A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.
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Updated
Jun 30, 2025 - Python
A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.
Redis Vector Library (RedisVL) -- the AI-native Python client for Redis.
Unified AI Gateway for 30+ LLMs (OpenAI, Anthropic, Bedrock, Azure etc) with Caching, Guardrails, A/B test & cost controls. Go-native Fastest & Scalable AI Gateway LiteLLM & Kong AI Gateway alternative.
SmarterRouter: An intelligent LLM gateway and VRAM-aware router for Ollama, llama.cpp, and OpenAI. Features semantic caching, model profiling, and automatic failover for local AI labs.
mimir is a drop-in proxy that caches LLM API responses using semantic similarity, reducing costs and latency for repeated or similar queries.
Reliable and Efficient Semantic Prompt Caching with vCache
Own your LLM's web search: a local search->fetch->rank pipeline that replaces hosted web-search tools. Measured: matches hosted accuracy at 66% lower cost and up to 88% fewer tokens, plus a precision-tuned semantic caching with query-dependant TTL that no API offers.
RAMen is a fast in-memory data store like Redis, but built for AI: drop-in Redis protocol, native vector search, semantic caching, and a built-in MCP server for agents. Single Go binary, BSD-3.
Transparent, transport-layer semantic cache for LLM API calls, powered by Redis 8 Vector Sets.
Unified multi-layer caching library for AI/agent pipelines — LangChain, LangGraph, AutoGen, CrewAI, Agno, A2A
Redis integration for Google Agent Development Kit (ADK) - Memory, Sessions, Search Tools, MCP
Enterprise AI traffic gateway — unified compliance, routing across 20+ LLM providers, semantic cache, quotas, and audit. SDK / network / OS-layer intercept.
Redis Vector Library (RedisVL) -- the AI-native Java client for Redis.
This is a RAG based chatbot in which semantic cache and guardrails have been incorporated.
This repository contains sample code demonstrating how to implement a verified semantic cache using Amazon Bedrock Knowledge Bases to prevent hallucinations in Large Language Model (LLM) responses while improving latency and reducing costs.
High-performance LLM query cache with semantic search. Reduce API costs 80% and latency from 8.5s to 1ms using Redis + Qdrant vector DB. Multi-provider support (OpenAI, Anthropic).
ToolOps is a framework-agnostic middleware SDK that treats every tool call as a first-class operation. By wrapping your tools in a single decorator, you instantly upgrade them with industrial-grade caching, resilience, and observability.
AI real-estate automation platform: Telegram bot, RAG, apartment search, CRM workflows, voice agent, Langfuse observability, and Dockerized AI runtime.
Local-first semantic cache for AI agents. A small C daemon + CLI that remembers what your agent learned across sessions. Plugs into Claude Code, Codex, Gemini CLI, and Claude Desktop / ChatGPT via MCP. No LLM calls, no SaaS, no API key.
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