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Hercules – Open-Source software testing agent

Hacker News

Hercules – Open-Source software testing agent

Hey HN, After spending way too many late nights wrestling with brittle test scripts and UIs that seem to change just to spite me, we decided to do something about it. So we built Hercules, an open-source testing agent aiming to make end-to-end testing of modern web applications less of a Herculean task (pun absolutely intended). P.S.- Paid solutions can cost upwords of $15000 and still struggle with maintenance. What is Hercules? Hercules is an AI-powered testing agent that turns simple Gherkin steps into fully automated end-to-end tests, eliminating the need for coding skills. It leverages Large Language Models (LLMs) to reason and perform actions based on test requirements. Key Features- - Gherkin In, Results Out: Just provide your tests in Gherkin format, and Hercules executes them automatically, outputting results in JUnit XML and HTML reports. - Free and Open Source: Hercules is released under the AGPL v3 license, allowing anyone to use, modify, and contribute. - Salesforce Ready: Capable of handling intricate UIs, Hercules excels in testing platforms like Salesforce. - No Code Required: Automates Gherkin features without the need for scripting or dealing with selectors. - Multilingual Support: Supports multiple languages out of the box, facilitating global collaboration. - Precisely Accurate: Records execution videos and captures network logs for reliable test results. - No Maintenance: Features auto-healing capabilities to adapt to changes without manual intervention. - CI/CD Ready: Easily integrates into your CI/CD pipeline and can run in Docker containers. Why is Hercules Different? Unlike traditional testing tools, Hercules acts as an intelligent agent that can think, reason, and adapt. It's built on a multi-agent architecture, allowing it to plan and execute tasks based on the provided Gherkin steps. Additionally, it comes packed with tool usage like Browsers, or APIs; and users can build/attach external tools too. Try It Out The source code and detailed documentation are available on GitHub: [ https://github.com/test-zeus-ai/testzeus-hercules](https://g... To get started: - Install via PyPI: ```bash pip install testzeus-hercules playwright install --with-deps ``` - Or use the Docker image: ```bash docker pull testzeus/hercules:latest ``` We've included sample feature files and a quick-start guide in the README to help you get up and running quickly. Feedback Welcome- We'd love to hear your thoughts, experiences, or even horror stories about testing gone wrong. Feel free to open issues on GitHub or join forces on our Discord : https://discord.gg/4fyEMWVD

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, users, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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