Best LLM Evaluation Tools for OpenAI

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

With a suite observability tools, you can confidently evaluate, test and ship LLM apps across your development and production lifecycle. Log traces and spans. Define and compute evaluation metrics. Score LLM outputs. Compare performance between app versions. Record, sort, find, and understand every step that your LLM app makes to generate a result. You can manually annotate and compare LLM results in a table. Log traces in development and production. Run experiments using different prompts, and evaluate them against a test collection. You can choose and run preconfigured evaluation metrics, or create your own using our SDK library. Consult the built-in LLM judges to help you with complex issues such as hallucination detection, factuality and moderation. Opik LLM unit tests built on PyTest provide reliable performance baselines. Build comprehensive test suites for every deployment to evaluate your entire LLM pipe-line.

Introducing the inaugural platform designed specifically for prompt engineers, where you can log OpenAI requests, review usage history, monitor performance, and easily manage your prompt templates. With this tool, you’ll never lose track of that perfect prompt again, ensuring GPT operates seamlessly in production. More than 1,000 engineers have placed their trust in this platform to version their prompts and oversee API utilization effectively. Begin integrating your prompts into production by creating an account on PromptLayer; just click “log in” to get started. Once you’ve logged in, generate an API key and make sure to store it securely. After you’ve executed a few requests, you’ll find them displayed on the PromptLayer dashboard! Additionally, you can leverage PromptLayer alongside LangChain, a widely used Python library that facilitates the development of LLM applications with a suite of useful features like chains, agents, and memory capabilities. Currently, the main method to access PromptLayer is via our Python wrapper library, which you can install effortlessly using pip. This streamlined approach enhances your workflow and maximizes the efficiency of your prompt engineering endeavors.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Athina functions as a collaborative platform for AI development, empowering teams to efficiently create, test, and oversee their AI applications. It includes a variety of features such as prompt management, evaluation tools, dataset management, and observability, all aimed at facilitating the development of dependable AI systems. With the ability to integrate various models and services, including custom solutions, Athina also prioritizes data privacy through detailed access controls and options for self-hosted deployments. Moreover, the platform adheres to SOC-2 Type 2 compliance standards, ensuring a secure setting for AI development activities. Its intuitive interface enables seamless collaboration between both technical and non-technical team members, significantly speeding up the process of deploying AI capabilities. Ultimately, Athina stands out as a versatile solution that helps teams harness the full potential of artificial intelligence.

OpenPipe offers an efficient platform for developers to fine-tune their models. It allows you to keep your datasets, models, and evaluations organized in a single location. You can train new models effortlessly with just a click. The system automatically logs all LLM requests and responses for easy reference. You can create datasets from the data you've captured, and even train multiple base models using the same dataset simultaneously. Our managed endpoints are designed to handle millions of requests seamlessly. Additionally, you can write evaluations and compare the outputs of different models side by side for better insights. A few simple lines of code can get you started; just swap out your Python or Javascript OpenAI SDK with an OpenPipe API key. Enhance the searchability of your data by using custom tags. Notably, smaller specialized models are significantly cheaper to operate compared to large multipurpose LLMs. Transitioning from prompts to models can be achieved in minutes instead of weeks. Our fine-tuned Mistral and Llama 2 models routinely exceed the performance of GPT-4-1106-Turbo, while also being more cost-effective. With a commitment to open-source, we provide access to many of the base models we utilize. When you fine-tune Mistral and Llama 2, you maintain ownership of your weights and can download them whenever needed. Embrace the future of model training and deployment with OpenPipe's comprehensive tools and features.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

Ragas is a comprehensive open-source framework aimed at testing and evaluating applications that utilize Large Language Models (LLMs). It provides automated metrics to gauge performance and resilience, along with the capability to generate synthetic test data that meets specific needs, ensuring quality during both development and production phases. Furthermore, Ragas is designed to integrate smoothly with existing technology stacks, offering valuable insights to enhance the effectiveness of LLM applications. The project is driven by a dedicated team that combines advanced research with practical engineering strategies to support innovators in transforming the landscape of LLM applications. Users can create high-quality, diverse evaluation datasets that are tailored to their specific requirements, allowing for an effective assessment of their LLM applications in real-world scenarios. This approach not only fosters quality assurance but also enables the continuous improvement of applications through insightful feedback and automatic performance metrics that clarify the robustness and efficiency of the models. Additionally, Ragas stands as a vital resource for developers seeking to elevate their LLM projects to new heights.

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

Promptfoo proactively identifies and mitigates significant risks associated with large language models before they reach production. The founders boast a wealth of experience in deploying and scaling AI solutions for over 100 million users, utilizing automated red-teaming and rigorous testing to address security, legal, and compliance challenges effectively. By adopting an open-source, developer-centric methodology, Promptfoo has become the leading tool in its field, attracting a community of more than 20,000 users. It offers custom probes tailored to your specific application, focusing on identifying critical failures instead of merely targeting generic vulnerabilities like jailbreaks and prompt injections. With a user-friendly command-line interface, live reloading, and efficient caching, users can operate swiftly without the need for SDKs, cloud services, or login requirements. This tool is employed by teams reaching millions of users and is backed by a vibrant open-source community. Users can create dependable prompts, models, and retrieval-augmented generation (RAG) systems with benchmarks that align with their unique use cases. Additionally, it enhances the security of applications through automated red teaming and pentesting, while also expediting evaluations via its caching, concurrency, and live reloading features. Consequently, Promptfoo stands out as a comprehensive solution for developers aiming for both efficiency and security in their AI applications.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

AI engineering can be transparent rather than opaque. With a suite of tools for tracing, assessment, prompt management, and more, HoneyHive emerges as a comprehensive platform for AI observability and evaluation, aimed at helping teams create dependable generative AI applications. This platform equips users with resources for model evaluation, testing, and monitoring, promoting effective collaboration among engineers, product managers, and domain specialists. By measuring quality across extensive test suites, teams can pinpoint enhancements and regressions throughout the development process. Furthermore, it allows for the tracking of usage, feedback, and quality on a large scale, which aids in swiftly identifying problems and fostering ongoing improvements. HoneyHive is designed to seamlessly integrate with various model providers and frameworks, offering the necessary flexibility and scalability to accommodate a wide range of organizational requirements. This makes it an ideal solution for teams focused on maintaining the quality and performance of their AI agents, delivering a holistic platform for evaluation, monitoring, and prompt management, ultimately enhancing the overall effectiveness of AI initiatives. As organizations increasingly rely on AI, tools like HoneyHive become essential for ensuring robust performance and reliability.

Orq.ai stands out as the leading platform tailored for software teams to effectively manage agentic AI systems on a large scale. It allows you to refine prompts, implement various use cases, and track performance meticulously, ensuring no blind spots and eliminating the need for vibe checks. Users can test different prompts and LLM settings prior to launching them into production. Furthermore, it provides the capability to assess agentic AI systems within offline environments. The platform enables the deployment of GenAI features to designated user groups, all while maintaining robust guardrails, prioritizing data privacy, and utilizing advanced RAG pipelines. It also offers the ability to visualize all agent-triggered events, facilitating rapid debugging. Users gain detailed oversight of costs, latency, and overall performance. Additionally, you can connect with your preferred AI models or even integrate your own. Orq.ai accelerates workflow efficiency with readily available components specifically designed for agentic AI systems. It centralizes the management of essential phases in the LLM application lifecycle within a single platform. With options for self-hosted or hybrid deployment, it ensures compliance with SOC 2 and GDPR standards, thereby providing enterprise-level security. This comprehensive approach not only streamlines operations but also empowers teams to innovate and adapt swiftly in a dynamic technological landscape.

Understanding the shortcomings of models can be challenging, particularly in identifying which data caused poor performance and the reasons behind it. Galileo offers a comprehensive suite of tools that allows machine learning teams to detect and rectify data errors up to ten times quicker. By analyzing your unlabeled data, Galileo can automatically pinpoint patterns of errors and gaps in the dataset utilized by your model. We recognize that the process of ML experimentation can be chaotic, requiring substantial data and numerous model adjustments over multiple iterations. With Galileo, you can manage and compare your experiment runs in a centralized location and swiftly distribute reports to your team. Designed to seamlessly fit into your existing ML infrastructure, Galileo enables you to send a curated dataset to your data repository for retraining, direct mislabeled data to your labeling team, and share collaborative insights, among other functionalities. Ultimately, Galileo is specifically crafted for ML teams aiming to enhance the quality of their models more efficiently and effectively. This focus on collaboration and speed makes it an invaluable asset for teams striving to innovate in the machine learning landscape.

Symflower revolutionizes the software development landscape by merging static, dynamic, and symbolic analyses with Large Language Models (LLMs). This innovative fusion capitalizes on the accuracy of deterministic analyses while harnessing the imaginative capabilities of LLMs, leading to enhanced quality and expedited software creation. The platform plays a crucial role in determining the most appropriate LLM for particular projects by rigorously assessing various models against practical scenarios, which helps ensure they fit specific environments, workflows, and needs. To tackle prevalent challenges associated with LLMs, Symflower employs automatic pre-and post-processing techniques that bolster code quality and enhance functionality. By supplying relevant context through Retrieval-Augmented Generation (RAG), it minimizes the risk of hallucinations and boosts the overall effectiveness of LLMs. Ongoing benchmarking guarantees that different use cases remain robust and aligned with the most recent models. Furthermore, Symflower streamlines both fine-tuning and the curation of training data, providing comprehensive reports that detail these processes. This thorough approach empowers developers to make informed decisions and enhances overall productivity in software projects.

Literal AI is a collaborative platform crafted to support engineering and product teams in the creation of production-ready Large Language Model (LLM) applications. It features an array of tools focused on observability, evaluation, and analytics, which allows for efficient monitoring, optimization, and integration of different prompt versions. Among its noteworthy functionalities are multimodal logging, which incorporates vision, audio, and video, as well as prompt management that includes versioning and A/B testing features. Additionally, it offers a prompt playground that allows users to experiment with various LLM providers and configurations. Literal AI is designed to integrate effortlessly with a variety of LLM providers and AI frameworks, including OpenAI, LangChain, and LlamaIndex, and comes equipped with SDKs in both Python and TypeScript for straightforward code instrumentation. The platform further facilitates the development of experiments against datasets, promoting ongoing enhancements and minimizing the risk of regressions in LLM applications. With these capabilities, teams can not only streamline their workflows but also foster innovation and ensure high-quality outputs in their projects.

ChainForge serves as an open-source visual programming platform aimed at enhancing prompt engineering and evaluating large language models. This tool allows users to rigorously examine the reliability of their prompts and text-generation models, moving beyond mere anecdotal assessments. Users can conduct simultaneous tests of various prompt concepts and their iterations across different LLMs to discover the most successful combinations. Additionally, it assesses the quality of responses generated across diverse prompts, models, and configurations to determine the best setup for particular applications. Evaluation metrics can be established, and results can be visualized across prompts, parameters, models, and configurations, promoting a data-driven approach to decision-making. The platform also enables the management of multiple conversations at once, allows for the templating of follow-up messages, and supports the inspection of outputs at each interaction to enhance communication strategies. ChainForge is compatible with a variety of model providers, such as OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and locally hosted models like Alpaca and Llama. Users have the flexibility to modify model settings and leverage visualization nodes for better insights and outcomes. Overall, ChainForge is a comprehensive tool tailored for both prompt engineering and LLM evaluation, encouraging innovation and efficiency in this field.