@ -1,49 +1,90 @@
from typing import List from swarms import Agent , SequentialWorkflow
import http . client
import json
from swarms . structs . agent import Agent def get_properties ( postal_code : str , min_price : int , max_price : int , limit : int = 1 ) - > str :
from swarms . structs . conversation import Conversation """
from swarms . utils . history_output_formatter import history_output_formatter Fetches real estate properties from Realty - in - US API using given zipcode , min price , and max price .
All other payload fields remain constant .
# --- Exa Search Tool Integration --- Returns the property ' s data as a string (JSON-encoded).
# Import and define exa_search as a callable tool for property research. """
from swarms_tools import exa_search
# System prompts for each agent
LEAD_GENERATION_PROMPT = """ payload_dict = {
You are the Lead Generation Agent for Real Estate . " limit " : limit ,
" offset " : 0 ,
" postal_code " : postal_code ,
" status " : [ " for_sale " , " ready_to_build " ] ,
" sort " : { " direction " : " desc " , " field " : " list_date " } ,
" price_min " : min_price ,
" price_max " : max_price
}
payload = json . dumps ( payload_dict )
conn = http . client . HTTPSConnection ( " realty-in-us.p.rapidapi.com " )
headers = {
" x-rapidapi-key " : " 35ae958601msh5c0eae51c54f989p1463c4jsn098ec5be18b8 " ,
" x-rapidapi-host " : " realty-in-us.p.rapidapi.com " ,
" Content-Type " : " application/json "
}
conn . request ( " POST " , " /properties/v3/list " , payload , headers )
res = conn . getresponse ( )
data = res . read ( )
decoded = data . decode ( " utf-8 " )
try :
result_dict = json . loads ( decoded )
except Exception :
return decoded
props_data = (
result_dict . get ( " data " , { } )
. get ( " home_search " , { } )
. get ( " results " , [ ] )
)
if not props_data :
return json . dumps ( { " error " : " No properties found for that query. " } )
return json . dumps ( props_data [ : limit ] )
ROLE :
Collect potential leads for real estate transactions by identifying buyers , sellers , and investors through various channels .
REQUIREMENTS_ANALYZER_PROMPT = """
You are the Requirements Analyzer Agent for Real Estate .
ROLE :
Extract and clarify requirements from user input to create optimized property search queries .
RESPONSIBILITIES : RESPONSIBILITIES :
- Identify potential clients through : - Engage with the user to understand :
* Real estate websites
* Desired property types and featur es
* Social media platforms
* Required amenities and specification s
* Referral networks
* Preferred locations ( city / area / zip )
* Local community events
* Price/ budget range
- Conduct initial consultations to understand : * Timeline and purchase situation
* Client ' s property requirements
* Additional constraints or prioritie s
* Budget constraints
- Analyze user responses to identify :
* Preferred locations
* Key search terms and must - have feature s
* Investment goals
* Priority factors in selection
- Qualify leads by assessing : * Deal - breakers or constraints
* Financial readiness
* Missing or unclear information to be clarified
* Specific property needs
- Generate search strategies :
* Urgency of transaction
* Formulate 3 - 5 targeted search queries based on user requirements
OUTPUT FORMAT : OUTPUT FORMAT :
Provide a comprehensive lead report that includes : Provide a comprehensive requirements analysis :
1. Client profile and contact information 1. User Profile Summary :
2. Detailed requirements and preferences - Property types / requirements of interest
3. Initial assessment of client ' s real estate goals - Key features and specifications
4. Qualification status - Location and budget preferences
5. Recommended next steps - Priority factors
2. Search Strategy :
IMPORTANT CONTEXT SHARING : - 3 - 5 optimized search queries ( plain language , suitable for next agent ' s use)
When preparing the lead report , clearly summarize and include all answers and information provided by the user . Integrate these user responses directly into your analysis and the lead report . This ensures that when your report is sent to the next agent , it contains all relevant user preferences , requirements , and context needed for further research and decision - making . - Rationale for each query
- Expected property / result types
REMEMBER : 3. Clarifications Needed :
- Ensure the user ' s answers are explicitly included in your report so the next agent can use them for property research and analysis. - Questions to refine search
- Any missing info
IMPORTANT :
- INCLUDE all user responses verbatim in your analysis .
- Format queries clearly for the next agent .
- Ask follow - up questions if requirements are unclear .
""" """
PROPERTY_RESEARCH_PROMPT = """ PROPERTY_RESEARCH_PROMPT = """
@ -52,40 +93,25 @@ ROLE:
Conduct in - depth research on properties that match client criteria and market trends . Conduct in - depth research on properties that match client criteria and market trends .
TOOLS : TOOLS :
You have access to the exa_search tool . Use exa_search to find up - to - date and relevant information about properties , market trends , and neighborhood data . Leverage the answers provided by the user and the outputs from previous agents to formulate your search queries . Always use exa_search to supplement your research and validate your findings . You have access to get_properties . Use get_properties to find up - to - date and relevant information about properties for sale . Use ALL search queries produced by the previous agent ( REQUIREMENTS_ANALYZER ) as arguments to get_properties .
RESPONSIBILITIES : RESPONSIBILITIES :
- Perform comprehensive property market analysis using exa_search : - Perform property research using get_properties :
* Local market trends
* Seek properties by each proposed query and shortlist promising results .
* Property value assessments
* Analyze each result by price , location , features , and comparables .
* Investment potential
* Highlight market trends if apparent from results .
* Neighborhood evaluations
* Assess investment or suitability potential .
- Research properties matching client specifications ( using both user answers and previous agent outputs ) : - Structure and cite property search findings .
* Price range
* Location preferences
* Property type
* Specific amenities
- Compile detailed property reports including :
* Comparative market analysis ( use exa_search for recent comps )
* Property history
* Potential appreciation
* Neighborhood insights ( gathered via exa_search )
INSTRUCTIONS :
- Always use exa_search to find the most current and relevant information for your analysis .
- Formulate your exa_search queries based on the user ' s answers and the outputs from previous agents.
- Clearly indicate in your report where exa_search was used to obtain information .
OUTPUT FORMAT : OUTPUT FORMAT :
Provide a structured property research report : Provide a structured property research report :
1. Shortlist of matching properties ( include sources from exa_search ) 1. Shortlist of matching properties ( show summaries of each from get_properties results )
2. Detailed property analysis for each option ( cite exa_search findings ) 2. Detailed property analysis for each option
3. Market trend insights ( supported by exa_search data ) 3. Insights on price , area , trends
4. Investment potential assessment 4. Investment or suitability assessment
5. Recommendations for client consideration 5. Recommendations for client
INSTRUCTIONS :
REMEMBER : - Always use get_properties for up - to - date listing info ; do not fabricate .
Do not rely solely on prior knowledge . Always use exa_search to verify and enhance your research with the latest available data . - Clearly indicate which properties are found from which query .
""" """
MARKETING_PROMPT = """ MARKETING_PROMPT = """
@ -168,236 +194,69 @@ Provide a comprehensive property maintenance report:
5. Contractor and service provider details 5. Contractor and service provider details
""" """
class RealEstateSwarm :
def __init__ (
self ,
name : str = " Real Estate Swarm " ,
description : str = " A comprehensive AI-driven real estate transaction workflow " ,
max_loops : int = 1 ,
user_name : str = " Real Estate Manager " ,
property_type : str = " Residential " ,
output_type : str = " json " ,
user_lead_info : str = " " ,
) :
self . max_loops = max_loops
self . name = name
self . description = description
self . user_name = user_name
self . property_type = property_type
self . output_type = output_type
self . user_lead_info = user_lead_info
self . agents = self . _initialize_agents ( )
self . conversation = Conversation ( )
self . handle_initial_processing ( )
self . exa_search_results = [ ] # Store exa_search results for property research
def handle_initial_processing ( self ) :
self . conversation . add (
role = self . user_name ,
content = f " Company: { self . name } \n "
f " Description: { self . description } \n "
f " Property Type: { self . property_type } "
)
def _initialize_agents ( self ) - > List [ Agent ] :
return [
Agent (
agent_name = " Alex-Lead-Generation " ,
agent_description = " Identifies and qualifies potential real estate clients across various channels. " ,
system_prompt = LEAD_GENERATION_PROMPT ,
max_loops = self . max_loops ,
dynamic_temperature_enabled = True ,
output_type = " final " ,
) ,
Agent (
agent_name = " Emma-Property-Research " ,
agent_description = " Conducts comprehensive property research and market analysis. " ,
system_prompt = PROPERTY_RESEARCH_PROMPT ,
max_loops = self . max_loops ,
dynamic_temperature_enabled = True ,
output_type = " final " ,
) ,
Agent (
agent_name = " Jack-Marketing " ,
agent_description = " Develops and executes marketing strategies for properties. " ,
system_prompt = MARKETING_PROMPT ,
max_loops = self . max_loops ,
dynamic_temperature_enabled = True ,
output_type = " final " ,
) ,
Agent (
agent_name = " Sophia-Transaction-Management " ,
agent_description = " Manages legal and financial aspects of real estate transactions. " ,
system_prompt = TRANSACTION_MANAGEMENT_PROMPT ,
max_loops = self . max_loops ,
dynamic_temperature_enabled = True ,
output_type = " final " ,
) ,
Agent (
agent_name = " Michael-Property-Maintenance " ,
agent_description = " Oversees property condition, maintenance, and market preparation. " ,
system_prompt = PROPERTY_MAINTENANCE_PROMPT ,
max_loops = self . max_loops ,
dynamic_temperature_enabled = True ,
output_type = " final " ,
) ,
]
def find_agent_by_name ( self , name : str ) - > Agent :
for agent in self . agents :
if name in agent . agent_name :
return agent
return None
def lead_generation ( self ) :
alex_agent = self . find_agent_by_name ( " Lead-Generation " )
# Directly inject the user_lead_info into the prompt for the first agent
alex_output = alex_agent . run (
f " User Lead Information: \n { self . user_lead_info } \n \n "
f " History: { self . conversation . get_str ( ) } \n "
f " Generate leads for { self . property_type } real estate transactions. Identify potential clients and their specific requirements. "
)
self . conversation . add (
role = " Lead-Generation " , content = alex_output
)
# --- After lead generation, use user_lead_info as queries for exa_search ---
queries = [ ]
if isinstance ( self . user_lead_info , list ) :
queries = [ str ( q ) for q in self . user_lead_info if str ( q ) . strip ( ) ]
elif isinstance ( self . user_lead_info , str ) :
if " \n " in self . user_lead_info :
queries = [ q . strip ( ) for q in self . user_lead_info . split ( " \n " ) if q . strip ( ) ]
else :
queries = [ self . user_lead_info . strip ( ) ] if self . user_lead_info . strip ( ) else [ ]
self . exa_search_results = [ ]
for q in queries :
result = exa_search ( q )
self . exa_search_results . append ( {
" query " : q ,
" exa_result " : result
} )
def property_research ( self ) :
emma_agent = self . find_agent_by_name ( " Property-Research " )
# Pass ALL exa_search results as direct context to the property research agent
exa_context = " "
if hasattr ( self , " exa_search_results " ) and self . exa_search_results :
# Directly inject all exa_search results as context
exa_context = " \n \n [Exa Search Results] \n "
for item in self . exa_search_results :
exa_context + = f " Query: { item [ ' query ' ] } \n Exa Search Result: { item [ ' exa_result ' ] } \n "
emma_output = emma_agent . run (
f " History: { self . conversation . get_str ( ) } \n "
f " { exa_context } "
f " Conduct research on { self . property_type } properties, analyze market trends, and prepare a comprehensive property report. "
)
self . conversation . add (
role = " Property-Research " , content = emma_output
)
def property_marketing ( self ) :
jack_agent = self . find_agent_by_name ( " Marketing " )
jack_output = jack_agent . run (
f " History: { self . conversation . get_str ( ) } \n "
f " Develop marketing strategies for { self . property_type } properties, create listings, and manage client interactions. "
)
self . conversation . add (
role = " Marketing " , content = jack_output
)
def transaction_management ( self ) :
sophia_agent = self . find_agent_by_name ( " Transaction-Management " )
sophia_output = sophia_agent . run (
f " History: { self . conversation . get_str ( ) } \n "
f " Manage legal and financial aspects of the { self . property_type } property transaction. "
)
self . conversation . add (
role = " Transaction-Management " , content = sophia_output
)
def property_maintenance ( self ) :
michael_agent = self . find_agent_by_name ( " Property-Maintenance " )
michael_output = michael_agent . run (
f " History: { self . conversation . get_str ( ) } \n "
f " Assess and manage maintenance for the { self . property_type } property to prepare it for market. "
)
self . conversation . add (
role = " Property-Maintenance " , content = michael_output
)
def run ( self , task : str ) :
"""
Process the real estate workflow through the swarm , coordinating tasks among agents .
"""
self . conversation . add ( role = self . user_name , content = task )
# Execute workflow stages
self . lead_generation ( )
self . property_research ( )
self . property_marketing ( )
self . transaction_management ( )
self . property_maintenance ( )
return history_output_formatter (
self . conversation , type = self . output_type
)
def main ( ) : def main ( ) :
""" FOR ACTUAL INPUT FROM USER """
user_requirements = """
# # Collect user lead information at the beginning
I ' m looking for a spacious 3-bedroom apartment with modern amenities.
# print("Please provide the following information for lead generation:")
- Price : $ 1 , 000 , 000 - $ 1 , 500 , 000
# questions = [
- Location : Downtown Manhattan , Upper East Side
# "What are the client's property requirements?",
- Investment : Long - term , high ROI preferred
# "What is the client's budget range or constraints?",
- Contact : john . doe @email.com , + 1 - 555 - 123 - 4567
# "What are the client's preferred locations?",
- Timeline : Within the next 3 months
# "What are the client's investment goals?",
- Financials : Pre - approved for mortgage
# "What is the client's contact information?",
"""
# "What is the urgency of the transaction?",
# "Is the client financially ready to proceed?"
# ]
# user_lead_info = ""
agents = [
# for q in questions:
Agent (
# answer = input(f"{q}\n> ").strip()
agent_name = " Alex-Requirements-Analyzer " ,
# user_lead_info += f"Q: {q}\nA: {answer if answer else '[No answer provided]'}\n\n"
agent_description = " Analyzes user property requirements and creates optimized property search queries. " ,
# Pre-filled placeholder answers for each question
system_prompt = REQUIREMENTS_ANALYZER_PROMPT ,
user_lead_info = [
model_name = " gpt-4.1 " ,
" Spacious 3-bedroom apartment with modern amenities " ,
max_loops = 1 ,
" $1,000,000 - $1,500,000 " ,
temperature = 0.7 ,
" Downtown Manhattan, Upper East Side " ,
) ,
" Long-term investment with high ROI " ,
Agent (
" john.doe@email.com, +1-555-123-4567 " ,
agent_name = " Emma-Property-Research " ,
" Within the next 3 months " ,
agent_description = " Conducts comprehensive property search and market analysis. " ,
" Yes, pre-approved for mortgage "
system_prompt = PROPERTY_RESEARCH_PROMPT ,
model_name = " gpt-4.1 " ,
max_loops = 1 ,
temperature = 0.7 ,
tools = [ get_properties ] ,
) ,
Agent (
agent_name = " Jack-Marketing " ,
agent_description = " Develops and executes marketing strategies for properties. " ,
system_prompt = MARKETING_PROMPT ,
model_name = " gpt-4.1 " ,
max_loops = 1 ,
temperature = 0.7 ,
) ,
Agent (
agent_name = " Sophia-Transaction-Management " ,
agent_description = " Handles legal, financial, and document aspects of property transactions. " ,
system_prompt = TRANSACTION_MANAGEMENT_PROMPT ,
model_name = " gpt-4.1 " ,
max_loops = 1 ,
temperature = 0.7 ,
) ,
Agent (
agent_name = " Michael-Property-Maintenance " ,
agent_description = " Oversees property condition, maintenance, and market readiness. " ,
system_prompt = PROPERTY_MAINTENANCE_PROMPT ,
model_name = " gpt-4.1 " ,
max_loops = 1 ,
temperature = 0.7 ,
) ,
]
]
workflow = SequentialWorkflow (
# Initialize the swarm
name = " real-estate-sequential-workflow " ,
real_estate_swarm = RealEstateSwarm (
agents = agents ,
max_loops = 1 ,
max_loops = 1 ,
name = " Global Real Estate Solutions " ,
team_awareness = True ,
description = " Comprehensive AI-driven real estate transaction workflow " ,
user_name = " Real Estate Director " ,
property_type = " Luxury Residential " ,
output_type = " json " ,
user_lead_info = user_lead_info ,
)
)
# Sample real estate task
workflow . run ( user_requirements )
sample_task = """
We have a high - end luxury residential property in downtown Manhattan .
Client requirements :
- Sell the property quickly
- Target high - net - worth individuals
- Maximize property value
- Ensure smooth and discreet transaction
"""
# Run the swarm
real_estate_swarm . run ( task = sample_task )
if __name__ == " __main__ " : if __name__ == " __main__ " :
main ( )
main ( )
Aksh Parekh
2 months ago
committed by
GitHub