Auto-Generate Integration Tests from User Traffic. Keploy does comprehensive Backend testing (99% coverage) by creating test-cases from real-world production traffic without any manual effort. All the dependencies of the app are mocked so no need to have Test Environments.
External link for Keploy 🐰
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Keploy 🐰 reposted this
𝟯𝟬𝟬𝗠+ tests & mocks this year 🤯 and it's not just devs running Keploy anymore. the agents are using it too 👀🤖 Makes sense tbh! 😍 if AI is writing the code, something's gotta test it. Keploy digital twins, recordings. Turns out machines want confidence before shipping too 😅 Thank you to the Keploy community ❤️ Many more DEAD BUGS to count! 🔪🐞 #testautomation #continuousVerification
Real user traffic can expose performance bottlenecks that often go unnoticed during development This Keploy guide explores how open-source load testing tools helps DevOps teams build reliable, scalable applications and improve release confidence 👉 Read here: https://lnkd.in/gmrg9ZV3 #LoadTesting #DevOps #PerformanceTesting #SoftwareTesting #CI_CD #OpenSource #Keploy
Keploy 🐰 reposted this
Keploy: Automatically captures API traffic and converts it into reusable test cases and mocks — reducing backend testing effort significantly. #AIinQA #AutomationTesting #SoftwareTesting #QualityEngineering
Keploy 🐰 reposted this
AI is helping developers write code faster than ever before. But can our testing processes keep up? I recently came across insights shared by Keploy, an AI-powered testing platform: 📊 46% of new code is now AI-authored. 📊 AI-assisted code changes are linked to 3.4× more production incidents. 📊 Software defects could rise by 2,500% by 2028. ✅ Faster coding ❌ More defects ❌ Greater production risk As software engineers, we're witnessing a major shift. AI can dramatically boost productivity and speed up development, but speed without quality creates risk. The future isn't just about generating code faster—it's about ensuring that code is reliable, secure, and thoroughly tested before it reaches production. Source: https://keploy.io #AI #SoftwareEngineering #SoftwareTesting #QA #DevOps #AutomationTesting #GenAI
Keploy 🐰 reposted this
We’re expanding the Sales team at Keploy 🐰 🚀 If you’re excited about enterprise sales, developer tools, AI, and working with high-growth startups - I'd love to talk. We’re hiring Account Executives across India and the US. apply here: 👉 https://lnkd.in/giEaZ37p At Keploy, we’re helping engineering teams automatically generate tests, sandbox, and coverage from real API traffic, so developers can ship faster without breaking things. It's like giving the power of making Digital Twins to your customers. If you’re someone who loves technical sales, understands enterprise motion, and wants to be part of a team building in the AI + testing infrastructure space, apply here: 👉 https://lnkd.in/giEaZ37p #hiring #AE #sales #accountExecutives #enterpriseSales
Keploy 🐰 reposted this
Keploy 🐰 𝗚𝗶𝘁𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝗦𝗙 by was special. 🧡 Not because of the size of the room. Because the room was curious. People came after work to talk about what AI agents are really changing - testing, documentation, token, trust, infra, architecture, and all the messy parts we don’t talk about enough. Huge thanks to our speakers — Paige Bailey from Google DeepMind, Ritvik Pandya from J.P. Morgan, Mohit Juneja from TestMu AI, Ellie Daw from @gjalla Shubham Jain 🐰 from Keploy 🐰, Satya Devarakonda from Scalekit, and everyone from the community who kept it honest and sharing what they’re actually shipping. Still smiling from the energy in that room. 😄 #GitTogether #DeveloperCommunity #AIEngineering #AgenticTesting #MCP #OpenSource #SanFrancisco
Quality testing starts with quality test data, helping teams catch issues early and keep releases moving smoothly across environments This Keploy guide explores test data management, common challenges, and best practices to help teams build more reliable testing workflows 👉 Read here: https://lnkd.in/gZvnpHPi #TestDataManagement #SoftwareTesting #TestAutomation #QA #DevOps #CICD #Keploy
Keploy 🐰 reposted this
🎙️ Just leveled up my AI Voice Interview Agent with automated API testing via Keploy 🐰 — and the integration journey was anything but boring. This project is a fully offline mock interview platform that simulates real interview experiences using local LLMs, speech recognition, and voice synthesis — zero cloud dependency at runtime. Keploy 🐰 is an open-source, AI-powered API testing platform that automates backend testing using traffic recording and replay validation instead of manually written test scripts. ───────────────────────── 🔧 Tech Stack ───────────────────────── ⚡ FastAPI · 🐳 Docker 🦙 Ollama 🎤 Faster-Whisper (offline STT) 🔊 pyttsx3 (offline TTS) 🖥️ Streamlit 🐰 Keploy (AI-powered API testing) ───────────────────────── ✅ Key Implementations ───────────────────────── 🐳 Fully Dockerized backend services 🧠 Local LLM inference via Ollama (no API keys, no internet) 📄 Resume-aware AI interview workflows 🎯 Automated API recording & replay testing with Keploy 📡 Captured real interview traffic flows — resume upload, question generation, feedback scoring, and speech synthesis 🔍 Validated all endpoints without writing a single manual test script ───────────────────────── 🕵️ The Interesting Part ───────────────────────── The trickiest challenge wasn't the AI — it was the networking. Debugging communication between 5 layers simultaneously: 🪟 Windows host machine 🐳 Docker containers 🦙 Ollama local inference server 🖥️ Streamlit frontend 🔁 Keploy proxy interception …taught me more about Docker networking in one afternoon than months of reading docs ever could. 😅 ───────────────────────── 💡 What I Took Away ───────────────────────── 🏗️ Designing production-style AI application architecture 🧪 Record-and-replay testing for non-deterministic LLM responses 🐳 Docker networking deep-dive (host.docker.internal, bridge networks, service discovery) 🔒 Building local-first AI systems with no data leaving the machine 📊 Backend observability and API validation at scale 🔗 GitHub: https://lnkd.in/gQSwNmpr Find more about AI Voice Interview Agent here : https://lnkd.in/g-qMQ7we If you're building AI apps and skipping testing because "LLM outputs are unpredictable" — Keploy's noise filtering and stub mocking make it actually tractable. Highly recommend exploring it. 🙌 #AI #Python #FastAPI #Docker #Ollama #Keploy #LLM #GenerativeAI #MachineLearning #BackendDevelopment #APITesting #DevOps #OpenSource #SoftwareEngineering #LocalAI #OfflineAI
Well-designed APIs are the foundation of modern applications, enabling smooth integrations, better developer experience, and scalable systems across products and services This Keploy guide explores API design best practices, key principles, and common mistakes to help teams build clean and reliable APIs 👉 Read here: https://lnkd.in/gVuhTcS5 #APIDesign #SoftwareDevelopment #BackendDevelopment #SoftwareTesting #DevOps #CICD #Keploy
Keploy 🐰 reposted this
Today Google I/O, Sundar Pichai mentioned something that really stuck with me: At 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻-𝘁𝗼𝗸𝗲𝗻/𝗱𝗮𝘆 scale, moving 80% of workloads to Gemini 3.5 Flash could 𝘀𝗮𝘃𝗲 𝗼𝘃𝗲𝗿 $𝟭𝗕 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆 per organization. Thanks to Google for Startups, I also got to speak with a lot of engineering leaders, CIOs, founders, and industry execs this week. And the pattern was obvious: Teams want to adopt coding agents. > The blocker is no longer just model quality or token cost. >> It’s infra. • Where do these agents safely run the app?? • Where do they get realistic DB states?? • How do they test against queues, APIs, third-party services, auth flows, edge cases, and compliance boundaries?? > 𝘉𝘦𝘤𝘢𝘶𝘴𝘦 𝘪𝘧 𝘦𝘷𝘦𝘳𝘺 𝘢𝘨𝘦𝘯𝘵 𝘯𝘦𝘦𝘥𝘴 𝘪𝘵𝘴 𝘰𝘸𝘯 𝘴𝘵𝘢𝘨𝘪𝘯𝘨-𝘭𝘪𝘬𝘦 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵, 𝘵𝘩𝘦 𝘤𝘰𝘴𝘵 𝘫𝘶𝘴𝘵 𝘮𝘰𝘷𝘦𝘴 𝘧𝘳𝘰𝘮: 𝘁𝗼𝗸𝗲𝗻𝘀 → 𝘁𝗲𝘀𝘁 𝗶𝗻𝗳𝗿𝗮 I’ve calling this the 𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝗳𝗿𝗮 𝗧𝗮𝘅. 💵 💥 • Cheaper models will create more agents. • More agents will create more code. • More code will need more validation. And unless the infra layer changes, teams will either overspend on environments or slow down agent adoption. At Keploy 🐰, this is exactly the problem we solved: record real app behavior once, then replay it in isolated sandboxes with mocked dependencies and realistic data states. 𝘚𝘰 𝘢𝘨𝘦𝘯𝘵𝘴 𝘤𝘢𝘯 𝘵𝘦𝘴𝘵 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘵𝘦𝘢𝘮𝘴 𝘴𝘱𝘪𝘯𝘯𝘪𝘯𝘨 𝘶𝘱 100+ 𝘴𝘵𝘢𝘨𝘪𝘯𝘨 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵𝘴. Google is helping bring down the cost of intelligence. Now Keploy 🐰 is helping 𝗯𝗿𝗶𝗻𝗴 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗶𝗻𝗳𝗿𝗮 𝗰𝗼𝘀𝘁 𝗼𝗳 𝘁𝗿𝘂𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. >> So we’re launching #𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝟭𝟬𝗕 - to help engineering teams identify and cut unnecessary test, staging, and agent-validation infra spend. The goal is: Help 𝘀𝗮𝘃𝗲 $𝟭𝟬𝗕 𝗶𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘄𝗮𝘀𝘁𝗲 for teams adopting agentic software development. If your team has a large infra bill, too many staging environments, or you’re trying to adopt coding agents but don’t know where they’ll safely run and test code, please feel free to connect with me. #googleIO #project10B #creatorStudio