GitHub
Transparent, transport-layer semantic cache for LLM API calls, powered by Redis 8 Vector Sets. - GuglielmoCerri/khazad
GitHub launched on June 30, 2026 with 5 votes, #2,466 of 22,647 launches that month and more than 75% of that year's launches.
What it does
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).
Does a similar job
all alternatives →- ACAgent-cache – Multi-tier LLM/tool/session caching for Valkey and RedisApr 2026 · ▲18
Multi-tier exact-match cache for AI agents backed by Valkey or Redis. LLM responses, tool results, and session state behind one connection. Framework adapters for LangChain, LangGraph, and Vercel AI SDK. OpenTelemetry and Prometheus built in. No modules required - works on vanilla Valkey 7+ and Redis 6.2+. Shipped v0.1.0 yesterday, v0.2.0 today with cluster mode. Streaming support coming next. Existing options locked you into one tier (LangChain = LLM only, LangGraph = state only) or one framework. This solves both. npm:…
LLM Gateway2025 · llmgateway.io · ▲296I built an OpenAI-compatible LLM gateway that routes requests to OpenAI, Anthropic, Ollama, vLLM, llama-server, SGLang... anything that speaks /v1/chat/completions. Single Go binary, one YAML config file, no infrastructure. It does the things you'd expect from this kind of gateway... semantic routing via a three-layer cascade (keyword heuristics, embedding similarity, LLM classifier) that picks the best model when clients omit the model field, weighted round-robin load balancing across local inference servers with health checks and failover. The part I think is most…
GPTCache – Redis for LLMs2023 · github.com · ▲7Hey folks, As much as we love GPT-4, it's expensive and can be slow at times. That's why we built GPTCache - a semantic cache for autoregressive LMs - atop the vector database Milvus and SQLite. GPTCache provides several benefits: 1) reduced expenses due to minimizing the number of requests and tokens sent to the LLM service 2) enhanced performance by fetching cached query results directly 3) improved scalability and availability by avoiding rate limits, and 4) a flexible development environment that allows developers to verify their application's features without connecting to LLM APIs or…
An LLM response cache that's aware of dynamic dataJan 2026 · blog.butter.dev · ▲17Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs. Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables…


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