Apidog CLI vs Keploy: Record-and-Replay vs Designed API Tests

If you are comparing Apidog CLI vs Keploy, start with the category difference: both can run API tests, but they create and execute those tests in very different ways.

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Keploy records a running service and auto-generates tests plus dependency mocks from real traffic using eBPF. It requires no SDK, no code changes, and works across languages because capture happens at the network layer. Apidog CLI runs test scenarios that you author, or generate from an API spec, inside a broader API design, mock, documentation, and testing platform.

That difference should drive your choice:

• Use Keploy when you want to capture how an existing service behaves today.
• Use Apidog CLI when you want a maintainable test suite that your team designs, reviews, runs in CI, and evolves with the API.

The core difference

Keploy watches your running application. You start your app with keploy record, send real requests, and Keploy captures the API calls plus downstream dependencies such as database queries, network calls, and streaming events through eBPF. It then converts that captured traffic into replayable test cases and mocks.

Apidog works from the opposite direction. You design endpoints, schemas, and test scenarios, or generate test cases from an OpenAPI schema, then run those scenarios from the terminal with apidog run.

In short:

• Keploy records reality.
• Apidog expresses intent.

Neither approach is universally better. They solve different problems.

How tests get created

Keploy: create tests from observed behavior

Install Keploy:

curl --silent -O -L https://keploy.io/install.sh && source install.sh

Record traffic against your real app:

keploy record -c "CMD_TO_RUN_APP"

Replay the generated tests:

keploy test -c "CMD_TO_RUN_APP" --delay 10

During recording, Keploy turns real interactions into test cases and dependency calls into mocks. That includes database, network, and streaming dependencies captured at the eBPF layer.

This is useful when you have:

• An existing service with limited test coverage
• Complex downstream dependencies
• A need to generate regression coverage quickly
• No desire to add SDKs or change application code

Keploy also supports AI test generation from OpenAPI, Postman, cURL, or a live endpoint, with cleanup support.

Apidog CLI: run authored or spec-generated scenarios

With Apidog, tests start from API design. You define endpoints and schemas in the platform, then build test scenarios with:

• Request steps
• Assertions
• Variables
• Environment values
• Data passed between requests
• Mock servers where needed

Run a scenario from the terminal:

apidog run --access-token <TOKEN> -t <SCENARIO_ID> -e <ENV_ID>

This works well when your team wants test cases to be explicit, reviewable, and maintained alongside the API lifecycle.

For more CLI details, see the Apidog CLI complete guide.

Apidog CLI vs Keploy: feature comparison

Dimension	Keploy	Apidog CLI
Core approach	Record real traffic and replay it deterministically	Run authored or AI/spec-generated test scenarios
How tests are created	Captured from live API calls and dependencies	Designed in-platform or generated from a spec
Code changes required	None; eBPF capture, no SDK	None to your app; you author scenarios
Language-agnostic	Yes, capture happens at the eBPF network layer	Yes, runs against any HTTP API
Dependency mocking	Auto-generated from captured traffic, including DB, network, and streams	Designed mock servers that you configure
Data-driven testing	Derived from recorded variations	Built in with -d using CSV or JSON
Reporters	Replay test results	CLI, HTML, JSON, plus cloud reports with --upload-report
OS constraints	Linux-oriented; elevated privileges are often required for eBPF	Runs where the CLI runs: macOS, Linux, Windows, CI
Platform breadth	Focused testing and test-generation tool	Full lifecycle: design, debug, mock, document, test
Open source	Yes, Apache-2.0	Free tier; commercial platform

Dependency mocking: the main dividing line

Dependency mocking is where the tools differ most.

Keploy’s strength is automatic dependency mocking. Because it captures database queries and network events alongside API calls, replay tests can run without a live database or third-party service.

That makes Keploy useful when you want to:

1. Run your existing app.
2. Send real traffic to it.
3. Capture API behavior and dependencies.
4. Replay the same behavior later as regression tests.

Apidog uses designed mocks instead. You configure mock servers and define the responses you expect. It does not capture production database calls through eBPF or turn runtime traffic into mocks.

That makes Apidog useful when you want to:

1. Model expected API behavior.
2. Mock endpoints before implementation exists.
3. Keep mock behavior aligned with API design.
4. Share designed tests and mocks across the team.

If your goal is to freeze how a live service talks to its database, Keploy is the better fit. If your goal is to model intended API behavior and maintain it as part of the API lifecycle, Apidog fits better.

For related tooling, see this guide to contract testing and mocking.

To be precise: Apidog does not capture live traffic via eBPF and does not auto-generate tests by recording production calls plus dependency mocks. That record-and-replay workflow is Keploy’s distinct capability.

Data-driven runs and reporting

Once you have authored scenarios, Apidog CLI behaves like a CI-friendly API test runner.

Run the same scenario against many input rows:

apidog run -t <SCENARIO_ID> -e <ENV_ID> -d ./users.csv -r html,cli

Useful flags:

-d ./users.csv        # Use CSV or JSON test data
-e <ENV_ID>           # Select the target environment
-r html,cli,json      # Choose reporters
--upload-report       # Upload the report to Apidog cloud

Example CI-style command:

apidog run \
  --access-token "$APIDOG_ACCESS_TOKEN" \
  -t "$SCENARIO_ID" \
  -e "$ENV_ID" \
  -d ./test-data/users.csv \
  -r cli,json,html \
  --upload-report

This is the workflow to use when you need:

• Repeatable test execution
• Data-driven coverage
• CLI logs for pipeline output
• HTML reports for humans
• JSON reports for machines
• Cloud reports for sharing

For implementation details, see:

• Apidog CLI data-driven testing
• Apidog CLI test reports
• Apidog CLI CI/CD setup

Keploy reports whether the captured replay suite still passes against the current code. That is excellent for detecting regressions in existing behavior. It answers a different question than, “Do my designed assertions pass across 200 input rows?”

OS and environment constraints

Keploy’s eBPF capture usually means Linux and elevated privileges. That is the tradeoff for code-less, language-agnostic network capture.

This is often fine in Linux-based CI, but you should check your runtime constraints before adopting it.

Apidog CLI is a portable runner. It sends HTTP requests instead of instrumenting the kernel, so it can run on:

• macOS
• Linux
• Windows
• Standard CI runners

There is also a test-quality consideration.

Keploy captures what happened during recording. That can include noisy data, one-off states, and accidental behavior. You should review and curate generated tests before relying on them as a long-term baseline.

Apidog scenarios start from intent. They usually require more upfront authoring, but the resulting tests are explicit and easier for teams to review.

Platform breadth

Keploy is focused on test generation and replay. It is not trying to be your API design workspace, documentation system, or mock-server platform.

Apidog is broader. The CLI is one part of a platform that also supports:

• API design
• API debugging
• Mock servers
• Test scenarios
• Auto-generated documentation
• OpenAPI import
• Team collaboration

Use Apidog when you want the same platform to define, mock, document, and test an API.

Use Keploy when you mainly need to capture and replay the behavior of an existing service.

For a broader view, see this comparison of API test automation tools.

Which one should you pick?

Pick Keploy when:

• You have a running service with limited test coverage.
• You want quick regression coverage from real traffic.
• You need automatic dependency mocks.
• Your service has database or network dependencies that are hard to mock manually.
• Your environment supports Linux and the required eBPF privileges.
• You are comfortable reviewing and cleaning up generated tests.

Pick Apidog CLI when:

• You want designed, maintainable API tests.
• Your team owns API design, mocks, docs, and tests together.
• You need data-driven runs with CSV or JSON.
• You want CLI, HTML, JSON, or cloud reports.
• You need an easy fit for standard CI runners.
• You want tests based on intended behavior, not only recorded behavior.

A practical split is also possible: use Keploy to bootstrap regression coverage for legacy services, then use Apidog to design and maintain tests for APIs your team is actively building.

If you want the designed-test workflow, Apidog has a free tier, and you can download Apidog to try it.

Additional references:

• What is Keploy
• Best Keploy alternative
• Apidog CLI vs Keploy
• Migrate from Keploy to Apidog CLI
• Keploy documentation
• Keploy GitHub repository
• eBPF project site

FAQ

Does Apidog record live traffic like Keploy?

No. Apidog does not capture live traffic via eBPF and does not auto-generate tests from production calls. You author test scenarios or generate them from an API spec, then run them with the CLI.

Recording runtime behavior plus dependency mocks is Keploy’s distinct capability.

Is Keploy or Apidog better for services with many database dependencies?

Use Keploy if you want to capture and replay those dependencies automatically. Its eBPF capture can record database queries and mock them so replays run without a live database.

Use Apidog if you want designed mock servers that model intended behavior.

Do I need to change my code to use either tool?

No code changes are required for either tool.

Keploy instruments traffic at the eBPF network layer and does not require an SDK. Apidog sends HTTP requests to your API and does not modify your application code.

Can both generate tests from an OpenAPI spec?

Yes. This is the main overlap.

Keploy can generate validated suites from OpenAPI, Postman, cURL, or a live endpoint. Apidog can generate AI test cases from your schema and endpoints inside the platform.

The difference is that only Keploy also generates tests from recorded runtime behavior.

Which one is easier to run across operating systems?

Apidog CLI is easier across standard environments because it runs as a portable CLI on macOS, Linux, Windows, and CI runners.

Keploy’s eBPF capture is more Linux-oriented and may require elevated privileges.

Short version

Choose Keploy if you need to snapshot an existing service with minimal effort and automatic dependency mocks.

Choose Apidog CLI if you need designed, maintainable API tests inside a platform that also handles API design, mocks, and documentation.

The decision is not “Apidog vs Keploy” in the abstract. It is whether your workflow is about capturing current behavior or designing intended behavior.