LayerLens

DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.

LLM Scout is an evaluation and analysis platform designed to help users benchmark, compare, and interpret the performance of large language models across diverse tasks, datasets, and real-world prompts within a unified environment. It enables side-by-side comparisons of models by measuring accuracy, reasoning, factuality, bias, safety, and other key metrics using customizable evaluation suites, curated benchmarks, and domain-specific tests. It supports the ingestion of user-provided data and queries so teams can assess how different models respond to their own real-world workflows or industry-specific needs, and visualize outputs in an intuitive dashboard that highlights performance trends, strengths, and weaknesses. LLM Scout also includes tools for analyzing token usage, latency, cost implications, and model behavior under varied conditions, helping stakeholders make informed decisions about which models best fit specific applications or quality requirements.