Alternatives to promptfoo

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Vercel combines the best in developer experience with a laser-focused focus on end-user performance. Our platform allows frontend teams to do their best work. Next.js is a React framework Vercel created with Google and Facebook. It's loved by developers. Next.js powers some of the most popular websites, including Twilio and Washington Post. It is used for news, e-commerce and travel. Vercel is the best place for any frontend app to be deployed. Start by connecting to our global edge network with zero configuration. Scale dynamically to millions upon millions of pages without breaking a sweat. Live editing for your UI components. Connect your pages to any data source or headless CMS and make them work in every dev environment. All of our cloud primitives, from caching to Serverless functions, work perfectly on localhost.

Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality. Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset. Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.

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.

Latitude is a comprehensive platform for prompt engineering, helping product teams design, test, and optimize AI prompts for large language models (LLMs). It provides a suite of tools for importing, refining, and evaluating prompts using real-time data and synthetic datasets. The platform integrates with production environments to allow seamless deployment of new prompts, with advanced features like automatic prompt refinement and dataset management. Latitude’s ability to handle evaluations and provide observability makes it a key tool for organizations seeking to improve AI performance and operational efficiency.

Pezzo serves as an open-source platform for LLMOps, specifically designed for developers and their teams. With merely two lines of code, users can effortlessly monitor and troubleshoot AI operations, streamline collaboration and prompt management in a unified location, and swiftly implement updates across various environments. This efficiency allows teams to focus more on innovation rather than operational challenges.

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

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.

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.

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.

Relying solely on a few examples is insufficient for thorough evaluation. To gain actionable insights for enhancing your models, it’s essential to gather extensive end-user feedback. With the improvement engine designed for GPT, you can effortlessly conduct A/B tests on models and prompts. While prompts serve as a starting point, achieving superior results necessitates fine-tuning on your most valuable data—no coding expertise or data science knowledge is required. Integrate with just a single line of code and seamlessly experiment with various language model providers like Claude and ChatGPT without needing to revisit the setup. By leveraging robust APIs, you can create innovative and sustainable products, provided you have the right tools to tailor the models to your clients’ needs. Copy AI fine-tunes models using their best data, leading to cost efficiencies and a competitive edge. This approach fosters enchanting product experiences that captivate over 2 million active users, highlighting the importance of continuous improvement and adaptation in a rapidly evolving landscape. Additionally, the ability to iterate quickly on user feedback ensures that your offerings remain relevant and engaging.

TruLens is a versatile open-source Python library aimed at the systematic evaluation and monitoring of Large Language Model (LLM) applications. It features detailed instrumentation, feedback mechanisms, and an intuitive interface that allows developers to compare and refine various versions of their applications, thereby promoting swift enhancements in LLM-driven projects. The library includes programmatic tools that evaluate the quality of inputs, outputs, and intermediate results, enabling efficient and scalable assessments. With its precise, stack-agnostic instrumentation and thorough evaluations, TruLens assists in pinpointing failure modes while fostering systematic improvements in applications. Developers benefit from an accessible interface that aids in comparing different application versions, supporting informed decision-making and optimization strategies. TruLens caters to a wide range of applications, including but not limited to question-answering, summarization, retrieval-augmented generation, and agent-based systems, making it a valuable asset for diverse development needs. As developers leverage TruLens, they can expect to achieve more reliable and effective LLM applications.

Pose any inquiry to two different anonymous AI chatbots, such as ChatGPT, Gemini, Claude, or Llama, and select the most impressive answer; you can continue this process until one emerges as the champion. Should the identity of any AI be disclosed, your selection will be disqualified. You have the option to upload an image and converse, or utilize text-to-image models like DALL-E 3, Flux, and Ideogram to create visuals. Additionally, you can engage with GitHub repositories using the RepoChat feature. Our platform, which is supported by over a million community votes, evaluates and ranks the top LLMs and AI chatbots. Chatbot Arena serves as a collaborative space for crowdsourced AI evaluation, maintained by researchers at UC Berkeley SkyLab and LMArena. We also offer the FastChat project as open source on GitHub and provide publicly available datasets for further exploration. This initiative fosters a thriving community centered around AI advancements and user engagement.

Introduce features powered by LLMs into production using tools designed for prompt engineering, semantic search, version control, quantitative testing, and performance tracking, all of which are compatible with the leading LLM providers. Expedite the process of developing a minimum viable product by testing various prompts, parameters, and different LLM providers to quickly find the optimal setup for your specific needs. Vellum serves as a fast, dependable proxy to LLM providers, enabling you to implement version-controlled modifications to your prompts without any coding requirements. Additionally, Vellum gathers model inputs, outputs, and user feedback, utilizing this information to create invaluable testing datasets that can be leveraged to assess future modifications before deployment. Furthermore, you can seamlessly integrate company-specific context into your prompts while avoiding the hassle of managing your own semantic search infrastructure, enhancing the relevance and precision of your interactions.

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.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

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.

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.

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.

Launch top-notch LLM applications swiftly while maintaining rigorous testing standards. You should never feel constrained by the intricate and often subjective aspects of LLM interactions. Generative AI often yields subjective outcomes, and determining the quality of generated content frequently necessitates the expertise of a subject matter professional. If you're developing an LLM application, you're likely aware of the myriad constraints and edge cases that must be managed before a successful release. Issues such as hallucinations, inaccurate responses, biases, policy deviations, and potentially harmful content must all be identified, investigated, and addressed both prior to and following the launch of your application. Deepchecks offers a solution that automates the assessment process, allowing you to obtain "estimated annotations" that only require your intervention when absolutely necessary. With over 1000 companies utilizing our platform and integration into more than 300 open-source projects, our core LLM product is both extensively validated and reliable. You can efficiently validate machine learning models and datasets with minimal effort during both research and production stages, streamlining your workflow and improving overall efficiency. This ensures that you can focus on innovation without sacrificing quality or safety.

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!

Prompt Flow is a comprehensive suite of development tools aimed at optimizing the entire development lifecycle of AI applications built on LLMs, encompassing everything from concept creation and prototyping to testing, evaluation, and final deployment. By simplifying the prompt engineering process, it empowers users to develop high-quality LLM applications efficiently. Users can design workflows that seamlessly combine LLMs, prompts, Python scripts, and various other tools into a cohesive executable flow. This platform enhances the debugging and iterative process, particularly by allowing users to easily trace interactions with LLMs. Furthermore, it provides capabilities to assess the performance and quality of flows using extensive datasets, while integrating the evaluation phase into your CI/CD pipeline to maintain high standards. The deployment process is streamlined, enabling users to effortlessly transfer their flows to their preferred serving platform or integrate them directly into their application code. Collaboration among team members is also improved through the utilization of the cloud-based version of Prompt Flow available on Azure AI, making it easier to work together on projects. This holistic approach to development not only enhances efficiency but also fosters innovation in LLM application creation.

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.

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.

Traceloop is an all-encompassing observability platform tailored for the monitoring, debugging, and quality assessment of outputs generated by Large Language Models (LLMs). It features real-time notifications for any unexpected variations in output quality and provides execution tracing for each request, allowing for gradual implementation of changes to models and prompts. Developers can effectively troubleshoot and re-execute production issues directly within their Integrated Development Environment (IDE), streamlining the debugging process. The platform is designed to integrate smoothly with the OpenLLMetry SDK and supports a variety of programming languages, including Python, JavaScript/TypeScript, Go, and Ruby. To evaluate LLM outputs comprehensively, Traceloop offers an extensive array of metrics that encompass semantic, syntactic, safety, and structural dimensions. These metrics include QA relevance, faithfulness, overall text quality, grammatical accuracy, redundancy detection, focus evaluation, text length, word count, and the identification of sensitive information such as Personally Identifiable Information (PII), secrets, and toxic content. Additionally, it provides capabilities for validation through regex, SQL, and JSON schema, as well as code validation, ensuring a robust framework for the assessment of model performance. With such a diverse toolkit, Traceloop enhances the reliability and effectiveness of LLM outputs significantly.

Atla's Selene 1 API delivers cutting-edge AI evaluation models, empowering developers to set personalized assessment standards and achieve precise evaluations of their AI applications' effectiveness. Selene surpasses leading models on widely recognized evaluation benchmarks, guaranteeing trustworthy and accurate assessments. Users benefit from the ability to tailor evaluations to their unique requirements via the Alignment Platform, which supports detailed analysis and customized scoring systems. This API not only offers actionable feedback along with precise evaluation scores but also integrates smoothly into current workflows. It features established metrics like relevance, correctness, helpfulness, faithfulness, logical coherence, and conciseness, designed to tackle prevalent evaluation challenges, such as identifying hallucinations in retrieval-augmented generation scenarios or contrasting results with established ground truth data. Furthermore, the flexibility of the API allows developers to innovate and refine their evaluation methods continuously, making it an invaluable tool for enhancing AI application performance.

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.

DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.

Utilize BenchLLM to assess your code in real-time, creating comprehensive test suites for your models while generating detailed quality reports. You can select from automated, interactive, or customized evaluation methodologies. Our dedicated team of engineers is passionate about developing AI solutions without sacrificing the balance between the strength and adaptability of AI and reliable outcomes. We've created a versatile and open-source LLM evaluation tool that we always wished existed. Execute and review models effortlessly with intuitive CLI commands, employing this interface as a testing instrument for your CI/CD workflows. Keep track of model performance and identify potential regressions in a production environment. Assess your code instantly, as BenchLLM is compatible with OpenAI, Langchain, and a variety of other APIs right out of the box. Explore diverse evaluation strategies and present valuable insights through visual reports, ensuring that your AI models meet the highest standards. Our goal is to empower developers with the tools they need for seamless integration and evaluation.

Teammately is an innovative AI agent designed to transform the landscape of AI development by autonomously iterating on AI products, models, and agents to achieve goals that surpass human abilities. Utilizing a scientific methodology, it fine-tunes and selects the best combinations of prompts, foundational models, and methods for knowledge organization. To guarantee dependability, Teammately creates unbiased test datasets and develops adaptive LLM-as-a-judge systems customized for specific projects, effectively measuring AI performance and reducing instances of hallucinations. The platform is tailored to align with your objectives through Product Requirement Docs (PRD), facilitating targeted iterations towards the intended results. Among its notable features are multi-step prompting, serverless vector search capabilities, and thorough iteration processes that consistently enhance AI until the set goals are met. Furthermore, Teammately prioritizes efficiency by focusing on identifying the most compact models, which leads to cost reductions and improved overall performance. This approach not only streamlines the development process but also empowers users to leverage AI technology more effectively in achieving their aspirations.

Monitor the performance of your models to identify and respond to data drift, enhancing accuracy for improved business results. Foster trust, ensure regulatory compliance, and promote actionable machine learning outcomes using Arthur’s APIs that prioritize explainability and transparency. Actively supervise for biases, evaluate model results against tailored bias metrics, and enhance your models' fairness. Understand how each model interacts with various demographic groups, detect biases early, and apply Arthur's unique bias reduction strategies. Arthur is capable of scaling to accommodate up to 1 million transactions per second, providing quick insights. Only authorized personnel can perform actions, ensuring data security. Different teams or departments can maintain separate environments with tailored access controls, and once data is ingested, it becomes immutable, safeguarding the integrity of metrics and insights. This level of control and monitoring not only improves model performance but also supports ethical AI practices.

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.

AgentBench serves as a comprehensive evaluation framework tailored to measure the effectiveness and performance of autonomous AI agents. It features a uniform set of benchmarks designed to assess various dimensions of an agent's behavior, including their proficiency in task-solving, decision-making, adaptability, and interactions with simulated environments. By conducting evaluations on tasks spanning multiple domains, AgentBench aids developers in pinpointing both the strengths and limitations in the agents' performance, particularly regarding their planning, reasoning, and capacity to learn from feedback. This framework provides valuable insights into an agent's capability to navigate intricate scenarios that mirror real-world challenges, making it beneficial for both academic research and practical applications. Ultimately, AgentBench plays a crucial role in facilitating the ongoing enhancement of autonomous agents, ensuring they achieve the required standards of reliability and efficiency prior to their deployment in broader contexts. This iterative assessment process not only fosters innovation but also builds trust in the performance of these autonomous systems.

Utilize Weights & Biases (WandB) for experiment tracking, hyperparameter tuning, and versioning of both models and datasets. With just five lines of code, you can efficiently monitor, compare, and visualize your machine learning experiments. Simply enhance your script with a few additional lines, and each time you create a new model version, a fresh experiment will appear in real-time on your dashboard. Leverage our highly scalable hyperparameter optimization tool to enhance your models' performance. Sweeps are designed to be quick, easy to set up, and seamlessly integrate into your current infrastructure for model execution. Capture every aspect of your comprehensive machine learning pipeline, encompassing data preparation, versioning, training, and evaluation, making it incredibly straightforward to share updates on your projects. Implementing experiment logging is a breeze; just add a few lines to your existing script and begin recording your results. Our streamlined integration is compatible with any Python codebase, ensuring a smooth experience for developers. Additionally, W&B Weave empowers developers to confidently create and refine their AI applications through enhanced support and resources.

Introducing the ultimate data annotation tool that offers unparalleled flexibility and ease of installation. Users can create customized user interfaces or opt for ready-made labeling templates tailored to their specific needs. The adaptable layouts and templates seamlessly integrate with your dataset and workflow requirements. It supports various object detection methods in images, including boxes, polygons, circles, and key points, and allows for the segmentation of images into numerous parts. Additionally, machine learning models can be utilized to pre-label data and enhance efficiency throughout the annotation process. Features such as webhooks, a Python SDK, and an API enable users to authenticate, initiate projects, import tasks, and manage model predictions effortlessly. Save valuable time by leveraging predictions to streamline your labeling tasks, thanks to the integration with ML backends. Furthermore, users can connect to cloud object storage solutions like S3 and GCP to label data directly in the cloud. The Data Manager equips you with advanced filtering options to effectively prepare and oversee your dataset. This platform accommodates multiple projects, diverse use cases, and various data types, all in one convenient space. By simply typing in the configuration, you can instantly preview the labeling interface. Live serialization updates at the bottom of the page provide a real-time view of what Label Studio anticipates as input, ensuring a smooth user experience. This tool not only improves annotation accuracy but also fosters collaboration among teams working on similar projects.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

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.

A unified platform for LLM applications. Use all the best-in class LLMs. Integration is dead simple. You can easily trace user sessions, debug and trace user sessions.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

Garak evaluates the potential failures of an LLM in undesirable ways, examining aspects such as hallucination, data leakage, prompt injection, misinformation, toxicity, jailbreaks, and various other vulnerabilities. This free tool is designed with an eagerness for development, continually seeking to enhance its functionalities for better application support. Operating as a command-line utility, Garak is compatible with both Linux and OSX systems; you can easily download it from PyPI and get started right away. The pip version of Garak receives regular updates, ensuring it remains current, while its specific dependencies recommend setting it up within its own Conda environment. To initiate a scan, Garak requires the model to be analyzed and, by default, will conduct all available probes on that model utilizing the suggested vulnerability detectors for each. During the scanning process, users will see a progress bar for every loaded probe, and upon completion, Garak will provide a detailed evaluation of each probe's findings across all detectors. This makes Garak not only a powerful tool for assessment but also a vital resource for researchers and developers aiming to enhance the safety and reliability of LLMs.

Scale Evaluation presents an all-encompassing evaluation platform specifically designed for developers of large language models. This innovative platform tackles pressing issues in the field of AI model evaluation, including the limited availability of reliable and high-quality evaluation datasets as well as the inconsistency in model comparisons. By supplying exclusive evaluation sets that span a range of domains and capabilities, Scale guarantees precise model assessments while preventing overfitting. Its intuitive interface allows users to analyze and report on model performance effectively, promoting standardized evaluations that enable genuine comparisons. Furthermore, Scale benefits from a network of skilled human raters who provide trustworthy evaluations, bolstered by clear metrics and robust quality assurance processes. The platform also provides targeted evaluations utilizing customized sets that concentrate on particular model issues, thereby allowing for accurate enhancements through the incorporation of new training data. In this way, Scale Evaluation not only improves model efficacy but also contributes to the overall advancement of AI technology by fostering rigorous evaluation practices.

RagaAI stands out as the premier AI testing platform, empowering businesses to minimize risks associated with artificial intelligence while ensuring that their models are both secure and trustworthy. By effectively lowering AI risk exposure in both cloud and edge environments, companies can also manage MLOps expenses more efficiently through smart recommendations. This innovative foundation model is crafted to transform the landscape of AI testing. Users can quickly pinpoint necessary actions to address any dataset or model challenges. Current AI-testing practices often demand significant time investments and hinder productivity during model development, leaving organizations vulnerable to unexpected risks that can lead to subpar performance after deployment, ultimately wasting valuable resources. To combat this, we have developed a comprehensive, end-to-end AI testing platform designed to significantly enhance the AI development process and avert potential inefficiencies and risks after deployment. With over 300 tests available, our platform ensures that every model, data, and operational issue is addressed, thereby speeding up the AI development cycle through thorough testing. This rigorous approach not only saves time but also maximizes the return on investment for businesses navigating the complex AI landscape.

Our dashboard provides an in-depth analysis that allows you to confirm all essential details concerning request submissions to Guardrails AI. Streamline your processes by utilizing our comprehensive library of pre-built validators designed for immediate use. Enhance your workflow with strong validation measures that cater to various scenarios, ensuring adaptability and effectiveness. Empower your projects through a flexible framework that supports the creation, management, and reuse of custom validators, making it easier to address a wide range of innovative applications. This blend of versatility and user-friendliness facilitates seamless integration and application across different projects. By pinpointing errors and verifying outcomes, you can swiftly produce alternative options, ensuring that results consistently align with your expectations for accuracy, precision, and reliability in interactions with LLMs. Additionally, this proactive approach to error management fosters a more efficient development environment.

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Create modular APIs with a zero-trust approach and deploy them serverlessly, regardless of the legacy systems in place. Constructing a robust infrastructure can be quite challenging, as even the most proficient teams may find it difficult to adhere to architectural plans amidst rapidly changing requirements and the complexities of the technology landscape. Typegraphs serve as programmable virtual graphs that represent all the elements of your system architecture. They allow for the composition of APIs, storage solutions, and business logic in a manner that ensures type safety. Typegate acts as a distributed HTTP/GraphQL query engine that compiles, optimizes, executes, and caches queries on typegraphs, while also handling authentication, authorization, and security measures for you. You can easily integrate third-party dependencies and begin reusing existing components with ease. The Meta CLI enhances your workflow by providing live reloading features and facilitating one-command deployment to Metacloud or any personal instance. Furthermore, Metatype addresses a critical gap in the technology ecosystem by offering a novel method for building rapid, developer-centric APIs that meet growing demands. By utilizing these innovative tools, you can streamline your development process and adapt more swiftly to the changing tech environment.

The best digital signage software will have, without exception, three distinct hallmarks: it will not only be user-friendly but also secure and scalable. Pickcel's cloud-based digital signage software is the perfect solution for all your digital signage needs. Real-time monitoring of the device status on different parameters, such as network status and content sync status. Remote troubleshooting features include restarting devices, reloading content, clearing cache, clearing data, and taking screenshots. Advanced features such as automated content distribution (Enterprise) can be customized using custom parameters. You can also set default content to screens so that they never go blank. Easy roll-out to deploy digital signage software across large screens. Pickcel digital signage software is also available for deployment at your private cloud or datacenter. You have complete control over your digital signage system with on-premise solutions.

Create flawless, single-codebase applications for Mobile, Web, and Desktop utilizing C#, XAML, and .NET, which is the pioneering C# & XAML platform that is free and open-source for developing genuine single-source, multi-platform applications. Achieve a remarkable 99% reuse of business logic and UI components across native mobile, web, and desktop environments, ensuring you maintain pixel-perfect precision in your design while having the flexibility to implement platform-specific features or a unique aesthetic for your application. Experience the familiarity and depth of C# and XAML, enhanced by productivity features such as hot reload, hot restart, and edit-and-continue capabilities. You can utilize well-known editors like Visual Studio, Visual Studio Code, or Rider to streamline your development process. Additionally, both community-driven and official support options are available, including chat, ticketing, and even screen-sharing assistance to help you troubleshoot and enhance your experience. Your development journey is backed by a vibrant community, ensuring you never feel alone in tackling challenges.

Businesses that operate real-time web and mobile applications often face challenges related to latency, bandwidth, and scalability, which can adversely affect both their total cost of ownership and the immediate experience for their users. Such challenges arise from traditional methods, including HTTP polling and long polling, which are commonly used for facilitating real-time communication through web and application servers. To address these shortcomings, we developed MigratoryData, an innovative real-time messaging technology that utilizes the WebSockets standard to efficiently stream data to users through persistent WebSocket connections, achieving response times in milliseconds while keeping traffic overhead low. Unlike many other existing real-time messaging solutions, MigratoryData is specifically engineered to accommodate a vast number of users simultaneously. In fact, it has been tested to successfully deliver real-time data to as many as 10 million concurrent users from a single standard server, showcasing its exceptional performance and scalability capabilities. This advancement not only enhances user experience but also optimizes operational costs for enterprises.

Guardrails play an essential role in the upkeep of AI systems, and LangWatch serves to protect both you and your organization from the risks of disclosing sensitive information, prompt injection, and potential AI misbehavior, thereby safeguarding your brand from unexpected harm. For businesses employing integrated AI, deciphering the interactions between AI and users can present significant challenges. To guarantee that responses remain accurate and suitable, it is vital to maintain consistent quality through diligent oversight. LangWatch's safety protocols and guardrails effectively mitigate prevalent AI challenges, such as jailbreaking, unauthorized data exposure, and irrelevant discussions. By leveraging real-time metrics, you can monitor conversion rates, assess output quality, gather user feedback, and identify gaps in your knowledge base, thus fostering ongoing enhancement. Additionally, the robust data analysis capabilities enable the evaluation of new models and prompts, the creation of specialized datasets for testing purposes, and the execution of experimental simulations tailored to your unique needs, ensuring that your AI system evolves in alignment with your business objectives. With these tools, businesses can confidently navigate the complexities of AI integration and optimize their operational effectiveness.