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GitHub
Transparent semantic cache for LLM API calls on Redis VS
Khazad is a transparent semantic cache for LLM API calls. It intercepts LLM HTTP traffic at the httpx transport layer and serves semantically-equivalent requests from a Redis 8 vector cache with zero code changes. Works with OpenAI, Anthropic, Gemini, Azure OpenAI, and Mistral. Model-aware and conversation-aware caching, full streaming support, TTL, and tunable similarity thresholds. Stop paying for the same prompt twice in dev, CI, demos, or production. Open source (MIT).
Hi everyone, I'm the maker of Khazad.
I kept running into the same problem: I was paying for the
exact same LLM prompt over and over, and even in production a lot of user
traffic is near-identical questions (FAQ bots, RAG front-ends). Traditional
caching doesn't help because no two prompts are byte-for-byte the same.
So I built a semantic cache, but I wanted it to be truly transparent. Most
caching tools make you wrap their SDK or route traffic through a proxy. Khazad
instead intercepts outgoing LLM HTTP requests at the httpx transport layer, so
it works with the OpenAI, Anthropic, Gemini, Azure OpenAI, and Mistral SDKs with
zero changes to your application code. You call init() once and it's active.
Under the hood it uses Redis Vector Sets: each (provider, model) pair gets its
own vector set, the whole conversation is embedded (not just the last message),
and a similarity search decides whether to replay a cached response or let the
call go upstream.
The part that evolved most was getting streaming right, cache hits replay as
real SSE streams, and streamed misses are captured chunk-by-chunk and
reassembled into JSON, so a streamed answer can later serve a non-streamed
request and vice versa.
It's open source (MIT) and I'd genuinely love feedback, especially on the
transport-layer approach and how people would want to handle false-positive
control.
GitHub: https://github.com/GuglielmoCerr...
About GitHub on Product Hunt
“Transparent semantic cache for LLM API calls on Redis VS”
GitHub was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #145 on the daily leaderboard. Khazad is a transparent semantic cache for LLM API calls. It intercepts LLM HTTP traffic at the httpx transport layer and serves semantically-equivalent requests from a Redis 8 vector cache with zero code changes. Works with OpenAI, Anthropic, Gemini, Azure OpenAI, and Mistral. Model-aware and conversation-aware caching, full streaming support, TTL, and tunable similarity thresholds. Stop paying for the same prompt twice in dev, CI, demos, or production. Open source (MIT).
On the analytics side, GitHub competes within API, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how GitHub performed against the three products that launched closest to it on the same day.
Who hunted GitHub?
GitHub was hunted by Guglielmo Cerri. 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.
For a complete overview of GitHub including community comment highlights and product details, visit the product overview.
Guglielmo Cerri