Why I created an HTTP client that doesn't try to be clever and why it might interest you
At Q2BSTUDIO we believe that sometimes the best solution is not to reinvent the wheel. Many tools for testing APIs introduce their own syntax, heavy interfaces, or abstractions that complicate workflows. Our proposal aligns with that philosophy: use HTTP syntax as-is to work with APIs directly, simply, and versionably. As a company specialized in custom applications and custom software, at Q2BSTUDIO we value practical solutions that favor productivity and continuous integration.
The current problem is the proliferation of abstractions. Tools with graphical interfaces that consume resources and don't work well in CI, file-based tools with proprietary syntax that force you to learn something new, and command-line utilities without persistence or project structure. All of this complicates working with APIs in teams that already use version control, automated deployments, and DevOps practices. At Q2BSTUDIO, where we offer AWS and Azure cloud services and business intelligence services, we prefer solutions that fit the team's workflow and don't break it.
The solution we propose is simple: raw HTTP organized in workspaces. Writing requests in traditional HTTP format allows you to reuse existing knowledge, keep all files in the repository, and automate their execution in pipelines. This makes it easy to integrate API tests into code repositories, something key for projects where we develop custom software and artificial intelligence integrations.
How it works in practice: create docks or workspaces that act as project folders, write .http files with standard requests, keep env files with environment variables, and run requests from the command line. Configuration can be hierarchical, with variable inheritance from global levels to specific folders, which simplifies managing local, staging, and production environments. This way of organizing is especially useful when we deploy solutions on AWS and Azure cloud services and need consistency between environments.
The variables are smart: in addition to simple substitutions, functions are supported to read files, calculate checksums, generate UUIDs and timestamps, and other common operations. This is very useful for workflows that include file uploads or dynamic data generation in automated tests. At Q2BSTUDIO we have applied these ideas in artificial intelligence integration and enterprise AI projects, where it is common to need AI agents and automation between services.
Environment management is native: multiple env files allow you to quickly switch between local, staging, and production configuration. Running tests in CI is straightforward, which fits with GitHub Actions, GitLab CI, or any continuous integration system pipelines. For clients requiring enhanced cybersecurity, our practices allow keeping secrets out of the code and applying access controls and auditing in repositories.
Key advantages: native version control, project integration, CI/CD ready, zero learning curve if you already know HTTP, and fast execution from the terminal. Additionally, this approach is protocol-agnostic: files with .http, .ws, .grpc, or .graphql extensions can coexist in the same workspace, using the same variable system and reuse. At Q2BSTUDIO we plan to add support for AI agents, Power BI, and other business intelligence tools to extract data and generate automated reports.
Getting started is direct and portable. The flow allows you to initialize a dock, create request files, add variables in env files, and run requests locally or in pipelines. This works for both basic API tests and complex scenarios like authentication, file uploads, or OAuth flows. In our software development and custom application projects, we use similar patterns to maintain traceability and facilitate secure deployments on AWS and Azure cloud services.
Typical use cases: functional testing of REST endpoints, automation of health checks in staging environments, microservice integration in pipelines, validation of authentication flows, and light load testing. For clients interested in artificial intelligence, we can link these tests with monitoring and metric extraction processes that feed Power BI dashboards and business intelligence systems.
What Q2BSTUDIO brings: in addition to implementing tools and workflows based on raw HTTP, we offer complete custom software development services, artificial intelligence integration, secure cloud deployments, cybersecurity consulting, and creation of custom AI agents. If you need security for your APIs, positioning in cloud environments like AWS and Azure, or integrating artificial intelligence solutions for your business, our team has the experience to design and implement the right solution.
Planned next steps include support for WebSocket and gRPC, response validations and assertions, templates and data extraction, and a plugin system for custom functions. We are also exploring native integrations with Power BI and business intelligence tools to facilitate automated reports and dashboards.
If you want to try the approach with a real project, at Q2BSTUDIO we can do a demo tailored to your needs and show how to integrate these practices into your pipelines and your security strategy. Keywords that guide our offering and that we apply in every project include custom applications, custom software, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, enterprise AI, AI agents, and Power BI. Contact us to evaluate how to apply this method to your APIs and accelerate the development cycle with security and traceability.
Reference links and example material are available at https://github.com/marcomit/rq and we can collaborate to adapt these ideas to your specific requirements within development projects, artificial intelligence integration, and adoption of efficient cybersecurity practices.
