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.
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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
For example:
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.
For example:
Product Lifecycle
- Login
- Extract authentication token
- Get products
- Create product
- Extract product ID
- Get product
- Update product
- Delete product
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.
For example:
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.
For example:
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/
Microsoft Store: API Hero on Microsoft Store
What part of your API workflow becomes painful once you move beyond a handful of independent requests
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