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  1. Stackups
  2. AI
  3. Text & Language Models
  4. Llms
  5. DeepEval vs OpenAI

DeepEval vs OpenAI

OverviewComparisonAlternatives

Overview

OpenAI
OpenAI
Stacks942
Followers197
Votes0
DeepEval
DeepEval
Stacks1
Followers1
Votes0
GitHub Stars11.9K
Forks1.0K

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Detailed Comparison

OpenAI
OpenAI
DeepEval
DeepEval

Creating safe artificial general intelligence that benefits all of humanity. Our work to create safe and beneficial AI requires a deep understanding of the potential risks and benefits, as well as careful consideration of the impact.

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.

Pioneering research on the path to AGI; Transforming work and creativity with AI
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
Statistics
GitHub Stars
-
GitHub Stars
11.9K
GitHub Forks
-
GitHub Forks
1.0K
Stacks
942
Stacks
1
Followers
197
Followers
1
Votes
0
Votes
0
Integrations
Microsoft Azure
Microsoft Azure
ChatGPT
ChatGPT
LlamaIndex
LlamaIndex
GuardRails
GuardRails
LangChain
LangChain

What are some alternatives to OpenAI, DeepEval?

Grok-1

Grok-1

It is the base model weights and network architecture of Grok-1, the large language model. Grok-1 is a 314 billion parameter Mixture-of-Experts model trained from scratch by xAI.

Clever AI Humanizer

Clever AI Humanizer

That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

LangChain

LangChain

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.

Google Gemini

Google Gemini

It is Google’s largest and most capable AI model. It is built to be multimodal, it can generalize, understand, operate across, and combine different types of info — like text, images, audio, video, and code.

Ollama

Ollama

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

LLaMA

LLaMA

It is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI.

LlamaIndex

LlamaIndex

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.

Whisper

Whisper

It is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

LangGraph

LangGraph

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.

LangSmith

LangSmith

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.

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