from swarms import Agent, SequentialWorkflow
import http.client
import json
import urllib.parse
def get_jobs(query: str, limit: int = 10) -> str:
"""
Fetches real-time jobs using JSearch API based on role, location, and experience.
Uses http.client to match verified working example.
"""
# Prepare query string for URL
encoded_query = urllib.parse.quote(query)
path = f"/search?query={encoded_query}&page=1&num_pages=1&country=us&limit={limit}&date_posted=all"
conn = http.client.HTTPSConnection("jsearch.p.rapidapi.com")
headers = {
"x-rapidapi-key": "", #<------- Add your RapidAPI key here otherwise it will not work
"x-rapidapi-host": "jsearch.p.rapidapi.com"
}
conn.request("GET", path, headers=headers)
res = conn.getresponse()
data = res.read()
decoded = data.decode("utf-8")
try:
result_dict = json.loads(decoded)
except Exception:
# fallback for unexpected output
return decoded
results = result_dict.get("data", [])
jobs_list = [
{
"title": job.get("job_title"),
"company": job.get("employer_name"),
"location": job.get("job_city") or job.get("job_country"),
"experience": job.get("job_required_experience", {}).get("required_experience_in_months"),
"url": job.get("job_apply_link")
}
for job in results
]
return json.dumps(jobs_list)
REQUIREMENTS_ANALYZER_PROMPT = """
You are the Requirements Analyzer Agent for Job Search.
ROLE:
Extract and clarify job search requirements from user input to create optimized search queries.
RESPONSIBILITIES:
- Engage with the user to understand:
* Desired job titles and roles
* Required skills and qualifications
* Preferred locations (remote, hybrid, on-site)
* Salary expectations
* Company size and culture preferences
* Industry preferences
* Experience level
* Work authorization status
* Career goals and priorities
- Analyze user responses to identify:
* Key search terms and keywords
* Must-have vs nice-to-have requirements
* Deal-breakers or constraints
* Priority factors in job selection
- Generate optimized search queries:
* Create 3-5 targeted search queries based on user requirements
OUTPUT FORMAT:
Provide a comprehensive requirements analysis:
1. User Profile Summary:
- Job titles of interest
- Key skills and qualifications
- Location preferences
- Salary range
- Priority factors
2. Search Strategy:
- List of 3-5 optimized search queries, formatted EXACTLY for linkedin.com/jobs/search/?keywords=...
- Rationale for each query
- Expected result types
3. Clarifications Needed (if any):
- Questions to refine search
- Missing information
IMPORTANT:
- Always include ALL user responses verbatim in your analysis
- Format search queries clearly for the next agent and fit directly to LinkedIn search URLs
- Be specific and actionable in your recommendations
- Ask follow-up questions if requirements are unclear
"""
SEARCH_EXECUTOR_PROMPT = """
You are the Search Executor Agent for Job Search.
ROLE:
Your job is to execute a job search by querying the tool EXACTLY ONCE using the following required format (FILL IN WHERE IT HAS [ ] WITH THE QUERY INFO OTHERWISE STATED):
The argument for the query is to be provided as a plain text string in the following format (DO NOT include technical addresses, just the core query string):
[jobrole] jobs in [geographiclocation/remoteorinpersonorhybrid]
For example:
developer jobs in chicago
senior product manager jobs in remote
data engineer jobs in new york hybrid
TOOLS:
You have access to three tools:
- get_jobs: helps find open job opportunities for your specific job and requirements.
RESPONSIBILITIES:
- Run ONE single query, in the above format, as the argument to get_jobs.
- Analyze search results for:
* Job title match
* Skills alignment
* Location compatibility
* Salary range fit
* Company reputation
* Role responsibilities
* Growth opportunities
- Categorize each result into one of:
* Strong Match (80-100% alignment)
* Good Match (60-79% alignment)
* Moderate Match (40-59% alignment)
* Weak Match (<40% alignment)
- For each job listing, extract:
* Job title and company
* Location and work arrangement
* Key requirements
* Salary range (if available)
* Application link or contact
* Match score and reasoning
OUTPUT FORMAT:
1. Search Execution Summary:
- The query executed (write ONLY the string argument supplied, e.g., "developer jobs in chicago" or "software engineer jobs in new york remote")
- Total results found
- Distribution by match category
2. Detailed Job Listings (grouped by match strength):
For each job:
- Company and Job Title
- Location and Work Type
- Key Requirements
- Why it's a match (or not)
- Match Score (percentage)
- Application link
- Source (specify get_jobs)
3. Search Insights:
- Common trends/themes in the results
- Gaps between results and requirements
- Market observations
INSTRUCTIONS:
- Run only the single query in the format described above, with no extra path, no technical addresses, and no full URLs.
- Use all three tools, as applicable, with that exact query argument.
- Clearly cite which results come from which source.
- Be objective in match assessment.
- Provide actionable, structured insights.
"""
RESULTS_CURATOR_PROMPT = """
You are the Results Curator Agent for Job Search.
ROLE:
Filter, organize, and present job search results to the user for decision-making.
RESPONSIBILITIES:
- Review all search results from the Search Executor
- Filter and prioritize based on:
* Match scores
* User requirements
* Application deadlines
* Job quality indicators
- Organize results into:
* Top Recommendations (top 3-5 best matches)
* Strong Alternatives (next 5-10 options)
* Worth Considering (other relevant matches)
- For top recommendations, provide:
* Detailed comparison
* Pros and cons for each
* Application strategy suggestions
* Next steps
- Engage user for feedback:
* Present curated results clearly
* Ask which jobs interest them
* Identify what's missing
* Determine if new search is needed
OUTPUT FORMAT:
Provide a curated job search report:
1. Executive Summary:
- Total jobs reviewed
- Number of strong matches
- Key findings
2. Top Recommendations (detailed):
For each (max 5):
- Company & Title
- Why it's a top match
- Key highlights
- Potential concerns
- Recommendation strength (1-10)
- Application priority (High/Medium/Low)
3. Strong Alternatives (brief list):
- Company & Title
- One-line match summary
- Match score
4. User Decision Point:
Ask the user:
- "Which of these jobs interest you most?"
- "What's missing from these results?"
- "Should we refine the search or proceed with applications?"
- "Any requirements you'd like to adjust?"
5. Next Steps:
Based on user response, either:
- Proceed with selected jobs
- Run new search with adjusted criteria
- Deep dive into specific opportunities
IMPORTANT:
- Make it easy for users to make decisions
- Be honest about job fit
- Provide clear paths forward
- Always ask for user feedback before concluding
"""
def main():
# User input for job requirements
user_requirements = """
I'm looking for a senior software engineer position with the following requirements:
- Job Title: Senior Software Engineer or Staff Engineer
- Skills: Python, distributed systems, cloud architecture (AWS/GCP), Kubernetes
- Location: Remote (US-based) or San Francisco Bay Area
- Salary: $180k - $250k
- Company: Mid-size to large tech companies, prefer companies with strong engineering culture
- Experience Level: 7+ years
- Industry: SaaS, Cloud Infrastructure, or Developer Tools
- Work Authorization: US Citizen
- Priorities: Technical challenges, work-life balance, remote flexibility, equity upside
- Deal-breakers: No pure management roles, no strict return-to-office policies
"""
# Define your agents in a list as in the example format
agents = [
Agent(
agent_name="Sarah-Requirements-Analyzer",
agent_description="Analyzes user requirements and creates optimized job search queries.",
system_prompt=REQUIREMENTS_ANALYZER_PROMPT,
model_name="gpt-4.1",
max_loops=1,
temperature=0.7,
),
Agent(
agent_name="David-Search-Executor",
agent_description="Executes job searches and analyzes results for relevance.",
system_prompt=SEARCH_EXECUTOR_PROMPT,
model_name="gpt-4.1",
max_loops=1,
temperature=0.7,
tools=[get_jobs],
),
Agent(
agent_name="Lisa-Results-Curator",
agent_description="Curates and presents job results for user decision-making.",
system_prompt=RESULTS_CURATOR_PROMPT,
model_name="gpt-4.1",
max_loops=1,
temperature=0.7,
),
]
# Setup the SequentialWorkflow pipeline (following the style of the ETF example)
workflow = SequentialWorkflow(
name="job-search-sequential-workflow",
agents=agents,
max_loops=1,
team_awareness=True,
)
workflow.run(user_requirements)
if __name__ == "__main__":
main()
