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Stagehand + Browserbase: Credit Karma with Caching & Variables

AT A GLANCE

  • Goal: Automate Credit Karma mortgage rate comparisons using caching and parameterized actions
  • Key Features:
    • Cache DOM snapshots for 10x faster subsequent runs
    • Parameterize form inputs with variables for flexible automation
    • Get mortgage refinance rates programmatically
  • Tech Stack: Stagehand, Browserbase, Python
  • Docs → Stagehand | Browserbase

GLOSSARY

  • Caching: Stagehand stores DOM snapshots in a local cache directory to dramatically speed up repeat automations. On subsequent runs, Stagehand uses cached DOM state instead of re-analyzing pages from scratch. Docs → Caching Guide
  • Variables: Parameterize your act() instructions with %variableName% syntax to make automations reusable and dynamic. Docs → act() with Variables
  • act(): Stagehand's natural language instruction method that performs UI actions (clicking, typing, selecting) without selectors. Docs → act() Method
  • Browserbase: Cloud browser infrastructure for reliable, scalable web automation with stealth mode and CAPTCHA solving. Docs → Browserbase Platform

QUICKSTART

  1. cd python/credit-karma-cache
  2. uv venv venv
  3. source venv/bin/activate # On Windows: venv\Scripts\activate
  4. uv pip install .
  5. cp .env.example .env # Add your Browserbase API key and Project ID to .env
  6. python main.py

EXPECTED OUTPUT

On the first run, you'll see:

🏠 Starting Credit Karma mortgage rate automation...

✓ Successfully navigated to Credit Karma mortgage rates page
✓ Selected Refinance tab
✓ Selected credit score: Above 760
✓ Entered ZIP code: 10001
✓ Entered loan balance: $100000
✓ Entered home value: $150000
✓ Entered cash-out amount: $0
✓ Clicked 'Get my rates' button

✓ Mortgage rates loaded successfully!

┌─────────────────────────────────────┐
│ Credit Karma Refinance Query        │
├─────────────────────────────────────┤
│ Credit Score: Above 760             │
│ ZIP Code: 10001                     │
│ Loan Balance: $100000               │
│ Home Value: $150000                 │
│ Cash Out: $0                        │
└─────────────────────────────────────┘

On subsequent runs: The automation runs ~10x faster thanks to caching! Stagehand reuses DOM snapshots from the credit-karma-cache/ directory instead of re-analyzing every page element.

COMMON PITFALLS

  • Missing environment variables: Ensure your .env file contains valid BROWSERBASE_PROJECT_ID and BROWSERBASE_API_KEY. The script will fail silently if these are missing.
  • Cache directory conflicts: If you run multiple Credit Karma automations simultaneously, use different cache_dir values to avoid conflicts. Example: cache_dir="credit-karma-cache-user1"
  • Stale cache issues: If Credit Karma's UI changes significantly, delete the credit-karma-cache/ directory to regenerate fresh snapshots.
  • Rate limiting: Credit Karma may rate-limit requests. Use Browserbase's stealth mode (advanced_stealth=True) or add delays between runs if you encounter blocks.

Find more information on your Browserbase dashboard → Sessions

USE CASES

  • Mortgage shopping automation: Compare refinance rates across different credit scores and loan amounts without manual data entry
  • Financial data aggregation: Scrape mortgage rates for market analysis, customer comparison tools, or personal finance apps
  • Testing and QA: Validate Credit Karma's UI flows and form handling across different user scenarios
  • Personal finance tracking: Monitor how rate changes affect your specific refinance scenario over time
  • Lead generation tools: Pre-qualify mortgage leads by automating rate checks based on customer-provided information

NEXT STEPS

  • Extract rate data: Use Stagehand's extract() method to scrape the mortgage rates table and return structured JSON data
  • Loop through scenarios: Iterate through multiple USER_CONFIG dictionaries to compare rates across different credit scores or ZIP codes
  • Add error recovery: Implement retry logic with exponential backoff for network failures or CAPTCHA challenges
  • Schedule regular runs: Set up a cron job or GitHub Action to track rate changes daily/weekly
  • Integrate with databases: Store extracted rates in PostgreSQL, MongoDB, or Airtable for historical tracking
  • Enable proxies: Set proxies=True in browserbase_session_create_params to rotate IPs and avoid rate limiting

HELPFUL RESOURCES