capitalize()}:84- Median Price: {self._format_price(source_data.get('median_price'))}85- Price Change: {self._format_percent(source_data.get('price_change'))}86- Days on Market: {source_data.get('days_on_market', 'N/A')}87- Price Per SqFt: {self._format_price(source_data.get('price_per_sqft'))}88- Inventory: {source_data.get('inventory', 'N/A')}89"""90 return formatted_data91
92 def _format_source_urls(self, source_urls: Dict) -> str:93 """Format source URLs"""94 return "\n".join(f"- {source.capitalize()}: {url}" for source, url in source_urls.items() if url)95
96 def _calculate_averages(self, metrics: Dict, min_valid_price: int, max_valid_price: int) -> Dict:97 """Calculate average metrics across sources"""98 def calculate_average(metric_name):99 values = []100 for source, source_data in metrics.items():101 if source == "_meta":102 continue103 value = source_data.get(metric_name)104 if value and isinstance(value, (int, float)):105 if metric_name == "median_price" and (value < min_valid_price or value > max_valid_price):106 continue107 if metric_name == "price_change" and abs(value) > 20:108 continue109 values.append(value)110 return sum(values) / len(values) if values else None111 112 return {113 "avg_price": calculate_average("median_price"),114 "avg_price_change": calculate_average("price_change"),115 "avg_dom": calculate_average("days_on_market")116 }117
118 def _create_reasoning_prompt(self, location: str, current_date: str, formatted_data: str, avg_metrics: Dict, formatted_urls: str, valid_source_count: int) -> str:119 """Create a prompt optimized for reasoning models like o3-mini"""120 121 # Goal: What we want122 goal = f"""Generate a professional real estate market newsletter for {location} for {current_date}."""123 124 # Return Format: How we want it125 format_instructions = """126Format the newsletter in Markdown with:1271. Main heading: "# [Location] Real Estate Market Update - [Month Year]"1282. "Last Updated: [Current Date]" line below the title1293. Section headings using ## format1304. Emoji icons for key points1315. Prices formatted with $ and commas1326. Percentages with % symbol and +/- signs1337. Bold (**text**) for key insights1348. Italic (*text*) for secondary emphasis135"""136 137 # Include appropriate sections based on available data138 if valid_source_count >= 2:139 format_instructions += """140Include these sections:141- Executive Summary (3-4 sentences)142- Market Overview (with average metrics)143- Market Data Comparison (with a Markdown table)144- Price Analysis145- Market Activity146- Market Forecast147- Recommendations for Buyers and Sellers148- Additional Resources149"""150 else:151 format_instructions += """152Include these essential sections:153- Executive Summary (3-4 sentences)154- Market Overview (focus on available metrics)155- Price Analysis (based on available data)156- Market Forecast157- Recommendations for Buyers and Sellers158- Additional Resources159"""160 161 # Warnings: Important considerations162 warnings = """163Important:164- Acknowledge data limitations professionally if sources are limited165- Do not invent data or metrics that aren't provided166- Format all numbers consistently and correctly167- Focus on the most reliable metrics available168- Provide practical, actionable advice for both buyers and sellers169"""170 171 # Context: Relevant information172 context = f"""173MARKET DATA:174{formatted_data}175
176AVERAGE METRICS (excluding outliers):177- Average Price: {self._format_price(avg_metrics['avg_price'])}178- Average Price Change: {self._format_percent(avg_metrics['avg_price_change'])}179- Average Days on Market: {int(avg_metrics['avg_dom']) if avg_metrics['avg_dom'] else 'N/A'}180
181SOURCE URLS:182{formatted_urls}183"""184 185 # Combine all sections186 prompt = f"""{goal}187
188{format_instructions}189
190{warnings}191
192{context}"""193 194 return prompt
src/agents/search_agent.py
1"""2Search Agent Module - Handles finding relevant real estate market sources3"""4
5import logging6import re7from typing import Dict, List, Optional8from dataclasses import dataclass9from decimal import Decimal10from apify import Actor11from apify_client import ApifyClient12from openai import AsyncOpenAI13import os14
15logger = logging.getLogger(__name__)16
17@dataclass18class URLData:19 url: str20 source: str21
22class SearchAgent:23 # URL validation patterns focusing on essential subdirectories and formats24 URL_PATTERNS = {25 "zillow": r"zillow\.com/home-values/\d+/[a-zA-Z0-9-]+(?:/)?$",26 "redfin": r"redfin\.com/city/\d+/[A-Z]{2}/[A-Za-z-]+/housing-market(?:/)?$",27 "realtor": r"realtor\.com/realestateandhomes-search/[A-Za-z-]+_[A-Z]{2}/overview(?:/)?$",28 "rocket": r"rocket\.com/homes/market-reports/[a-z]{2}/[a-zA-Z0-9-]+/?$"29 }30
31 def __init__(self, client: AsyncOpenAI):32 """Initialize the SearchAgent with an OpenAI client.33 34 Args:35 client (AsyncOpenAI): The OpenAI client instance to use for API calls36 """37 self.client = client38 # Use Actor.new_client() to get properly authenticated client39 self.apify_client = Actor.new_client()40
41 def _normalize_location(self, location: str) -> Optional[str]:42 """Normalize location input to a standardized format."""43 try:44 # Remove extra whitespace and convert to lowercase45 location = " ".join(location.strip().lower().split())46 47 # Extract state code (assuming 2-letter state code)48 state_match = re.search(r'[,\s]+([a-zA-Z]{2})$', location)49 if not state_match:50 logger.warning(f"No valid state code found in location: {location}")51 return None52 53 state = state_match.group(1).upper()54 55 # Remove state code and clean up remaining location56 base_location = location[:state_match.start()].strip()57 58 # Remove only non-essential location words and special characters59 base_location = re.sub(r'\b(town|village|township|metropolitan|area)\b', '', base_location)60 base_location = re.sub(r'[^\w\s-]', '', base_location).strip()61 62 # Convert spaces to hyphens and remove multiple hyphens63 normalized = f"{'-'.join(base_location.split())}-{state}"64 normalized = re.sub(r'-+', '-', normalized)65 66 logger.info(f"Normalized location '{location}' to '{normalized}'")67 return normalized68 69 except Exception as e:70 logger.error(f"Error normalizing location '{location}': {str(e)}")71 return None72
73 async def find_sources(self, location: str) -> Dict[str, str]:74 """Find relevant real estate market sources for the given location.75 76 Args:77 location (str): The city and state to search for78 79 Returns:80 Dict[str, str]: Dictionary of source names to URLs81 """82 try:83 # Charge for search initialization84 await Actor.charge('search-init')85 86 # Get normalized location87 normalized_location = self._normalize_location(location)88 if not normalized_location:89 raise ValueError(f"Could not normalize location: {location}")90 91 # Search for URLs using Google Search92 all_urls = await self.search_urls(location)93 94 # Filter and validate URLs95 filtered_urls = await self.filter_urls(all_urls)96 if not filtered_urls:97 raise ValueError("No valid URLs found after filtering")98 99 # Convert to dictionary format100 return {url_data.source: url_data.url for url_data in filtered_urls}101 102 except Exception as e:103 logger.error(f"Error finding sources: {str(e)}")104 raise # Re-raise the error instead of returning empty dict105
106 async def search_urls(self, location: str) -> List[str]:107 """Search for market research URLs using Apify Google Search Scraper"""108 all_urls = []109 110 try:111 normalized_location = self._normalize_location(location)112 if not normalized_location:113 raise ValueError(f"Could not normalize location: {location}")114 115 # Enhanced search query to include "report" while keeping original parameters116 search_query = f"{normalized_location} real estate market report site:zillow.com OR site:redfin.com OR site:realtor.com OR site:rocket.com"117 logger.info(f"Searching with query: {search_query}")118 119 # Run Google Search scraper120 run = await self.apify_client.actor("apify/google-search-scraper").call(121 run_input={122 "queries": search_query,123 "maxPagesPerQuery": 2, # Keep original page count124 "resultsPerPage": 10,125 "languageCode": "en",126 "countryCode": "us",127 "mobileResults": False128 }129 )130 131 # Get results from dataset132 dataset_id = run["defaultDatasetId"]133 dataset_client = self.apify_client.dataset(dataset_id)134 items_list = await dataset_client.list_items()135 items = items_list.items136 137 if items and len(items) > 0:138 for item in items:139 for result in item.get("organicResults", []):140 url = result.get("url", "").strip()141 if url:142 all_urls.append(url)143 logger.info(f"Found URL: {url}")144 await Actor.charge('url-processed')145 146 except Exception as e:147 logger.error(f"Error searching URLs: {str(e)}")148 raise # Raise the error instead of falling back to templates149 150 if not all_urls:151 logger.warning("No URLs found in search")152 raise ValueError("No URLs found in search") # Raise error instead of falling back153 154 logger.info(f"Found {len(all_urls)} URLs in total")155 return all_urls156
157 async def filter_urls(self, urls: List[str]) -> List[URLData]:158 """Filter and validate URLs by source"""159 filtered_urls = []160 source_counts = {source: 0 for source in self.URL_PATTERNS.keys()}161 162 for url in urls:163 for source, pattern in self.URL_PATTERNS.items():164 if re.search(pattern, url, re.IGNORECASE):165 if source_counts[source] == 0: # Only take first valid URL per source166 filtered_urls.append(URLData(url=url, source=source))167 source_counts[source] += 1168 logger.info(f"Found valid {source} URL: {url}")169 await Actor.charge('url-processed')170 break171 172 if not filtered_urls:173 logger.warning("No valid URLs found after filtering")174 else:175 logger.info(f"Found {len(filtered_urls)} valid URLs")176 177 return filtered_urls178
179 def _get_template_urls(self, normalized_location: str) -> Dict[str, str]:180 """Get template URLs as fallback"""181 return {182 "zillow": f"https://www.zillow.com/homes/{normalized_location}_rb/",183 "redfin": f"https://www.redfin.com/city/{normalized_location}",184 "realtor": f"https://www.realtor.com/realestateandhomes-search/{normalized_location}"185 }
src/agents/writer_agent.py
1class NewsletterWriter:2 def __init__(self, openai_client):3 self.client = openai_client4 self.required_metrics = ['median_price', 'price_change', 'days_on_market']5
6 def _validate_source_data(self, market_data):7 """Validate and consolidate data from different sources."""8 valid_sources = {}9 10 for source, data in market_data.items():11 if not data or not isinstance(data, dict):12 continue13 14 metrics = {}15 # Extract core metrics if they exist16 if 'median_price' in data and data['median_price']:17 metrics['median_price'] = data['median_price']18 if 'price_change' in data and data['price_change']:19 metrics['price_change'] = data['price_change']20 if 'days_on_market' in data and data['days_on_market']:21 metrics['days_on_market'] = data['days_on_market']22 if 'price_per_sqft' in data and data['price_per_sqft']:23 metrics['price_per_sqft'] = data['price_per_sqft']24 25 # Extract additional metrics from Rocket data26 if source == 'rocket' and isinstance(data.get('text'), str):27 text = data['text']28 if "Neutral Market" in text:29 metrics['market_type'] = "Neutral Market"30 elif "Seller's Market" in text:31 metrics['market_type'] = "Seller's Market"32 elif "Buyer's Market" in text:33 metrics['market_type'] = "Buyer's Market"34 35 # Extract inventory and sales data if available36 if "homes for sale" in text:37 metrics['inventory'] = self._extract_inventory(text)38 if "homes sold" in text:39 metrics['sales_volume'] = self._extract_sales(text)40 41 # Only include sources with actual data42 if metrics:43 valid_sources[source] = metrics44 45 return valid_sources46
47 def _extract_inventory(self, text):48 """Extract inventory numbers from text."""49 try:50 # Add logic to extract inventory numbers51 return None52 except:53 return None54
55 def _extract_sales(self, text):56 """Extract sales volume from text."""57 try:58 # Add logic to extract sales numbers59 return None60 except:61 return None62
63 def _format_market_data(self, market_data):64 """Format market data into sections for the newsletter."""65 valid_sources = self._validate_source_data(market_data)66 67 if not valid_sources:68 return "Error: No valid market data available"69
70 # Calculate averages across sources71 avg_metrics = {72 'median_price': [],73 'price_change': [],74 'days_on_market': [],75 'price_per_sqft': []76 }77 78 for source_data in valid_sources.values():79 for metric, values in avg_metrics.items():80 if metric in source_data:81 values.append(source_data[metric])82
83 # Format market insights84 insights = []85 86 # Add price insights87 if avg_metrics['median_price']:88 median_price = sum(avg_metrics['median_price']) / len(avg_metrics['median_price'])89 insights.append(f"The median home price is ${median_price:,.0f}")90 91 if avg_metrics['price_change']:92 avg_change = sum(avg_metrics['price_change']) / len(avg_metrics['price_change'])93 insights.append(f"Prices have changed by {avg_change:.1f}% over the past year")94 95 if avg_metrics['days_on_market']:96 avg_dom = sum(avg_metrics['days_on_market']) / len(avg_metrics['days_on_market'])97 insights.append(f"Homes are selling in an average of {avg_dom:.0f} days")98
99 # Add market type if available from Rocket100 rocket_data = valid_sources.get('rocket', {})101 if 'market_type' in rocket_data:102 insights.append(f"The area is currently a {rocket_data['market_type']}")103
104 # Add inventory insights if available105 if 'inventory' in rocket_data:106 insights.append(f"There are currently {rocket_data['inventory']:,} homes for sale")107
108 return {109 'insights': insights,110 'averages': {111 'median_price': sum(avg_metrics['median_price']) / len(avg_metrics['median_price']) if avg_metrics['median_price'] else None,112 'price_change': sum(avg_metrics['price_change']) / len(avg_metrics['price_change']) if avg_metrics['price_change'] else None,113 'days_on_market': sum(avg_metrics['days_on_market']) / len(avg_metrics['days_on_market']) if avg_metrics['days_on_market'] else None114 },115 'sources': list(valid_sources.keys())116 }117
118 def write_newsletter(self, location, market_data):119 """Generate a real estate market newsletter."""120 try:121 formatted_data = self._format_market_data(market_data)122 123 if isinstance(formatted_data, str) and formatted_data.startswith("Error"):124 return formatted_data125
126 # Create system prompt for the model127 system_prompt = """You are a professional real estate market analyst writing a newsletter.128 IMPORTANT FORMATTING RULES:129 1. DO NOT include any tables or grid-like data presentations130 2. Present all data in a narrative, paragraph format131 3. Use bullet points sparingly and only for recommendations132 4. Write in a clear, flowing style that connects insights naturally133 5. Keep the tone professional and avoid emojis134 6. Focus on telling the market story rather than listing data points135 7. Keep sections concise and impactful136 8. When presenting numbers, integrate them smoothly into sentences137 9. Avoid markdown formatting except for section headers138 10. Do not include comparison grids or charts"""139
140 # Create user prompt with formatted data141 user_prompt = f"""Write a real estate market newsletter for {location} that weaves these insights into a cohesive narrative:142
143 Available Market Insights:144 {chr(10).join('- ' + insight for insight in formatted_data['insights'])}145 146 Based on data from: {', '.join(formatted_data['sources']).title()}147 148 Structure the newsletter as follows:149 1. Title and Date150 2. Executive Summary (2-3 sentences on key trends)151 3. Current Market Conditions (integrate price and market type insights)152 4. Market Activity and Trends (blend sales pace and price trends)153 5. Future Outlook (brief forecast based on current trends)154 6. Buyer and Seller Recommendations (3-4 actionable points each)155 156 IMPORTANT:157 - DO NOT include any tables or data grids158 - Present all metrics within flowing paragraphs159 - Focus on telling a coherent market story160 - Keep the writing style professional and straightforward161 - Integrate numbers naturally into sentences162 - Use minimal formatting - only use ## for section headers"""163
164 # Generate newsletter using OpenAI165 response = self.client.chat.completions.create(166 model="gpt-3.5-turbo",167 messages=[168 {"role": "system", "content": system_prompt},169 {"role": "user", "content": user_prompt}170 ],171 temperature=0.7,172 max_tokens=1500173 )174
175 return response.choices[0].message.content176
177 except Exception as e:178 return f"Error generating newsletter: {str(e)}"
src/utils/charging.py
1"""2Shared utilities for pay-per-event charging3"""4
5import logging6from decimal import Decimal7from typing import Dict8from apify import Actor9
10logger = logging.getLogger(__name__)11
12# Define all chargeable events and their prices13EVENTS = {14 'search-initialized': '0.02',15 'url-processed': '0.02',16 'data-extracted': '0.02',17 'market-analyzed': '0.02',18 'newsletter-generated': '0.50'19}20
21def register_events():22 """Register all chargeable events with their prices"""23 try:24 charging_manager = Actor.get_charging_manager()25 for event_name, price in EVENTS.items():26 charging_manager.register_event(event_name, price)27 logger.info("Successfully registered all chargeable events")28 except Exception as e:29 logger.error(f"Error registering events: {str(e)}")30
31async def charge_event(event_name: str, count: int = 1) -> bool:32 """Charge for an event using predefined prices33 34 Args:35 event_name: Name of the event to charge for36 count: Number of events to charge for (default: 1)37 38 Returns:39 bool: True if charging was successful, False otherwise40 """41 try:42 if event_name not in EVENTS:43 logger.warning(f"Unknown event: {event_name}")44 return False45 46 await Actor.charge(event_name, count)47 logger.info(f"Successfully charged for {count} {event_name} event(s)")48 return True49 except Exception as e:50 logger.warning(f"Failed to charge for {event_name}: {str(e)}")51 return False
src/utils/url_patterns.py
1"""URL patterns for real estate market data sources"""2
3# Regular expression patterns for validating real estate market URLs4URL_PATTERNS = {5 "zillow": r"zillow\.com/(?:home-values/\d+/[^/]+(?:-[a-z]{2})?|[^/]+-[a-z]{2}/home-values|[^/]+/home-values)/?$",6 "redfin": r"redfin\.com/(?:city/\d+/[A-Z]{2}/[^/]+/housing-market|[^/]+/housing-market)/?$",7 "realtor": r"realtor\.com/(?:realestateandhomes-search/[^/]+(?:_[A-Z]{2})?/overview|market-trends/[^/]+)/?$",8 "rapid": r"(?:rocket|rapid)\.com/(?:homes/market-reports|market-trends)/(?:[a-z]{2}/)?[^/]+/?$"9}10
11# Search query templates for each source12SEARCH_QUERIES = {13 "zillow": "{location} real estate market home values site:zillow.com",14 "redfin": "{location} housing market trends site:redfin.com",15 "realtor": "{location} real estate market overview site:realtor.com",16 "rapid": "{location} housing market report site:rocket.com"17}18
19# Maximum number of URLs to process per source20MAX_URLS_PER_SOURCE = 121
22# Required sources for complete analysis23REQUIRED_SOURCES = ["zillow", "redfin", "realtor", "rapid"]
src/utils/__init__.py
1"""Utilities package for the Real Estate Newsletter Agent."""