Best LLM Evaluation Tools for JavaScript
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Compare the Top LLM Evaluation Tools that integrate with JavaScript as of July 2025
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This a list of LLM Evaluation tools that integrate with JavaScript. Use the filters on the left to add additional filters for products that have integrations with JavaScript. View the products that work with JavaScript in the table below.
What are LLM Evaluation Tools for JavaScript?
LLM (Large Language Model) evaluation tools are designed to assess the performance and accuracy of AI language models. These tools analyze various aspects, such as the model's ability to generate relevant, coherent, and contextually accurate responses. They often include metrics for measuring language fluency, factual correctness, bias, and ethical considerations. By providing detailed feedback, LLM evaluation tools help developers improve model quality, ensure alignment with user expectations, and address potential issues. Ultimately, these tools are essential for refining AI models to make them more reliable, safe, and effective for real-world applications. Compare and read user reviews of the best LLM Evaluation tools for JavaScript currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
LLM Evaluation in Vertex AI focuses on assessing the performance of large language models to ensure their effectiveness across various natural language processing tasks. Vertex AI provides tools for evaluating LLMs in tasks like text generation, question-answering, and language translation, allowing businesses to fine-tune models for better accuracy and relevance. By evaluating these models, businesses can optimize their AI solutions and ensure they meet specific application needs. New customers receive $300 in free credits to explore the evaluation process and test large language models in their own environment. This functionality enables businesses to enhance the performance of LLMs and integrate them into their applications with confidence.Starting Price: Free ($300 in free credits) -
2
OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
3
Arize Phoenix
Arize AI
Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.Starting Price: Free -
4
Traceloop
Traceloop
Traceloop is a comprehensive observability platform designed to monitor, debug, and test the quality of outputs from Large Language Models (LLMs). It offers real-time alerts for unexpected output quality changes, execution tracing for every request, and the ability to gradually roll out changes to models and prompts. Developers can debug and re-run issues from production directly in their Integrated Development Environment (IDE). Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform provides a range of semantic, syntactic, safety, and structural metrics to assess LLM outputs, such as QA relevancy, faithfulness, text quality, grammar correctness, redundancy detection, focus assessment, text length, word count, PII detection, secret detection, toxicity detection, regex validation, SQL validation, JSON schema validation, and code validation.Starting Price: $59 per month -
5
HumanSignal
HumanSignal
HumanSignal's Label Studio Enterprise is a comprehensive platform designed for creating high-quality labeled data and evaluating model outputs with human supervision. It supports labeling and evaluating multi-modal data, image, video, audio, text, and time series, all in one place. It offers customizable labeling interfaces with pre-built templates and powerful plugins, allowing users to tailor the UI and workflows to specific use cases. Label Studio Enterprise integrates seamlessly with popular cloud storage providers and ML/AI models, facilitating pre-annotation, AI-assisted labeling, and prediction generation for model evaluation. The Prompts feature enables users to leverage LLMs to swiftly generate accurate predictions, enabling instant labeling of thousands of tasks. It supports various labeling use cases, including text classification, named entity recognition, sentiment analysis, summarization, and image captioning.Starting Price: $99 per month -
6
HoneyHive
HoneyHive
AI engineering doesn't have to be a black box. Get full visibility with tools for tracing, evaluation, prompt management, and more. HoneyHive is an AI observability and evaluation platform designed to assist teams in building reliable generative AI applications. It offers tools for evaluating, testing, and monitoring AI models, enabling engineers, product managers, and domain experts to collaborate effectively. Measure quality over large test suites to identify improvements and regressions with each iteration. Track usage, feedback, and quality at scale, facilitating the identification of issues and driving continuous improvements. HoneyHive supports integration with various model providers and frameworks, offering flexibility and scalability to meet diverse organizational needs. It is suitable for teams aiming to ensure the quality and performance of their AI agents, providing a unified platform for evaluation, monitoring, and prompt management.
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