Getting started

Quickstart

From zero to a semantic cache hit in about five minutes. You need Docker, everything else, including the CLI, ships inside the image. Community edition is free and needs no license.

1. Pull and run#

shell

docker pull crowkis/crowkis:latest

docker run -d --name crowkis \
  -p 127.0.0.1:6379:6379 \
  -p 127.0.0.1:6380:6380 \
  -p 127.0.0.1:6381:6381 \
  -v crowkis-data:/data \
  crowkis/crowkis:latest

Endpoint	Port	What it is
RESP3 (Redis protocol)	127.0.0.1:6379	crowkis cli and any Redis client
Dashboard + REST	127.0.0.1:6380	live verdict feed, management API, /health
gRPC (h2c)	127.0.0.1:6381	protobuf surface

No environment variables are required to boot, defaults are sensible and the data volume persists your cache across restarts. Hardening and every knob: the Docker guide.

2. Talk to it#

The image ships the interactive REPL, crowkis cli, which connects like redis-cli does. Standard Redis commands work, and the C* family adds the semantic layer:

docker exec -it crowkis crowkis cli

PING
CSET "Explain vector caches" "Vector caches store embeddings of past queries so similar questions can reuse answers." EX 86400 MODEL gpt-4o TENANT demo
CGET "Explain vector caches" TENANT demo
CGET "what are vector caches?" TENANT demo
CSIM "France capital city" K 5
CVECCOUNT

The fourth line is the point: a paraphrase of the stored question still hits, because Crowkis matches meaning and structure, not bytes. Already a Redis shop? Your existing client connects to port 6379 unmodified.

3. Watch it decide#

Open the dashboard and you'll see every verdict streaming live, hits by type, misses, safety blocks, latency, and an estimate of what the cache saved you:

shell

open http://127.0.0.1:6380

4. Wire it into your app#

The SDKs wrap the whole cache-or-compute loop in one call. If the answer is cached and safe to reuse, you never touch the model:

pip install crowkis

from crowkis import CrowkisClient

cache = CrowkisClient(host="127.0.0.1", port=6379, tenant="demo", model="gpt-4o")

answer = cache.get_or_compute(
    "Explain vector caches",
    lambda query: call_llm(query),
    ttl=3600,
)

Node shop? npm install crowkis and getOrCompute, same pattern. Using Claude Code or agents? See MCP for AI apps.