Best AI tools for< Test Llm Applications >

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Giskard is an AI testing platform designed to secure Language Model (LLM) agents by continuously testing applications to prevent hallucinations and security issues. It is powered by leading AI researchers and trusted by Enterprise AI teams. Giskard offers features such as continuous testing, exhaustive risk detection, easy testing deployment, cross-team collaboration, and independent validation. The platform enables users to turn business knowledge into AI tests, generate comprehensive test scenarios, and stay protected with continuous Red Teaming that adapts to new threats.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

Lakera is the world's most advanced AI security platform that offers cutting-edge solutions to safeguard GenAI applications against various security threats. Lakera provides real-time security controls, stress-testing for AI systems, and protection against prompt attacks, data loss, and insecure content. The platform is powered by a proprietary AI threat database and aligns with global AI security frameworks to ensure top-notch security standards. Lakera is suitable for security teams, product teams, and LLM builders looking to secure their AI applications effectively and efficiently.

Haystack is a production-ready open-source AI framework designed to facilitate building AI applications. It offers a flexible components and pipelines architecture, allowing users to customize and build applications according to their specific requirements. With partnerships with leading LLM providers and AI tools, Haystack provides freedom of choice for users. The framework is built for production, with fully serializable pipelines, logging, monitoring integrations, and deployment guides for full-scale deployments on various platforms. Users can build Haystack apps faster using deepset Studio, a platform for drag-and-drop construction of pipelines, testing, debugging, and sharing prototypes.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

Tonic.ai is a platform that allows users to build AI models on their unstructured data. It offers various products for software development and LLM development, including tools for de-identifying and subsetting structured data, scaling down data, handling semi-structured data, and managing ephemeral data environments. Tonic.ai focuses on standardizing, enriching, and protecting unstructured data, as well as validating RAG systems. The platform also provides integrations with relational databases, data lakes, NoSQL databases, flat files, and SaaS applications, ensuring secure data transformation for software and AI developers.

Future AGI is a revolutionary AI data management platform that aims to achieve 99% accuracy in AI applications across software and hardware. It provides a comprehensive evaluation and optimization platform for enterprises to enhance the performance of their AI models. Future AGI offers features such as creating trustworthy, accurate, and responsible AI, 10x faster processing, generating and managing diverse synthetic datasets, testing and analyzing agentic workflow configurations, assessing agent performance, enhancing LLM application performance, monitoring and protecting applications in production, and evaluating AI across different modalities.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Ferhat Erata is an AI application developed by a Computer Science PhD graduate from Yale University. The application focuses on training transformers to solve NP-complete problems using reinforcement learning and improving test-time compute strategies for reasoning. It also explores learning randomized reductions and program properties for security, privacy, and side-channel resilience. Ferhat Erata is currently an Applied Scientist at the Automated Reasoning Group at AWS, working on Neuro-Symbolic AI to prevent factual errors caused by LLM hallucinations using mathematically sound Automated Reasoning checks.

Prompt Hippo is an AI tool designed as a side-by-side LLM prompt testing suite to ensure the robustness, reliability, and safety of prompts. It saves time by streamlining the process of testing LLM prompts and allows users to test custom agents and optimize them for production. With a focus on science and efficiency, Prompt Hippo helps users identify the best prompts for their needs.

Reprompt is a prompt testing tool designed to simplify the process for developers. It allows users to deploy prompts with confidence, make data-driven decisions, analyze data efficiently, speed up debugging, and compare changes with previous versions. The tool offers features such as real-time trading, fast operations, no commissions, enterprise encryption, and advanced security standards.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

AIMLAPI.com is an AI tool that provides access to over 200 AI models through a single AI API. It offers a wide range of AI features for tasks such as chat, code, image generation, music generation, video, voice embedding, language, genomic models, and 3D generation. The platform ensures fast inference, top-tier serverless infrastructure, high data security, 99% uptime, and 24/7 support. Users can integrate AI features easily into their products and test API models in a sandbox environment before deployment.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

PromptPoint Playground is an AI tool designed to help users design, test, and deploy prompts quickly and efficiently. It enables teams to create high-quality LLM outputs through automatic testing and evaluation. The platform allows users to make non-deterministic prompts predictable, organize prompt configurations, run automated tests, and monitor usage. With a focus on collaboration and accessibility, PromptPoint Playground empowers both technical and non-technical users to leverage the power of large language models for prompt engineering.

LLMChess is a web-based chess game that utilizes large language models (LLMs) to power the gameplay. Players can select the LLM model they wish to play against, and the game will commence once the "Start" button is clicked. The game logs are displayed in a black-bordered pane on the right-hand side of the screen. LLMChess is compatible with the Google Chrome browser. For more information on the game's functionality and participation guidelines, please refer to the provided link.

Freeplay is a tool that helps product teams experiment, test, monitor, and optimize AI features for customers. It provides a single pane of glass for the entire team, lightweight developer SDKs for Python, Node, and Java, and deployment options to meet compliance needs. Freeplay also offers best practices for the entire AI development lifecycle.

pytest-evals is a minimalistic pytest plugin designed to help evaluate the performance of Language Model (LLM) outputs against test cases. It allows users to test and evaluate LLM prompts against multiple cases, track metrics, and integrate easily with pytest, Jupyter notebooks, and CI/CD pipelines. Users can scale up by running tests in parallel with pytest-xdist and asynchronously with pytest-asyncio. The tool focuses on simplifying evaluation processes without the need for complex frameworks, keeping tests and evaluations together, and emphasizing logic over infrastructure.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

promptmap2 is a vulnerability scanning tool that automatically tests prompt injection attacks on custom LLM applications. It analyzes LLM system prompts, runs them, and sends attack prompts to determine if injection was successful. It has ready-to-use rules to steal system prompts or distract LLM applications. Supports multiple LLM providers like OpenAI, Anthropic, and open source models via Ollama. Customizable test rules in YAML format and automatic model download for Ollama.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

The LLM Engineer Toolkit is a curated repository containing over 120 LLM libraries categorized for various tasks such as training, application development, inference, serving, data extraction, data generation, agents, evaluation, monitoring, prompts, structured outputs, safety, security, embedding models, and other miscellaneous tools. It includes libraries for fine-tuning LLMs, building applications powered by LLMs, serving LLM models, extracting data, generating synthetic data, creating AI agents, evaluating LLM applications, monitoring LLM performance, optimizing prompts, handling structured outputs, ensuring safety and security, embedding models, and more. The toolkit covers a wide range of tools and frameworks to streamline the development, deployment, and optimization of large language models.

Agents-Flex is a LLM Application Framework like LangChain base on Java. It provides a set of tools and components for building LLM applications, including LLM Visit, Prompt and Prompt Template Loader, Function Calling Definer, Invoker and Running, Memory, Embedding, Vector Storage, Resource Loaders, Document, Splitter, Loader, Parser, LLMs Chain, and Agents Chain.

The Prompt Engineering Holy Grail repository is a curated resource for prompt engineering enthusiasts, providing essential resources, tools, templates, and best practices to support learning and working in prompt engineering. It covers a wide range of topics related to prompt engineering, from beginner fundamentals to advanced techniques, and includes sections on learning resources, online courses, books, prompt generation tools, prompt management platforms, prompt testing and experimentation, prompt crafting libraries, prompt libraries and datasets, prompt engineering communities, freelance and job opportunities, contributing guidelines, code of conduct, support for the project, and contact information.

A curated list of Generative AI projects, tools, artworks, and models

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

Praison AI is a low-code, centralised framework that simplifies the creation and orchestration of multi-agent systems for various LLM applications. It emphasizes ease of use, customization, and human-agent interaction. The tool leverages AutoGen and CrewAI frameworks to facilitate the development of AI-generated scripts and movie concepts. Users can easily create, run, test, and deploy agents for scriptwriting and movie concept development. Praison AI also provides options for full automatic mode and integration with OpenAI models for enhanced AI capabilities.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

Hack Me if you can - I can only talk to you about computer security, software security and LLM security @JacquesGariepy

Test Shaman: Guiding software testing with Grug wisdom and humor, balancing fun with practical advice.

Provides Raven's Progressive Matrices test with explanations and calculates your IQ score.

I craft tests first, then code, validating new features.

An AI conducting 30-question IQ tests, assessing and providing detailed feedback.

I will provide guidance on testing, verification, and validation for QA roles.

Formal and structured interactive IQ test platform.

Assists teachers in marking tests and generating score reports.

How smart are you? Solve it with your friends, find out who is smarter!

Software code tester and debugger

Formal, concise Angular 2 test guide.

Personality test by birthday.

Helps user learn essential vocabulary for GRE test with multiple choice questions

I'm your lab test consultant for blood tests and microbial cultures. How can I help you today?

Creates Application Security Test cases in YAML

Helping you study for cybersecurity certifications and get the job you want!

Expert in advanced data analysis and interpretation

Crafting full, accurate Apex test classes, with 100% user service.

Train to pass the Reverse Turing Test!

Your go-to MBTI assessment tool, providing personalized insights.