> ## Documentation Index
> Fetch the complete documentation index at: https://cache-pot.thatdevguy.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Cache-Pot: AI-native in-memory cache for modern apps

> Cache-Pot is a single-binary, Redis-compatible cache with vector search, semantic caching, and a native MCP server for AI agents.

Cache-Pot is an in-memory data store built in the Redis mould and reworked for the way AI applications actually use a cache. It speaks the Redis wire protocol out of the box, so every client and library you already rely on keeps working without modification — while also giving you vector search, semantic response caching, and a native MCP endpoint for AI agents, all packed into a single self-contained binary.

## Where to begin

<CardGroup cols={2}>
  <Card title="Introduction" icon="book-open" href="/introduction">
    Understand what Cache-Pot is, how it differs from Redis, and when to reach for it.
  </Card>

  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Install Cache-Pot, connect a Redis client, and run your first vector search in under 5 minutes.
  </Card>

  <Card title="AI Agents & MCP" icon="robot" href="/guides/ai-agents">
    Wire Cache-Pot into Claude or any MCP-compatible agent as a first-class tool.
  </Card>

  <Card title="Vector Commands" icon="database" href="/commands/vectors">
    Full reference for VSET, VSEARCH, VDEL, VCARD, and VDIM.
  </Card>
</CardGroup>

## Get up and running in three steps

<Steps>
  <Step title="Install Cache-Pot">
    Grab the binary with a single `go install` command or pull the Docker image — no external dependencies required.

    ```bash theme={null}
    go install github.com/subh05sus/cache-pot/cmd/cache-pot@latest
    ```
  </Step>

  <Step title="Start the server">
    Run the binary. Cache-Pot listens for Redis clients on `:6379` and opens a web dashboard on `:8080`.

    ```bash theme={null}
    cache-pot
    # cache-pot: listening on [::]:6379
    # cache-pot: dashboard on http://localhost:8080
    ```
  </Step>

  <Step title="Connect and use it">
    Point any Redis client or `redis-cli` at `localhost:6379` and start issuing commands. Switch to AI features — semantic caching, vector search, agent memory — whenever you need them.

    ```bash theme={null}
    redis-cli ping
    # PONG
    redis-cli set hello world
    # OK
    redis-cli get hello
    # "world"
    ```
  </Step>
</Steps>

## Three capabilities in one binary

<CardGroup cols={2}>
  <Card title="Redis-compatible" icon="database">
    Cache-Pot speaks RESP2, the same wire protocol your Redis clients already use. Strings, hashes, lists, sets, sorted sets, pub/sub, transactions, TTLs — all the everyday commands work without changing a line of application code.
  </Card>

  <Card title="AI-native" icon="bolt">
    Vector search and semantic caching ship built-in. Store and query high-dimensional embeddings with `VSET`/`VSEARCH`, or cache model responses by meaning with `SCACHE.SET`/`SCACHE.GET` — no extra modules or glue code needed.
  </Card>

  <Card title="MCP endpoint" icon="robot">
    Cache-Pot includes a native Model Context Protocol server. Connect Claude or any MCP-compatible agent directly, giving it read, write, search, and memory tools with zero adapter setup.
  </Card>

  <Card title="Single binary" icon="rocket">
    The entire stack — cache, vector store, semantic cache, MCP bridge, and web dashboard — lives in one self-contained binary. No external services, no database, no configuration file required to get started.
  </Card>
</CardGroup>