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Raman Kumar
Raman Kumar

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Automated API Testing with Keploy Cloud: A Developer’s CI/CD Journey

🚀 From Manual Tests to AI-Powered API Testing with Keploy Cloud + GitHub Actions

Testing APIs used to be one of the most repetitive and error-prone parts of my development cycle—until I tried Keploy Cloud, an AI-powered API testing platform. What began as a manual, time-consuming task evolved into a fully automated pipeline with zero manual test writing and full coverage in just minutes!

In this post, I’ll walk you through:

  • The pain points of traditional API testing
  • How I used Keploy Cloud to auto-generate API tests from real interactions
  • How I integrated everything into GitHub Actions
  • What I learned through the process

🐢 The Struggle Before Keploy

Before Keploy, API testing meant:

  • Writing and updating dozens of test cases manually
  • Using tools like Postman or Supertest
  • Dealing with breaking changes and test maintenance
  • Missing real-world edge cases

It wasn’t fun—and it definitely wasn’t scalable.

Code-Base

🤖 Enter Keploy Cloud: AI-Powered Testing

Keploy’s dashboard lets you record real API interactions (like when using curl or frontend clients), and then automatically turns them into test cases using AI.

Instead of writing .test.ts files or maintaining mocks, I just used my app normally. Keploy did the rest.

🌐 No keploy.yaml, No Problem

I didn’t have to manually define test configs. Keploy Cloud handled test creation, storage, and execution via its dashboard. My only job was to:

  • Export the OpenAPI schema (which Keploy supports)
  • Make API calls
  • Let Keploy do its magic!

🛠️ CI/CD Integration via GitHub Actions

Here’s where it gets exciting. I fully automated the testing workflow by integrating Keploy into my GitHub Actions pipeline.

🧩 My ci-cd.yml Includes: 

- name: Install Keploy CLI
  run: |
    curl --silent -L https://keploy.io/ent/install.sh | bash

- name: Run Keploy Test Suite
  run: |
    export KEPLOY_API_KEY=${{ secrets.KEPLOY_API_KEY }}
    export KEPLOY_APP_ID=${{ secrets.KEPLOY_APP_ID }}
    export KEPLOY_BASE_PATH=${{ secrets.KEPLOY_BASE_PATH }}
    keploy test-suite --app=$KEPLOY_APP_ID --base-path $KEPLOY_BASE_PATH --cloud
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With this:

  • ✅ I ran all Keploy tests stored in the cloud dashboard
  • ✅ Pipeline failures occurred if tests failed
  • ✅ Full visibility was provided via Keploy Dashboard

🖥️ Full Job Matrix

My pipeline has four jobs:

  1. lint-and-build
  2. unit-tests
  3. api-testing (Keploy)
  4. test-summary

Each job runs independently, with Keploy Cloud executing the API tests on each push to main or develop.

📸 What It Looks Like

I've added screenshots of:

  • Keploy test dashboard
  • CI/CD test summary
  • Keploy CLI execution logs

Keploy Dashboard

CI/CD Test Summary

(If you're reading this now, stay tuned or check my GitHub repo linked below!)

🔍 Lessons Learned

  • Keploy Cloud is a game-changer. I didn’t write a single API test manually—yet I achieved test coverage on every endpoint.
  • CI/CD + AI = powerful duo. Automating Keploy in GitHub Actions ensures I never forget to test.
  • Secrets matter. Be sure to add your Keploy API key, app ID, and base path as GitHub Secrets.
  • Dashboards are gold. The Keploy Cloud UI gives me insights into test pass/fail status with easy-to-read logs.

💡 Final Thoughts

Keploy Cloud turned API testing from a headache into something I barely think about—because it just works.

If you:

  • Hate writing repetitive API tests
  • Want to test real-world API flows
  • Are setting up a CI/CD pipeline

…I highly recommend giving Keploy Cloud a try. It’s AI for devs, where it actually makes life easier.

🔗 Useful Links:

Let me know in the comments if you've tried Keploy or want help getting started!

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