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It is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax. It can scale up LLM training to hundreds of TPU/GPU accelerators by leveraging JAX's pjit functionality. | It is a library for creating semantic cache for LLM queries. Slash your LLM API costs by 10x, and boost speed by 100x. |
Can scale up LLM training to hundreds of TPU/GPU accelerators;
Easy to customize codebase for training large language models | Boost speed by 100x;
Reduce LLM API cost by 10x;
Fully integrated with LangChain |
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That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.

It is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.

Transform basic prompts into expert-level AI instructions. Enhance, benchmark & optimize prompts for ChatGPT, Claude, Gemini & more.

It is a new scripting language to automate your interaction with a Large Language Model (LLM), namely OpenAI. The ultimate goal is to create a natural language programming experience. The syntax of GPTScript is largely natural language, making it very easy to learn and use.

Open-source Chrome extension that uses a local LLM to automatically summarize, tag, and store AI conversation context. EXAONE 3.5 7.8B summarizes every 20 turns locally, auto-tagging project names, tech stack, and key decisions. Semantic vector search powered by Cloudflare Vectorize + bge-m3 (1024 dimensions, 100+ languages). Zero cloud API cost — your data never leaves your machine. 로컬 LLM이 AI 대화를 자동으로 요약·태깅·저장하는 오픈소스 크롬 확장 프로그램. EXAONE 3.5로 프라이버시를 지키면서 대화 맥락을 영구 보존합니다.

Based on the implementation of Google's TurboQuant (ICLR 2026) — Quansloth brings elite KV cache compression to local LLM inference. Quansloth is a fully private, air-gapped AI server that runs massive context models natively on consumer hardware with ease! Please have a look at its GitHub (Apache 2.0 License) - https://github.com/PacifAIst/Quansloth