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Cache-Pot is a Redis-compatible in-memory data store, rebuilt for how AI apps and agents use a cache. Speak plain RESP2, your existing Redis client works untouched. On top: native vector search, a semantic cache (skip repeat LLM calls when a question means the same thing), a built-in MCP server so agents like Claude use it as a first-class tool, and a live web dashboard. All in one self-contained binary, no modules, no extra services.
Hey Product Hunt 👋
I built Cache-Pot because every AI project I touched ended up duct-taping the same three things onto Redis: a vector store, a semantic cache, and some bridge so an agent could actually use the cache as a tool. So I built them in.
What it is:
- **Redis-compatible core** — talks RESP2, so `redis-cli` and your existing Redis client work as-is. Point `REDIS_URL` at it and go.
- **Vector search** — `VSET` / `VSEARCH`, backed by a hybrid brute-force + HNSW index. No API key needed, you supply the vectors.
- **Semantic cache** — `SCACHE.SET` / `SCACHE.GET THRESHOLD 0.9`. Ask something close to a question you cached before, get the stored answer instead of paying for another model call.
- **Agent memory** — `REMEMBER` / `RECALL`, a simple key-value scratchpad scoped per session.
- **Native MCP server** — `cache-pot mcp` — Claude (or any MCP client) reads/writes/searches/remembers through it directly, no adapter layer.
- **Dashboard** — full web console baked into the binary: key browser, live profiler, slowlog, pub/sub, memory analysis, client list.
- **Single binary** — `go install` or Docker, nothing else to stand up.
Honest limits: no clustering/replication yet, and it's not chasing raw-throughput records against Redis/Valkey — it's built for single-node AI workloads, not to replace a Redis cluster in prod.
It's BSD-3-Clause, free, and open source, repo's linked above. Would love bug reports, "tried it with my client X" notes, and PRs, most open issues are scoped small with acceptance criteria already written, good for a first contribution.
Thanks for checking it out!
Dropped my existing redis client at it and everything just worked, which never happens. The semantic cache alone has already cut a chunk of repeat calls from my agent's loop.
One thing that would make this even more useful for me: built-in TTL policies tied to semantic similarity scores, so near-duplicate entries auto-expire before drifting too far from the original. Right now the semantic cache seems to grow forever unless I set a hard TTL manually, and that can lead to stale answers sneaking through.
About Cache-Pot on Product Hunt
“A superfast memory layer built for AI agents”
Cache-Pot was submitted on Product Hunt and earned 8 upvotes and 3 comments, placing #159 on the daily leaderboard. Cache-Pot is a Redis-compatible in-memory data store, rebuilt for how AI apps and agents use a cache. Speak plain RESP2, your existing Redis client works untouched. On top: native vector search, a semantic cache (skip repeat LLM calls when a question means the same thing), a built-in MCP server so agents like Claude use it as a first-class tool, and a live web dashboard. All in one self-contained binary, no modules, no extra services.
Cache-Pot was featured in Open Source (68.6k followers), Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 221.6k products, making this a competitive space to launch in.
Who hunted Cache-Pot?
Cache-Pot was hunted by Subhadip Saha. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Subhadip Saha