> ## 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: Redis-compatible cache built for AI applications
> Learn what Cache-Pot is, how it compares to Redis, and why it's purpose-built for AI apps that need semantic caching and vector search.
Cache-Pot is an in-memory data store that starts where Redis left off and keeps going in the direction AI applications actually need. It speaks the Redis wire protocol (RESP2) on the network, so every Redis client library and the application code around it works without modification. On top of that familiar foundation it adds a built-in vector store, a semantic cache that deduplicates model calls by meaning, persistent agent memory, and a native Model Context Protocol endpoint — all bundled into a single binary you can download and run in seconds.
## What makes Cache-Pot different
### 1. Redis-compatible by design
Cache-Pot communicates over RESP2, the same protocol Redis clients have used for years. Strings, hashes, lists, sets, sorted sets, pub/sub, transactions, TTLs, `SCAN`-family iterators — the everyday command surface you rely on is all there. Pointing an existing application at Cache-Pot is usually a one-line change:
```bash theme={null}
export REDIS_URL=redis://localhost:6379
```
No library swaps, no code changes, no migration scripts.
### 2. AI-native out of the box
Redis grew up serving application servers. Cache-Pot is aimed at AI agents and the applications that run them. Three capabilities that would require external modules or custom glue on Redis ship as first-class commands:
* **Vector store** — insert embeddings with `VSET`, query by cosine nearest-neighbour with `VSEARCH`. Your application supplies the float values; Cache-Pot handles the index.
* **Semantic cache** — store a model response once with `SCACHE.SET`. When a similar-enough prompt arrives later, `SCACHE.GET` returns the stored answer instead of making a fresh (and billable) model call.
* **Agent memory** — give an AI agent a persistent scratchpad with `REMEMBER` and `RECALL`, scoped to a session namespace.
### 3. Zero dependencies
The entire stack — RESP2 server, vector index, semantic cache, MCP bridge, and a full web management dashboard — compiles into a single binary. There is nothing else to install, no configuration file required to start, and no external service to keep running alongside it.
## How Cache-Pot compares to Redis
| Feature | Cache-Pot | Redis |
| ------------------------ | ------------ | ------------ |
| RESP2 protocol | ✅ | ✅ |
| Single binary | ✅ | ❌ |
| Vector search built-in | ✅ | Needs module |
| Semantic cache | ✅ | ❌ |
| MCP server | ✅ | ❌ |
| Clustering / replication | ❌ (roadmap) | ✅ |
| License | BSD-3-Clause | AGPL / RSAL |
Cache-Pot is the right fit when you need a capable Redis-style cache on a single machine and want AI features that work without bolting on extra infrastructure. For large production clusters with replication and failover, Redis or Valkey remain the mature choice — and Cache-Pot's roadmap includes replication as a future milestone.
## Core capabilities
Strings, hashes, lists, sets, sorted sets — the full Redis data model with identical command syntax.
Cache model responses by meaning and cut your inference bill every time a similar prompt hits the cache.
Store and search high-dimensional vectors with cosine similarity — no separate vector database required.
Give AI agents a named, persistent memory namespace they can read and write across turns.
Cache-Pot does not require authentication by default. Pass `--auth ` (or set `CACHEPOT_AUTH`) whenever you expose Cache-Pot on a network that isn't fully trusted. See the [configuration reference](/configuration/server) for the full list of flags and their environment-variable equivalents.