It is a simple-to-use, open-source evaluation framework for LLM applications. It is similar to Pytest but specialized for unit testing LLM applications. It evaluates performance based on metrics such as hallucination, answer relevancy, RAGAS, etc., using LLMs and various other NLP models locally on your machine. | 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. |
Simple functions to unit test LLM applications in the CLI;
Gain insights to quickly iterate towards optimal hyperparameters;
Evaluate existing LLM applications built with other frameworks | Build language agents as graphs;
Built on top of LangChain;
Inspired by Pregel and Apache Beam |
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That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

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

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Build, train, and deploy state of the art models powered by the reference open source in machine learning.

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