Get started with Cache-Pot in under 5 minutes

By the end of this guide you will have Cache-Pot running locally, a Redis client talking to it, a working vector search, and a semantic cache query — plus a live look at the built-in web dashboard. The whole walkthrough takes less than five minutes from a fresh machine.

Install Cache-Pot

Pick whichever method suits your environment. Both options produce the same running server.

go install github.com/subh05sus/cache-pot/cmd/cache-pot@latest

docker run -p 6379:6379 -p 8080:8080 ghcr.io/subh05sus/cache-pot:latest

The go install path requires Go 1.25 or newer and places the binary in $GOPATH/bin. The Docker image requires no local Go toolchain.

Start the server

If you installed via Go, launch the binary directly. You should see two startup lines confirming the RESP2 listener and the dashboard are both up.

cache-pot

cache-pot: listening on [::]:6379
cache-pot: dashboard on http://localhost:8080

Cache-Pot listens for Redis clients on :6379 and serves the management dashboard on :8080. Both ports are configurable — see the configuration reference for details.

Connect with redis-cli

Open a second terminal and connect with redis-cli. Any Redis client or library works in exactly the same way — this step just confirms the server is healthy.

redis-cli ping

PONG

Now set and retrieve a key:

redis-cli set hello world
# OK

redis-cli get hello
# "world"

Any Redis client library works with Cache-Pot out of the box. Here are quick examples for two popular ones.

const Redis = require('ioredis');
const client = new Redis({ host: 'localhost', port: 6379 });
await client.set('hello', 'world');
const val = await client.get('hello');
console.log(val); // world

import redis
r = redis.Redis(host='localhost', port=6379)
r.set('hello', 'world')
print(r.get('hello'))  # b'world'

Try the AI features

Vector search stores your embeddings directly — pass the float values and Cache-Pot handles the index. No API key is needed for vector search itself.

# Insert two vectors into the "embeddings" collection
redis-cli VSET embeddings doc1 0.1 0.2 0.3 META "hello world"
redis-cli VSET embeddings doc2 0.9 0.8 0.7 META "goodbye world"

# Find the nearest neighbour to a query vector
redis-cli VSEARCH embeddings 0.1 0.2 0.3
# 1) "doc1"
# 2) "hello world"

Semantic caching stores model responses and returns them when a close-enough prompt arrives later, saving you a model call.

redis-cli SCACHE.SET "What is the capital of France?" "Paris"
redis-cli SCACHE.GET "whats the capital of france" THRESHOLD 0.9
# "Paris"

Semantic caching requires an embeddings provider. Set CACHEPOT_EMBED_URL, CACHEPOT_EMBED_MODEL, and (if needed) CACHEPOT_EMBED_KEY before starting the server. A free local Ollama instance works, and so does the OpenAI embeddings API. See Semantic Cache configuration for setup instructions.

Open the dashboard

Navigate to http://localhost:8080 in your browser. The dashboard is baked into the binary — no build step, no external assets.From the dashboard you can:

Watch live command throughput, memory usage, and key counts on the Overview panel
Browse, inspect, and edit every key in the Browser
Run commands interactively in the Workbench
Stream a live command log with the Profiler
Review slow commands in the SlowLog
Publish and subscribe to channels in the Pub/Sub panel
Analyse memory distribution by type and namespace in Analysis
List and kill active connections from Clients

What’s next

You now have a fully working Cache-Pot instance. Explore the areas below to go deeper.

Introduction — understand the full feature set and how Cache-Pot compares to Redis
Installation — persistent volumes, build-from-source, and production flags
Configuration — authentication, persistence (snapshots and AOF), and embedding providers
AI Agents & MCP — connect Claude or another MCP agent to Cache-Pot

Get Started

Core Concepts

Configuration

Guides

Dashboard

Get started with Cache-Pot in under 5 minutes

What’s next

​What’s next

What’s next