Khazad is an open-source semantic cache that sits between Python applications and LLM APIs, replaying semantically similar cached responses instead of making redundant API calls. At a 0.50 hit rate, it reduces API call volume by ~50%, speeds up responses by ~96% on cache hits, and lowers costs by ~50%. It works transparently with OpenAI, Anthropic, Azure OpenAI, and compatible proxies, making it useful for teams running high-traffic FAQ bots, support tools, and development environments.
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Khazad, an open-source semantic cache for LLM API calls, intercepts HTTP traffic at the transport layer and serves semantically equivalent cached responses via Redis Vector Sets. At a 0.50 hit rate, it delivers ~50% fewer API calls, ~96% faster responses on cache hits, and ~50% lower spend; it works transparently with zero changes to application code.
Why it matters
For teams running high-volume, repetitive LLM traffic—FAQ bots, support assistants, RAG systems, dev/test environments—Khazad offers cost and latency savings without rewriting application code. It supports multiple LLM providers (OpenAI, Anthropic, Azure OpenAI, and OpenAI-compatible proxies like Ollama and vLLM) through a single Python init() call.
What to watch
Khazad requires Python ≥3.10 and Redis 8 with Vector Sets support. It is httpx-only, so SDKs built on requests, aiohttp, or boto3 (AWS Bedrock) are not intercepted. Start with threshold=0.90 to control false positives, and treat the Redis instance with the same security care as application logs, since prompts are embedded and responses are stored in clear text.
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