Continuous API validation for rock-solid speedyou can trust in production
Keploy automatically generates API tests by capturing real production traffic using eBPF kernel hooks. It records HTTP requests, database queries, and external API calls, then replays them as isolated test cases with all dependencies automatically mocked — achieving 90% test coverage in minutes without writing test code or modifying your application.
Sometimes, you just need to see it.
From Traffic to Tests in One Click
One AI-powered suite to generate verify and run API tests in seconds—no manual mocks needed.
Fully automated API testing
URL to Test Suite in Seconds
Paste your endpoint, spec, or cURL command and let Keploy build and execute your API tests automatically.
Enter Your Endpoint URL
Paste in the live API URL you want to test—Keploy will ping it and capture real requests and responses.
Drop in a cURL Snippet
Provide a sample curl command and Keploy will replay it to auto-generate corresponding test cases.
Upload Your OpenAPI Spec
Supply your OpenAPI (Swagger) definition to enrich assertions and ensure full schema coverage.
AI Mode
AI-Powered API Testing
Capture real traffic or upload your spec, then let our AI generate, run, and report on tests—so you catch regressions before they reach production.
Generate API tests now! →
AI search market size is growing rapidly in 2025
Users are rapidly shifting from traditional search engines to AI assistants, creating new visibility channels across major AI models in this growing market.
“As the underlying AI continues to improve at breathtaking speed, the set of human activities that can be handed off to agents will rapidly grow.”— Rob Toews, Forbes
Types of API Testing
There is no single type of API test. Each category targets a different failure mode, and a mature test suite combines several.
API Testing Approaches Compared
How traffic-based test generation stacks up against traditional GUI tools and code-based frameworks.
| Feature | Traffic-Based Keploy | Manual GUI Tools | Code-Based Frameworks |
|---|---|---|---|
| Test Generation | Automatic from traffic | Manual collection creation | Manual code authoring |
| Mock Generation | Auto-generated from deps | Manual mock server setup | Manual stubs required |
| Code Changes Required | None (eBPF kernel hooks) | None (external tool) | Yes (test code required) |
| Language Support | Any (language-agnostic) | Any (HTTP-level) | Language-specific only |
| CI/CD Integration | CLI + GitHub/GitLab native | CLI runner available | Build tool integration |
| Non-determinism Handling | AI noise detection + time-freeze | Manual variable extraction | Custom matchers needed |
| Protocol Support | HTTP, gRPC, GraphQL, DB | HTTP, GraphQL, WebSocket | HTTP (REST) primarily |
| Test Maintenance | Self-updating from traffic | Manual updates required | Manual updates required |
Key Takeaway
Traffic-based testing eliminates manual test authoring entirely by generating tests from real production traffic. For detailed head-to-head tool comparisons, see our comparison pages.
How to Automate API Testing
A step-by-step approach to automating API tests as your service count grows.
Inventory Your API Surface
Catalog every API endpoint your services expose. Export OpenAPI specs if available, or use traffic capture to discover endpoints automatically. The goal is a complete list of routes, methods, and expected schemas.
Choose Your Test Generation Strategy
Three approaches: manual authoring (maximum control, slow), spec-based generation (schema compliance only), or traffic-based generation (real-world coverage, zero manual effort).
Set Up Your Test Environment
Ensure mock servers or auto-generated mocks are version-matched to dependencies. Use containerized dependencies via Docker Compose. Seed databases with deterministic fixtures and reset state between runs.
Integrate into CI/CD
Run API tests on every pull request. Stage your pipeline: lint & build, unit tests, API functional tests with mocked dependencies, then integration tests against real dependencies.
Handle Non-determinism
Timestamps, UUIDs, and random tokens cause flaky tests. Use field-level matchers, time-freezing for deterministic clock behavior, and AI noise detection to auto-exclude varying fields.
Monitor and Maintain
Track endpoint coverage, flake rate, and execution time. Set alerts when coverage drops or flake rate exceeds a threshold. Periodically refresh traffic-captured tests as your API evolves.
Best Practices for API Testing
Battle-tested practices from high-performing engineering teams.
Test the Contract, Not the Implementation
Assert on status codes, response schemas, and required fields rather than internal implementation details. Tests should survive refactoring.
Use Realistic Test Data
Synthetic data misses edge cases — Unicode characters, long strings, null values. Traffic-captured tests inherently use real data.
Isolate Test Environments
Use database transactions that roll back, containerized dependencies, or auto-generated mocks to eliminate external state.
Validate Error Responses
Test 4xx and 5xx scenarios with the same rigor as 2xx. Most API bugs surface in error paths — leaked stack traces, wrong status codes.
Version Tests with API Code
Store tests in the same repository as the API. Review tests in the same PR as the API change for accurate bisection.
Parallelize Execution
API tests are I/O-bound — ideal for parallel execution across multiple workers. Ensure tests are stateless.
Shift-Left Security Testing
Run security scanners against your API in CI, not just in quarterly pen tests. Automate checks for the OWASP API Security Top 10.
Measure Endpoint-Level Coverage
Track what percentage of routes, HTTP methods, and documented error codes are exercised — not just line coverage.
See It in Action
Watch how Keploy captures traffic, generates mocks, and eliminates flaky tests — all without code changes.
FAQs
API testing validates that application programming interfaces work correctly, return expected responses, and handle errors properly. It's critical for microservices architectures where services communicate via APIs, ensuring reliability before production deployment.
Keploy captures real API traffic using eBPF kernel hooks, recording HTTP requests, responses, and all dependency interactions. These recordings are automatically converted into replayable test cases with mocked dependencies, eliminating manual test writing.
No. Keploy operates at the network layer using eBPF technology, capturing traffic at the Linux kernel level without instrumenting application code. This makes it ideal for testing legacy APIs and systems not designed for testability.
Keploy uses AI-powered noise detection to identify and filter non-deterministic fields like timestamps, UUIDs, and session tokens. This eliminates flaky test failures without manual assertion configuration.
Yes. Keploy provides native integration with GitHub Actions, GitLab CI, and Jenkins. Generated API tests run automatically on every commit, providing continuous regression detection with zero test maintenance.
Yes. Keploy's core platform is fully open source under the Apache 2.0 license with 17,000+ GitHub stars. You can self-host it for free or use the managed cloud platform for advanced features like team collaboration and enterprise support.
Yes. Keploy offers a free Playground tier with 30 test suites/month and 100 test runs/month, plus the open-source self-hosted version is completely free. Paid plans start at $19/user/month for teams that need faster generation and advanced features.
Yes. Keploy supports all major languages including Java, Go, Python, Node.js, and TypeScript. Because it captures traffic at the kernel level using eBPF, Keploy is fundamentally language-agnostic and works with any application that makes network calls.
Unit testing validates individual functions or methods in isolation, typically using mocks for external dependencies. API testing validates the full request-response cycle at the HTTP layer, exercising routing, middleware, serialization, database queries, and downstream service calls together.
Yes. Because Keploy operates at the kernel network layer via eBPF, it captures any protocol that uses TCP/IP sockets — REST, GraphQL, gRPC (HTTP/2), and database wire protocols like PostgreSQL, MongoDB, and Redis.
Synthetic tests reflect assumptions about how code should behave. Traffic-based tests capture how the API is actually used in production — including real payloads, authentication patterns, error scenarios, and dependency interactions — producing tests that are guaranteed to be realistic and relevant.
Join the Keploy community
Follow updates, ask questions, share feedback, and ship faster with other Keploy builders.
