# API Hero: Beyond API Requests — Building Executable API Workflows > Source: > Published: 2026-09-14 09:48:17+00:00 How API Hero connects API collections, dependencies, authentication, variables, testing, automation, and AI-assisted API workflows. Most API clients start with a simple problem: Send an HTTP request and show me the response. That problem is largely solved. The more interesting problem begins when you have dozens or hundreds of requests that depend on each other. A real API workflow rarely looks like: GET /products It looks more like: Login ↓ Get authentication token ↓ Get products ↓ Create product ↓ Extract product ID ↓ Update product ↓ Delete product At that point, API testing is no longer just about sending requests. It becomes a workflow problem. That's the problem I'm exploring while building API Hero. 6 Why API Hero focuses on workflows Individual API requests are easy to understand. The relationships between requests are where things become complicated. Consider: POST /auth/login GET /products POST /products GET /products/{id} PUT /products/{id} DELETE /products/{id} These aren't necessarily six independent operations. They might form a workflow: POST /auth/login │ └── accessToken │ ├── GET /products │ └── POST /products │ └── productId │ ├── GET /products/{id} ├── PUT /products/{id} └── DELETE /products/{id} The API itself already contains these relationships. API Hero is being designed to understand and work with those relationships. API Hero Collections are more than folders The starting point is the collection. For example: FakeStoreAPI ├── Auth │ └── Login User │ ├── Products │ ├── Get All Products │ ├── Get Product By Id │ ├── Add Product │ └── Update Product │ ├── Carts │ ├── Get All Carts │ ├── Get Cart By Id │ ├── Add Cart │ ├── Update Cart │ └── Delete Cart │ └── Users ├── Get All Users ├── Get User By Id ├── Add User └── Update User A collection provides context. Once requests are grouped together, we can start asking: Which requests require authentication? Which requests produce variables? Which requests consume those variables? Which endpoints are related? Which requests can run together? Which requests should become a scenario? Which tests should be generated? That makes the collection a useful foundation for API understanding. Variables create data flow Consider a login response: `{` "token": "abc123..." } Another request may use: Authorization: Bearer {{token}} There is now a dependency: Login ↓ Response ↓ Extract token ↓ Store variable ↓ Use token The same applies to resource IDs. Create Product ↓ Response ↓ productId = 123 ↓ Update Product ↓ Delete Product Variables therefore aren't simply placeholders. They can represent data moving through an API workflow. API Hero uses this concept when building executable scenarios and preparing collections. Authentication is part of the workflow Authentication creates another layer of dependency. An API collection might contain: Public endpoints + Authentication endpoint + Protected endpoints POST /auth/login ↓ token ↓ Authenticated context ↓ GET /products POST /products PUT /products/{id} DELETE /products/{id} The important question isn't only: Does this endpoint require authentication? It's also: Where does the authentication come from, and how does it get into the request? That's why API Hero treats authentication, variables, and dependencies as connected parts of the workflow. API Hero Scenarios Once dependencies are understood, we can create scenarios. A scenario represents an executable workflow rather than simply a list of requests. Product Lifecycle The important part is the relationship between the steps. Step 5 needs information produced by step 4. Step 7 needs the ID produced earlier. The scenario therefore represents the actual execution flow of the API. This is one of the areas where API Hero goes beyond treating API requests as isolated objects. API Hero and API testing Once requests are connected, testing becomes part of the same workflow. A request can contain assertions such as: Status = 200 Response time < threshold Required property exists Property has expected type Response contains expected value Negative testing can cover: Invalid authentication Missing required parameter Invalid resource ID Malformed request Unauthorized operation The execution flow becomes: Request ↓ Execute ↓ Response ↓ Assertions ↓ Pass / Fail For a scenario: Request ↓ Extract data ↓ Pass data forward ↓ Next request ↓ Assertions ↓ Scenario result This allows API testing to happen at both the request level and the workflow level. Where AI fits into API Hero AI is useful here, but I don't want AI to simply become a button that generates another HTTP request. Generate a GET request for products. That's useful. But a more interesting question is: Look at this API collection and help me understand how it can actually be executed. That requires understanding: authentication variables dependencies related endpoints potential scenarios test cases collection readiness The conceptual workflow becomes: API Collection ↓ Analyze ↓ Understand ↓ Prepare ↓ Generate Tests / Scenarios AI becomes an assistant for understanding and preparing an API. API Hero Prepare with AI This idea is reflected in the Prepare with AI workflow. Instead of exposing every technical operation as a separate action, API Hero is simplifying the experience around three user goals: Generate Tests Create positive and negative test cases. Build Scenario Create an executable workflow with dependencies. Prepare Collection Configure variables, authentication, dependencies, and collection readiness. Internally, preparation can involve several steps: Catalog API ↓ Understand structure ↓ Detect authentication ↓ Extract variables ↓ Detect dependencies ↓ Suggest scenarios ↓ Generate tests The user doesn't need to manually orchestrate every step. The complexity can stay underneath the interface. OpenAPI as a starting point Many APIs already have a machine-readable description through OpenAPI. An OpenAPI specification can describe: paths HTTP methods parameters request bodies response schemas authentication schemes That makes OpenAPI a natural starting point for API Hero. Instead of importing a specification and simply producing: GET /products POST /products GET /products/{id} the information can become the foundation for: OpenAPI ↓ Endpoints ↓ Schemas ↓ Authentication ↓ Variables ↓ Dependencies ↓ Tests ↓ Scenarios OpenAPI gives us structured information. API Hero's job is to turn that information into something developers can actually work with. Why API Hero is a desktop application API development rarely happens in isolation. A typical development environment might contain: VS Code Terminal Git Browser Database Docker API Client Documentation API Hero is being built as a desktop application to fit naturally into that environment. The application brings together: API requests Collections Environments Authentication Assertions History Collection runners OpenAPI import Scenarios Dependencies Git-oriented workflows AI-assisted API preparation The goal isn't to make every feature independent. The goal is to connect them. Building developer tools means fixing the small things too One thing I've learned while building API Hero is that technical functionality is only half of the product. A developer tool can have sophisticated architecture and still be frustrating if the UI gets in the way. A generated test list needs to scroll. A long AI preparation workflow needs to remain accessible. A navigation sidebar shouldn't consume unnecessary workspace. These aren't glamorous features, but they're important when a tool is used every day. That's why API Hero 0.1.9 also focuses on desktop UX improvements: better modal scrolling improved handling of long content simplified AI tools collapsible navigation collection workflow improvements The objective is simple: Make the workflow easier to use without hiding its power. The API Hero architecture I'm working toward The broader concept looks something like this: ``` API │ ▼ Collection │ ┌──────────┼──────────┐ ▼ ▼ ▼ ``` Variables Auth Endpoints │ │ │ └──────────┼──────────┘ ▼ Dependencies │ ▼ Scenarios │ ▼ Tests │ ▼ Automation AI can operate across these layers: ``` ` AI │ ┌──────────┼──────────┐ ▼ ▼ ▼ Analyze Prepare Generate │ │ │ └──────────┼──────────┘ ▼ API Workflow` ``` That is the direction I'm taking with API Hero. What I've learned building API Hero The biggest lesson is that API testing is a workflow problem. Sending an HTTP request is relatively straightforward. The complexity appears when requests depend on one another. You need to understand: where authentication comes from where variables come from which request produces an ID which request consumes it what happens when something fails which assertions should run what order requests should execute in which requests belong together how the workflow can be automated Once you start thinking about those relationships, an API collection starts looking less like a folder of requests and more like a graph of executable operations. That's the problem I'm exploring with API Hero. What's next for API Hero? API Hero is still actively evolving. Some of the areas I'm working on include: better dependency detection more reliable variable extraction stronger assertions richer scenario generation API workflow automation improved OpenAPI handling authentication workflows Git integration AI-assisted API analysis The goal isn't to create the biggest possible feature list. It's to make this workflow increasingly reliable: Import API ↓ Understand API ↓ Configure API ↓ Test API ↓ Connect dependencies ↓ Build scenarios ↓ Automate workflows Try API Hero If you're working with APIs, testing, automation, or developer tooling, I'd be interested in hearing what parts of your workflow are still unnecessarily difficult. API Hero: [https://apihero.in/](https://apihero.in/) Microsoft Store: API Hero on Microsoft Store What part of your API workflow becomes painful once you move beyond a handful of independent requests