Best API Testing Tools 2025: Postman, Bruno, Karate & More

API testing tools changed more in the first seven months of 2025 than in the previous three years. Git-native clients went mainstream, AI assistants arrived inside the most popular API platform, contract testing grew into a standard practice for microservices, and traffic-capture tools that generate tests from real requests matured. At the same time the classics, code-first libraries like REST Assured and DSLs like Karate, kept their place, because API tests that live next to the code are still the most reliable kind.

This guide covers the API testing tools we think matter most in 2025, grouped by the job they do. For each one we explain where it fits, show a short example and give an honest list of trade-offs. It reflects what was available up to the end of July 2025. Our earlier roundup of free automated API testing tools is still a useful companion for budget-constrained teams.

How we grouped the tools

API testing isn’t one activity. We find it useful to separate five jobs:

Job	Question it answers	Typical tools
Exploration and manual testing	“What does this endpoint do right now?”	Postman, Bruno, Insomnia, Hoppscotch, HTTPie
Functional test automation	“Does the API behave correctly, every build?”	Karate, REST Assured, Supertest, Playwright API, Postman/Newman
Contract testing	“Will this change break a consumer?”	Pact, PactFlow, Specmatic-style spec tests
Mocking and virtualisation	“Can I test without the real dependency?”	WireMock, Mockoon, Microcks, Hoverfly, Prism
Traffic-based test generation	“Can real traffic become tests?”	Keploy, HyperTest

Most teams need one tool from each of the first three rows and at least one from the fourth. The fifth row is optional but increasingly attractive for legacy services with little test coverage.

How we evaluated

We looked at each tool against the criteria that decide whether an API testing tool survives its first year in a team:

• Where tests live. Can tests be versioned and reviewed alongside the code, or do they live in a separate cloud workspace?
• CI fit. Is there a first-class CLI, sensible exit codes and machine-readable reports such as JUnit XML?
• Assertion power. How easy is it to assert on deep JSON structures, schemas and business rules, not just status codes?
• Protocol coverage. REST is a given. What about GraphQL, gRPC, WebSockets and event-driven APIs?
• Data and environment handling. How are secrets, environments and test data managed without leaking credentials into files?
• Total cost. Licence, seats, infrastructure, and the time to learn and maintain.

No tool wins on every criterion, which is why the stacks at the end of this guide combine several.

Quick comparison

Tool	Type	Licence / model	Best for	Watch out for
Postman	API platform	Freemium, paid tiers	Collaboration, docs, monitors, AI Agent Mode	Cloud-first workspaces, pricing at scale
Bruno	Git-native client	Open source + paid editions	Collections as plain files in Git	Smaller ecosystem than Postman
Insomnia	API client	Open source core + paid	GraphQL, gRPC, design-first workflows	Account and storage choices need care
Hoppscotch	Web/desktop client	Open source, self-hostable	Lightweight, self-hosted teams	Fewer enterprise test features
Karate	Test DSL	Open source	Readable API tests, mocks and perf in one	Gherkin-like DSL isn’t for everyone
REST Assured	Java library	Open source	JVM teams, tests next to code	Java only
Supertest	Node library	Open source	Node/Express services	In-process testing focus
Pact / PactFlow	Contract testing	Open source / SaaS	Consumer-driven contracts	Needs buy-in from both sides
WireMock	Mocking	Open source + cloud	HTTP stubbing and fault injection	Stub sprawl without ownership
Microcks	Mocking + contract	Open source	Mocks from OpenAPI/AsyncAPI specs	Kubernetes-oriented setup
Keploy	Traffic capture	Open source + cloud	Tests and mocks from real traffic	Noisy fields need configuration
Testcontainers	Test infrastructure	Open source	Real dependencies in tests	Docker required in CI

Exploration and collaboration clients

Postman

Postman remains the default API platform for most organisations: collections, environments, test scripts, mock servers, monitors and documentation in one place. The big 2025 change came at POST/CON 25 on 4 June, when Postman announced Agent Mode, an AI assistant that can build requests, write test scripts, debug failures and work across collections. Postman also expanded its support for the Model Context Protocol, including generating MCP servers from APIs.

A typical post-response test in Postman:

pm.test('returns the created order', () => {
  pm.response.to.have.status(201);
  const body = pm.response.json();
  pm.expect(body).to.have.property('id');
  pm.expect(body.total).to.eql(pm.environment.get('expectedTotal'));
  pm.environment.set('orderId', body.id);
});

Run collections in CI with Newman or the Postman CLI. Pros: the broadest feature set, strong collaboration and onboarding, AI assistance. Cons: cloud-centric storage, costs that grow with team size, and collections that can drift away from the code they test.

Bruno

Bruno is the fastest-growing alternative, built on one idea: API collections should be plain-text files in your repository. Each request is a .bru file, so changes go through pull requests like code. Bruno v2, released in spring 2025, brought a significant update to the app. A request with tests looks like this:

meta {
  name: Create order
  type: http
  seq: 1
}

post {
  url: {{baseUrl}}/orders
  body: json
}

body:json {
  { "sku": "SKU-123", "qty": 2 }
}

tests {
  test("status is 201", function() {
    expect(res.getStatus()).to.equal(201);
  });
  test("has id", function() {
    expect(res.getBody().id).to.be.a("string");
  });
}

Run in CI with bru run --env ci. Pros: Git-native, offline-first, no forced cloud account, easy code review. Cons: fewer integrations and enterprise features than Postman, and teams need to agree on folder conventions.

Insomnia

Insomnia is strong for multi-protocol work, including REST, GraphQL, gRPC and WebSockets, and for design-first workflows with OpenAPI. Insomnia 11 shipped in March 2025. It supports local, Git and cloud storage options. Choose the storage mode deliberately, because it determines where your collections, and potentially secrets, live.

Hoppscotch

Hoppscotch is a lightweight, open-source client that runs in the browser or as a desktop app and can be fully self-hosted. Its 2025 releases improved the self-hosted desktop experience and added enterprise features such as SCIM provisioning. It’s a good fit for teams that want a shared client under their own control.

HTTPie and curl

For quick checks and scripted smoke tests, HTTPie and curl remain indispensable. A deploy smoke test needs nothing more:

http --check-status --timeout=5 GET https://api.example.com/health && echo OK

Functional API test automation

Karate

Karate combines API testing, mocks, performance testing (via Gatling) and UI automation in one framework, with a readable DSL. It’s especially good for teams where testers who aren’t full-time Java developers write API tests:

Feature: Orders API

  Background:
    * url baseUrl
    * header Authorization = 'Bearer ' + token

  Scenario: create and fetch an order
    Given path 'orders'
    And request { sku: 'SKU-123', qty: 2 }
    When method post
    Then status 201
    And match response == { id: '#string', sku: 'SKU-123', qty: 2, total: '#number' }
    * def orderId = response.id

    Given path 'orders', orderId
    When method get
    Then status 200
    And match response.id == orderId

Pros: powerful match assertions for JSON, built-in parallel runs, mocks and performance reuse. Cons: the DSL is its own language, and debugging complex logic can be awkward.

REST Assured

For JVM teams, REST Assured is still the standard, with fluent tests that live next to the service code and run with JUnit 5:

@Test
void createsOrder() {
    given()
        .baseUri(baseUrl)
        .contentType(ContentType.JSON)
        .body(Map.of("sku", "SKU-123", "qty", 2))
    .when()
        .post("/orders")
    .then()
        .statusCode(201)
        .body("id", notNullValue())
        .body("qty", equalTo(2));
}

Supertest and Playwright’s API testing

In Node.js, Supertest tests Express-style apps in-process, which makes it fast and simple. Playwright’s request fixture is increasingly popular for API tests too, especially when the same suite also drives the UI. You can create data through the API, then verify it in the browser:

test('order appears in account history', async ({ request, page }) => {
  const res = await request.post('/api/orders', { data: { sku: 'SKU-123', qty: 1 } });
  expect(res.status()).toBe(201);
  const { id } = await res.json();
  await page.goto('/account/orders');
  await expect(page.getByRole('row', { name: new RegExp(id) })).toBeVisible();
});

Contract testing

Pact and PactFlow

Pact is the leading consumer-driven contract testing tool. Consumers record the interactions they rely on, providers verify them, and a broker such as PactFlow tells you whether a given version is safe to deploy. A consumer test in JavaScript:

const { PactV3, MatchersV3: M } = require('@pact-foundation/pact');
const provider = new PactV3({ consumer: 'web-app', provider: 'orders-api' });

it('fetches an order', () => {
  provider
    .given('order 42 exists')
    .uponReceiving('a request for order 42')
    .withRequest({ method: 'GET', path: '/orders/42' })
    .willRespondWith({ status: 200, body: { id: M.string('42'), total: M.decimal(54.0) } });

  return provider.executeTest(async mock => {
    const order = await getOrder(mock.url, '42');
    expect(order.id).toBe('42');
  });
});

Contracts catch breaking changes before deployment, which is something end-to-end tests do slowly and unreliably. We explained why this matters for release safety in our shift-left and shift-right essay. Pros: fast, precise, independent deployability. Cons: both consumer and provider teams must adopt it, and provider states take effort.

Spec-based testing

Schema-driven tools that generate tests from an OpenAPI document complement Pact. Schemathesis, for example, uses property-based testing to find inputs that crash an API or violate its schema:

st run https://api.example.com/openapi.json --checks all

That’s an excellent way to find 500 errors and validation gaps that hand-written tests miss.

Mocking and service virtualisation

WireMock is the most widely used HTTP mocking tool, with stubbing, request verification and fault simulation:

{
  "request": { "method": "GET", "urlPath": "/payments/status" },
  "response": { "status": 200, "fixedDelayMilliseconds": 3000,
                "jsonBody": { "status": "pending" } }
}

Mockoon offers a friendly desktop app and CLI for local mocks. Microcks generates mocks and contract tests directly from OpenAPI, AsyncAPI and gRPC specifications. Hoverfly captures and simulates traffic, and Prism serves mocks from OpenAPI files. Pair any of them with Testcontainers to run mocks and real dependencies in disposable containers during tests.

Traffic-based test generation

Keploy records real API calls, along with the database and downstream calls they trigger, using eBPF. It then turns them into replayable test cases and mocks:

keploy record -c "npm start"      # exercise the app; Keploy captures tests + mocks
keploy test   -c "npm start" --delay 10

This is particularly valuable for legacy services with little coverage. You get a regression baseline in hours rather than weeks. The trade-off is noise: timestamps, IDs and other dynamic fields need to be configured as ignored, or the tests will be flaky. HyperTest takes a similar traffic-driven approach.

GraphQL, gRPC and event-driven APIs

REST still dominates, but most organisations now run at least one GraphQL or gRPC service, and many depend on message queues and event streams. Each needs a slightly different approach.

GraphQL. Because a single endpoint serves every query, status codes tell you very little: errors arrive in a 200 response body. Assert on the errors array explicitly, test query depth and complexity limits, and check field-level authorisation, which is a common source of data leaks. Postman, Insomnia, Bruno and Karate all handle GraphQL requests well, and schema-based tools can find queries the server fails to handle.

gRPC. Use clients that understand protobuf descriptors and server reflection. Insomnia and Postman both support gRPC requests. For automation, the language’s native gRPC test tooling or Karate’s gRPC support keeps tests close to the service. Pay special attention to deadline and cancellation behaviour, because these are where gRPC services most often misbehave under load.

Event-driven APIs. For Kafka, AMQP and similar systems, the contract is the message schema, not an endpoint. Microcks can mock and test AsyncAPI-described channels, and Testcontainers can run a real broker in integration tests. Test ordering, duplicate delivery and poison messages as well as the happy path.

Common API testing mistakes

Whatever tools you choose, the same mistakes cause most of the pain we see:

1. Asserting only on status codes. A 200 with the wrong total is still a bug. Assert on the business-relevant fields.
2. Sharing mutable test data. Tests that depend on “user 42” collide in parallel runs. Create the data each test needs through the API, and clean it up.
3. Ignoring negative paths. Invalid input, missing auth, expired tokens, rate limits and oversized payloads deserve as many tests as the happy path.
4. Mocking everything. Heavily mocked suites pass while integrations break. Balance mocks with contract tests and a small set of real integration tests.
5. Secrets in collections. API keys committed in exported collections or .bru files are a frequent leak. Use environment variables and secret managers.
6. No ownership of the spec. If the OpenAPI document drifts from reality, every spec-driven tool becomes noise. Validate the spec against the running service in CI.

AI in API testing, mid-2025

AI features are now common in API tools. They generate tests from OpenAPI specs, explain failures, suggest assertions and, with Postman’s Agent Mode, work across a whole collection. Our advice is the same as for UI testing in Agentic QA goes mainstream: let AI draft, and keep a human deciding what correct means. AI-generated API tests tend to assert on status codes and shapes. Business rules, such as whether the total is correct or whether this user is allowed to see this record, still need explicit, human-designed assertions.

There’s a second AI dimension: APIs are now consumed by agents through MCP. If your API will be exposed as MCP tools, test the tool descriptions, parameter schemas and error messages as carefully as the endpoints themselves, because an agent will act on them literally.

API tests also have a blind spot: they can’t tell you whether the product works for a person using the UI on top of those APIs. That’s where end-to-end suites and exploratory agents come in, using the site like a real user to find bugs the API layer can’t see.

Choosing a stack

Team profile	Suggested stack
Small product team, JS/TS	Bruno for exploration, Playwright or Supertest for automation, WireMock or Mockoon for mocks
JVM microservices	REST Assured + JUnit 5, Pact + PactFlow, WireMock, Testcontainers
Mixed-skill QA team	Postman or Karate for automation, Pact for critical contracts, Microcks for spec mocks
Legacy services, low coverage	Keploy to create a baseline, then hand-written tests for business rules
Regulated or air-gapped	Bruno or self-hosted Hoppscotch, Karate, WireMock, all running in your infrastructure

A pipeline that puts it together

Here’s how the layers fit in a typical CI pipeline:

# .github/workflows/api-quality.yml (excerpt)
jobs:
  api-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npm ci
      - name: Unit + in-process API tests
        run: npm test
      - name: Contract tests (consumer)
        run: npm run test:pact && npx pact-broker publish ./pacts --consumer-app-version $GITHUB_SHA
      - name: Spec fuzzing against ephemeral env
        run: st run "$EPHEMERAL_URL/openapi.json" --checks all
      - name: Can I deploy?
        run: npx pact-broker can-i-deploy --pacticipant web-app --version $GITHUB_SHA --to-environment production

What this means for your team

Keep API tests in the repository. Whether you use Bruno files, Karate features or REST Assured classes, tests that live next to the code get reviewed and maintained. Collections in someone’s personal workspace don’t.

Adopt contract testing for your most-changed interfaces. Start with one consumer-provider pair and a can-i-deploy gate. It’s the most effective way to stop breaking changes reaching production.

Add spec-based fuzzing. Running a schema-driven tool against your OpenAPI document once a day finds real bugs for almost no effort.

Use mocks deliberately. Mock third parties and slow dependencies, and keep contract tests honest about what the mocks claim. Own your stubs like code.

Treat AI as a drafting tool. Let Agent Mode or similar features generate a first pass, then add the business-rule assertions that matter.

Test for agents as consumers. If your API is exposed through MCP, add tests for tool schemas and error messages.

The best API testing tools in 2025 aren’t one product. They’re a layered stack where each tool does one job well. For performance testing of your APIs, see Best performance testing tools 2025, and for security, Best security testing tools 2025.

Further reading on CheckOps

• Top free automated API testing tools
• Shift-left meets shift-right
• Agentic QA goes mainstream