Do I need Dragonfly running to replay?

No. The recorded responses are served from the test set, so a replay run needs no Dragonfly instance at all. You need it once, during the recording pass.

Is my Redis client still exercised?

Yes. Keploy replays at the protocol, so the client parses, converts and decodes exactly as it does in production. That is the difference from a stubbed client, which skips all of it.

How do I refresh the recordings?

Record again. One command re-captures every Dragonfly interaction, so a schema or response change surfaces as a diff instead of a stale fixture that still passes.

Does the multi-threaded engine matter for tests?

Not for replay. Whatever Dragonfly returned under whatever concurrency is what gets recorded, so the test asserts on real observed behaviour.

Does Keploy replace Testcontainers?

Not where you need the real engine, and that is worth being clear about. Use Testcontainers when a test genuinely has to exercise Dragonfly itself. Keploy covers the much larger set of tests that only need the responses.

Keploy as a Dragonfly testing framework

Keploy records the RESP protocol exchanges your service has with Dragonfly, then replays them byte for byte in tests. Your Redis client runs unchanged and does its real work on every run, but CI needs no Dragonfly instance, no schema and no seed data.

Generate Dragonfly tests free

keploy record -c "<your app start command>"

18.5K+

1.2M+300M+mocks createdSlack

What Keploy gives a Dragonfly team

Every Dragonfly call your service makes

Keploy records RESP exchanges with Dragonfly, so a service using it as a cache can be tested with nothing running and no cache warm-up step.

RESP protocol captured
Hits and misses both kept
Multi-threaded behaviour as observed
No Dragonfly in CI

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The problem

Why Dragonfly integration tests slow teams down

The friction is rarely the assertions. It is everything around them — spinning up dependencies, keeping mocks honest, and repairing tests after every refactor.

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Three ways to do it

Testing Dragonfly: by hand, with Testcontainers, or with Keploy

Testcontainers gives a test a real Dragonfly process at the cost of running one. A hand-written stub is cheap but never exercises RESP. Keploy replays the recorded exchange with nothing running.

By hand

Testcontainers

With Keploy

Select any row for the full comparison, with code.

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Same coverage, three costs

What you write for Dragonfly vs what Keploy records

All three produce the same assertion. Only the third still passes after the next refactor without anyone editing it.

By hand2–4 hours

order_repository_test.py

hand-written

from unittest.mock import MagicMock

# A stub Dragonfly client: every reply is typed out here.

def test_reads_an_order():

client = MagicMock()

client.fetch.return_value = {

"id": "o-1",

"total": 4200,

"status": "NEW",

}

order = OrderRepository(client).find("o-1")

assert order.total == 4200

# Nothing here exercised the driver, so a type or encoding

# change in the real reply cannot fail this test.

A hand-written stub of the Dragonfly client. The driver never runs.

Testcontainers1–2 hours

order_repository_container_test.py

tool-assisted

import pytest

from testcontainers.core.container import DockerContainer

@pytest.fixture(scope="session")

def client():

# Boots a real Dragonfly per suite. Needs a Docker daemon

# available wherever the tests run, including CI.

with DockerContainer("dragonfly:latest") as c:

c.start()

yield connect(c.get_container_host_ip(), c.get_exposed_port(0))

def test_reads_an_order(client):

# Schema and seed data are still written by hand.

seed(client, id="o-1", total=4200, status="NEW")

order = OrderRepository(client).find("o-1")

assert order.total == 4200

Testcontainers runs the real Dragonfly. Fidelity is genuine; the job pays for it.

With Keploy~5 minutes

keploy/test-set-0/test-1.yaml

auto-generated

version: api.keploy.io/v1beta1

kind: Http

name: test-1

spec:

req:

method: POST

url: /orders/o-1/confirm

resp:

status_code: 200

body: '{"id":"o-1","status":"CONFIRMED","total":4200}'

assertions:

noise:

- body.confirmedAt

---

kind: Redis

spec:

command: GET order:o-1

reply: '{"id":"o-1","total":4200}'

---

kind: Http

spec:

request:

url: https://api.payments.example.com/v1/payment_intents/tok_123/confirm

response:

body: '{"status":"AUTHORIZED"}'

Recorded from one real request. Nothing here was typed by hand.

Times are estimates for authoring one endpoint’s coverage from scratch, not measurements.

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Keploy vs the alternatives

Dragonfly testing tools, compared

The options a team on caches and search stores actually reaches for, and where each one genuinely wins. Select a row for the full comparison.

Keploy

Testcontainers

fakeredis

miniredis

redis-mock

Postman

SmartBear ReadyAPI

Best in classStrongPartialNot covered

Assessments reflect each tool’s documented behaviour, not benchmark measurements.

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How it works

Record your Dragonfly app once, replay it forever

Keploy sits below your Dragonfly process at the network layer. It watches the calls your app already makes, then serves them back on replay so tests run with no dependencies attached.

Your Dragonfly appGET/api/v1/orders/{id}200

RecordingKeploy proxyeBPF · userspace

RedisRESP beneath the cache client

MemcachedThe text protocol beneath the client

Outbound HTTPEvery outbound HTTP call

PostgreSQLThe store being cached in front of

+2 more Dragonfly dependencies

An example shape of a captured call. Your own endpoints and dependencies come from your real traffic, so nothing here has to be written by hand.

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Mock coverage

What Keploy mocks for Dragonfly, with zero config

6 of the 6 dependencies a typical Dragonfly service talks to are stubbed from the recording itself — no mock classes, no fixture files, no containers in CI.

Auto-mocked
RedisView page
RESP beneath the cache client
Auto-mocked
MemcachedView page
The text protocol beneath the client
Outbound HTTP
Auto-mocked
Outbound HTTP
Every outbound HTTP call
Auto-mocked
PostgreSQLView page
The store being cached in front of
Auto-mocked
MongoDBView page
Wire protocol, where documents are cached
Auto-mocked
ElasticsearchView page
Search responses over HTTP

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Quick start

Your first Dragonfly test suite in under five minutes

Every command below runs against your existing Dragonfly service. Nothing in your source tree changes.

1
1Install the Keploy CLI
A single binary. It needs a Linux kernel with eBPF support, or Docker on macOS and Windows — and it adds nothing to your project's dependencies.
curl -sSL https://keploy.io/install.sh | bash
2
2Record your service
Pass the command you already use to start the app. Keploy runs it and watches every socket it opens.
keploy record -c "<your app start command>"
3
3Exercise the paths you care about
Use curl, your frontend, or an existing smoke script. Every request becomes a test case with its Dragonfly calls captured alongside it.
curl -X POST localhost:8080/orders/o-1/confirm
curl localhost:8080/orders/o-1
4
4Replay in CI
Replay serves the recorded dependency responses, so the job needs no service containers and no Docker daemon.
keploy test -c "<your app start command>" --delay 10