How long does this build an ai job search agent tutorial take?

Most developers complete this tutorial in 15 to 30 minutes. You will need a Scavio API key (free tier works) and a working Python or JavaScript environment.

What do I need before starting?

Python 3.10+. Scavio API key. A Scavio API key gives you 50 free credits on signup.

Can I run this tutorial with the free tier?

Yes. The free tier includes 50 credits on signup, which is more than enough to complete this tutorial and prototype a working solution.

What frameworks does this work with?

Scavio has a native LangChain package (langchain-scavio), an MCP server, and a plain REST API that works with any HTTP client. This tutorial uses the raw REST API, but you can adapt to your framework of choice.

Build an AI Job Search Agent (2026)

An r/hiringcafe post launched an AI Job Search Agent matching power-user filters on a job board. This tutorial walks the data layer: SERP for ATS pages, Reddit for hiring threads, extract for the full job description. Scavio has no extract or crawl endpoint - it returns structured search data (SERP rows, Reddit post bodies, YouTube transcripts), not arbitrary page HTML or markdown. This tutorial uses what the API really returns for a URL: the Google result row, with its title, link and snippet. Fetch the page yourself when you genuinely need the full body.

Prerequisites

Python 3.10+
Scavio API key

Walkthrough

Step 1: Take user resume + preferences

Inputs: skills, location, salary range, remote preference.

Python

USER = {'skills': ['python', 'rust', 'mcp'], 'location': 'remote', 'min_salary': 150000}

Step 2: Generate ATS-targeted queries

site: queries find Greenhouse, Lever, Ashby pages.

Python

ATS_DOMAINS = ['greenhouse.io', 'lever.co', 'ashbyhq.com']

def queries(user):
    return [f'site:{d} {" ".join(user["skills"])} {user["location"]}' for d in ATS_DOMAINS]

Step 3: SERP across ATS domains

Scavio search per query.

Python

import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']

def ats_jobs(q):
    r = requests.post('https://api.scavio.dev/api/v2/google',
        headers={'Authorization': f'Bearer {API_KEY}'}, json={'query': q}).json()
    return r.get('organic_results', [])[:20]

Step 4: Reddit hiring threads as a parallel surface

r/cscareerquestions, r/jobs, niche subs surface unannounced openings.

Python

def reddit_jobs(skills):
    return requests.post('https://api.scavio.dev/api/v1/reddit/search',
        headers={'Authorization': f'Bearer {API_KEY}'},
        json={'query': f'{" ".join(skills)} hiring 2026'}).json().get('results', [])[:20]

Step 5: Extract full job description

ATS pages render as clean markdown.

Python


# Scavio returns structured search data, not page bodies: there is no extract
# or crawl endpoint. What you can get for a URL is the Google result row it
# already has - title, link and snippet. Fetch the page yourself when you need
# the full body.
def scavio_page_row(url, headers):
    target = url.split("://")[-1].rstrip("/")
    r = requests.post("https://api.scavio.dev/api/v2/google", headers=headers,
                      json={"query": "site:" + target}, timeout=30)
    r.raise_for_status()
    rows = r.json().get("organic_results", [])
    return rows[0] if rows else {"title": "", "link": url, "snippet": ""}

def description(url):
    r = scavio_page_row(url, {'Authorization': f'Bearer {API_KEY}'})
    return r.get('snippet', '')

Step 6: Score + rank with LLM

Match resume against full description.

Python

import anthropic
client = anthropic.Anthropic()

def score(resume, jd):
    msg = client.messages.create(model='claude-sonnet-4-6', max_tokens=200,
        messages=[{'role':'user','content':f'Score this resume vs job description 0-100 with 1-line reason. RESUME: {resume}. JD: {jd}'}])
    return msg.content[0].text

Python Example

Python

# Daily run: 3 ATS queries + 1 Reddit query + ~20 extracts = ~25 credits = $0.11.

JavaScript Example

JavaScript

// Same pattern in TS via the Anthropic SDK.

Expected Output

JSON

About 30-60 ranked jobs per day with full descriptions and 0-100 fit scores. Reddit thread surfacing catches unannounced roles 3-7 days earlier.

Prerequisites

Python 3.10+
Scavio API key

Walkthrough

Step 1: Take user resume + preferences

Inputs: skills, location, salary range, remote preference.

Python

USER = {'skills': ['python', 'rust', 'mcp'], 'location': 'remote', 'min_salary': 150000}

Step 2: Generate ATS-targeted queries

site: queries find Greenhouse, Lever, Ashby pages.

Python

ATS_DOMAINS = ['greenhouse.io', 'lever.co', 'ashbyhq.com']

def queries(user):
    return [f'site:{d} {" ".join(user["skills"])} {user["location"]}' for d in ATS_DOMAINS]

Step 3: SERP across ATS domains

Scavio search per query.

Python

import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']

def ats_jobs(q):
    r = requests.post('https://api.scavio.dev/api/v2/google',
        headers={'Authorization': f'Bearer {API_KEY}'}, json={'query': q}).json()
    return r.get('organic_results', [])[:20]

Step 4: Reddit hiring threads as a parallel surface

r/cscareerquestions, r/jobs, niche subs surface unannounced openings.

Python

def reddit_jobs(skills):
    return requests.post('https://api.scavio.dev/api/v1/reddit/search',
        headers={'Authorization': f'Bearer {API_KEY}'},
        json={'query': f'{" ".join(skills)} hiring 2026'}).json().get('results', [])[:20]

Step 5: Extract full job description

ATS pages render as clean markdown.

Python


# Scavio returns structured search data, not page bodies: there is no extract
# or crawl endpoint. What you can get for a URL is the Google result row it
# already has - title, link and snippet. Fetch the page yourself when you need
# the full body.
def scavio_page_row(url, headers):
    target = url.split("://")[-1].rstrip("/")
    r = requests.post("https://api.scavio.dev/api/v2/google", headers=headers,
                      json={"query": "site:" + target}, timeout=30)
    r.raise_for_status()
    rows = r.json().get("organic_results", [])
    return rows[0] if rows else {"title": "", "link": url, "snippet": ""}

def description(url):
    r = scavio_page_row(url, {'Authorization': f'Bearer {API_KEY}'})
    return r.get('snippet', '')

Step 6: Score + rank with LLM

Match resume against full description.

Python

import anthropic
client = anthropic.Anthropic()

def score(resume, jd):
    msg = client.messages.create(model='claude-sonnet-4-6', max_tokens=200,
        messages=[{'role':'user','content':f'Score this resume vs job description 0-100 with 1-line reason. RESUME: {resume}. JD: {jd}'}])
    return msg.content[0].text

Python Example

Python

# Daily run: 3 ATS queries + 1 Reddit query + ~20 extracts = ~25 credits = $0.11.

JavaScript Example

JavaScript

// Same pattern in TS via the Anthropic SDK.

Expected Output

JSON

About 30-60 ranked jobs per day with full descriptions and 0-100 fit scores. Reddit thread surfacing catches unannounced roles 3-7 days earlier.

How to Build an AI Job Search Agent

Prerequisites

Walkthrough

Step 1: Take user resume + preferences

Step 2: Generate ATS-targeted queries

Step 3: SERP across ATS domains

Step 4: Reddit hiring threads as a parallel surface

Step 5: Extract full job description

Step 6: Score + rank with LLM

Python Example

JavaScript Example

Expected Output

Related Tutorials

Frequently Asked Questions

How long does this build an ai job search agent tutorial take?

What do I need before starting?

Can I run this tutorial with the free tier?

What frameworks does this work with?

Related Resources

AI Job Search Agent Stack

Best APIs for Building Job Search Platforms in 2026

Best Agent Search Fallback Architectures (2026)

Build a Job Search Agent in n8n with Zero Custom Code

Agent Search Cost and Budget Tracking Workflow

AI Job Search Agent (End User)

Start Building

How to Build an AI Job Search Agent

Prerequisites

Walkthrough

Step 1: Take user resume + preferences

Step 2: Generate ATS-targeted queries

Step 3: SERP across ATS domains

Step 4: Reddit hiring threads as a parallel surface

Step 5: Extract full job description

Step 6: Score + rank with LLM

Python Example

JavaScript Example

Expected Output

Related Tutorials

Frequently Asked Questions

How long does this build an ai job search agent tutorial take?

What do I need before starting?

Can I run this tutorial with the free tier?

What frameworks does this work with?

Related Resources

AI Job Search Agent Stack

Best APIs for Building Job Search Platforms in 2026

Best Agent Search Fallback Architectures (2026)

Build a Job Search Agent in n8n with Zero Custom Code

Agent Search Cost and Budget Tracking Workflow

AI Job Search Agent (End User)

Start Building