Alternatives to Vellum AI

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.

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

Test, launch and refine Gen AI prompting. RAG Agents. Datasets. Workflows. A single workspace for Engineers and Product Managers to explore LLM technology.

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.

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!

The first platform designed for prompt engineers. Log OpenAI requests, track usage history, visual manage prompt templates, and track performance. Manage Never forget one good prompt. GPT in Prod, done right. Trusted by more than 1,000 engineers to monitor API usage and version prompts. Your prompts can be used in production. Click "log in" to create an account on PromptLayer. Once you have logged in, click on the button to create an API Key and save it in a secure place. After you have made your first few requests, the API key should be visible in the PromptLayer dashboard. LangChain can be used with PromptLayer. LangChain is a popular Python library that assists in the development and maintenance of LLM applications. It offers many useful features such as memory, agents, chains, and agents. Our Python wrapper library, which can be installed with pip, is the best way to access PromptLayer at this time.

Literal AI is an open-source platform that helps engineering and product teams develop production-grade Large Language Model applications. It provides a suite for observability and evaluation, as well as analytics. This allows for efficient tracking, optimization and integration of prompt version. The key features are multimodal logging encompassing audio, video, and vision, prompt management, with versioning and testing capabilities, as well as a prompt playground to test multiple LLM providers. Literal AI integrates seamlessly into various LLM frameworks and AI providers, including OpenAI, LangChain and LlamaIndex. It also provides SDKs for Python and TypeScript to instrument code. The platform supports the creation and execution of experiments against datasets to facilitate continuous improvement in LLM applications.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Pezzo is an open-source LLMOps tool for developers and teams. With just two lines of code you can monitor and troubleshoot your AI operations. You can also collaborate and manage all your prompts from one place.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

AI engineering does not have to be a mystery. You can get full visibility using tools for tracing and evaluation, prompt management and more. HoneyHive is a platform for AI observability, evaluation and team collaboration that helps teams build reliable generative AI applications. It provides tools for evaluating and testing AI models and monitoring them, allowing engineers, product managers and domain experts to work together effectively. Measure the quality of large test suites in order to identify improvements and regressions at each iteration. Track usage, feedback and quality at a large scale to identify issues and drive continuous improvements. HoneyHive offers flexibility and scalability for diverse organizational needs. It supports integration with different model providers and frameworks. It is ideal for teams who want to ensure the performance and quality of their AI agents. It provides a unified platform that allows for evaluation, monitoring and prompt management.

PromptHub allows you to test, collaborate, version and deploy prompts from a single location. Use variables to simplify prompt creation and stop copying and pasting. Say goodbye to spreadsheets and compare outputs easily when tweaking prompts. Batch testing allows you to test your datasets, and prompts, at scale. Test different models, parameters, and variables to ensure consistency. Test different models, system messaging, or chat templates. Commit prompts, branch out, and collaborate seamlessly. We detect prompts changes so you can concentrate on outputs. Review changes in a team setting, approve new versions and keep everyone on track. Monitor requests, costs and latencies easily. PromptHub allows you to easily test, collaborate, and version prompts. With our GitHub-style collaboration and versioning, it's easy to iterate and store your prompts in one place.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

Automated voice testing, monitoring and more. Test your AI voice agent with 1000s of simulated users within minutes. It's hard to get AI voice agents right. LLM outputs can be affected by a small change in the prompts, function calls or model providers. We are the only platform that can support you from development through to production. Hamming allows you to store, manage, update and sync your prompts with voice infra provider. This is 1000x faster than testing voice agents manually. Use our prompt playground for testing LLM outputs against a dataset of inputs. Our LLM judges quality of generated outputs. Save 80% on manual prompt engineering. Monitor your app in more than one way. We actively track, score and flag cases where you need to pay attention. Convert calls and traces to test cases, and add them to the golden dataset.

Iterate quickly, and ship confidently. Ship confidently by evaluating prompts using a suite evals such as context recall, llm rubric (LLM is a judge), latencies, and more. We can handle complex implementations and intelligent caching to save you money and time. Iterate quickly on your prompts using a collaborative playground. This includes all major providers, variables, versioning and more. You can easily build datasets using real data by using Logs. You can also upload your own CSV or collaborate to build and edit them within your Adaline workspace. Our APIs allow you to track usage, latency and other metrics in order to monitor the performance of your LLMs. Our APIs allow you to continuously evaluate your completions on production, see the way your users use your prompts, create datasets, and send logs. The platform allows you to iterate and monitor LLMs. You can easily rollback if you see a decline in your production and see how the team iterated on the prompt.

Create generative AI apps for engineers and not just ML practitioners. Focus on creating instead of configuring. No more switching platforms or wrestling with APIs. Use a familiar interface for AI and to prompt engineers. Workbooks can be easily streamlined into templates by using parameters. Create workflows using model outputs from LLMs and image and audio models. Create groups to manage workbooks between your teammates. Share your workbook with your team or the public, or to specific organizations that you define. Workbooks can be commented on and compared with your team. Create templates for you, your team or the developer community. Get started quickly by using templates to see what others are building.

Compose, test and optimize prompts for the most popular language models and AI platforms. Promptmetheus, an Integrated Development Environment for LLM prompts is designed to help automate workflows and enhance products and services using the mighty GPT and other cutting edge AI models. The transformer architecture has enabled cutting-edge Language Models to reach parity with the human ability in certain narrow cognitive tasks. To effectively leverage their power, however, we must ask the right questions. Promptmetheus is a complete prompt engineering software toolkit that adds composability and traceability to the prompt design to help you discover those questions.

Traceloop is an observability platform that allows you to monitor, debug and test the output quality from Large Language Models. It provides real-time alerts when unexpected output quality changes occur, execution tracing of every request and the ability to roll out changes to prompts and models in a gradual manner. Developers can debug issues directly from production in their Integrated Development Environment. Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform offers a wide range of semantic, syntax, safety and structural metrics for assessing LLM outputs. These include QA relevance, faithfulness and text quality. It also includes redundancy detection and focus assessment.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

Take control of your LLMs with Freeplay. It gives product teams the ability to prototype faster, test confidently, and optimize features. A better way to build using LLMs. Bridge the gap between domain specialists & developers. Engineering, testing & evaluation toolkits for your entire team.

ChainForge is a visual programming environment that is open-source and designed for large language model evaluation. It allows users to evaluate the robustness and accuracy of text-generation models and prompts beyond anecdotal data. Test prompt ideas and variations simultaneously across multiple LLMs in order to identify the most efficient combinations. Evaluate response quality for different prompts, models and settings to determine the optimal configuration. Set up evaluation metrics, and visualize results for prompts, parameters and models. This will facilitate data-driven decisions. Manage multiple conversations at once, template follow-ups, and inspect the outputs to refine interactions. ChainForge supports a variety of model providers including OpenAI HuggingFace Anthropic Google PaLM2, Azure OpenAI Endpoints and locally hosted models such as Alpaca and Llama. Users can modify model settings and use visualization nodes.

We are ready to meet all your business needs, whether it is quick engineering, fine-tuning or training. The Together Inference API makes it easy to integrate your new model in your production application. Together AI's elastic scaling and fastest performance allows it to grow with you. To increase accuracy and reduce risks, you can examine how models are created and what data was used. You are the owner of the model that you fine-tune and not your cloud provider. Change providers for any reason, even if the price changes. Store data locally or on our secure cloud to maintain complete data privacy.

All in one place, simplify prompt edits, SDK integration, and version control. No more waiting for deployments or scattered tools. Explore features designed to streamline your workflow and elevate prompting engineering. Manage your projects and prompts in a structured manner with tools that keep everything organized. Adapt your prompts dynamically to the context of your app, improving user experience through tailored interactions. Our user-friendly SDK is designed to minimize disruption and maximize efficiency. Utilize detailed analytics to better understand prompt performance, user interaction, and areas for improvements, based on concrete data. Invite team members to work together in a shared workspace where everyone can review, refine, and contribute prompts. Control access and permissions to ensure that your team members can work efficiently.

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.

Your platform for developing generative AI and custom copilots. Use pre-built and customizable AI model on your data to build solutions faster. Explore a growing collection of models, both open-source and frontier-built, that are pre-built and customizable. Create AI models using a code first experience and an accessible UI validated for accessibility by developers with disabilities. Integrate all your OneLake data into Microsoft Fabric. Integrate with GitHub codespaces, Semantic Kernel and LangChain. Build apps quickly with prebuilt capabilities. Reduce wait times by personalizing content and interactions. Reduce the risk for your organization and help them discover new things. Reduce the risk of human error by using data and tools. Automate operations so that employees can focus on more important tasks.

Create and version control your prompts. Retrieve prompts using an API generated automatically. Automate the end-to-end LLM test before updating your prompts in production. Let your engineers and industry specialists collaborate on the same platform. Let your industry experts and prompt engineers test, iterate and collaborate on the same platform, without any programming knowledge. You can run an unlimited number of test cases with our testing suite to ensure the quality and reliability of your prompt. Check for issues, edge-cases, and more. Our suite of prompts is the most complex. Use Git features to manage your prompts. Create a repository and multiple branches for each project to iterate your prompts. Commit changes and test them on a separate system. Revert to an earlier version with ease. Our real-time APIs allow you to update your prompt in real time with just one click.

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.

RagaAI is a leading AI testing platform which helps enterprises to mitigate AI risks, and make their models reliable and secure. Intelligent recommendations will reduce AI risk across cloud or edge deployments, and optimize MLOps cost. A foundation model designed specifically to revolutionize AI testing. You can easily identify the next steps for fixing dataset and model problems. AI-testing methods are used by many today, and they increase time commitments and reduce productivity when building models. They also leave unforeseen risks and perform poorly after deployment, wasting both time and money. We have created an end-toend AI testing platform to help enterprises improve their AI pipeline and prevent inefficiencies. 300+ tests to identify, fix, and accelerate AI development by identifying and fixing every model, data and operational issue.

Release high-quality LLM applications quickly without compromising testing. Never let the subjective and complex nature of LLM interactions hold you back. Generative AI produces subjective results. A subject matter expert must manually check a generated text to determine its quality. You probably know if you're developing an LLM application that you cannot release it without addressing numerous constraints and edge cases. Hallucinations and other issues, such as incorrect answers, bias and deviations from policy, harmful material, and others, need to be identified, investigated, and mitigated both before and after the app is released. Deepchecks allows you to automate your evaluation process. You will receive "estimated annotations", which you can only override if necessary. Our LLM product has been extensively tested and is robust. It is used by more than 1000 companies and integrated into over 300 open source projects. Validate machine-learning models and data in the research and production phases with minimal effort.

Our dashboard allows you to dig deeper into analytics, allowing you to verify the information you need to enter requests into Guardrails. Our library of ready-to-use validators will help you unlock efficiency. Validation for diverse use cases can optimize your workflow. Boost your projects by leveraging a dynamic framework that allows you to create, manage, and reuse custom validators. The versatility of the software is matched by its ease of use, allowing it to be used for a wide range innovative applications. You can quickly generate another output option by indicating the error and verifying it. Assures that the outcomes are in line, with expectations, accuracy, correctness, reliability, and interactions with LLMs.

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

DeepEval is an open-source, easy-to-use framework for evaluating large-language-model systems. It is similar Pytest, but is specialized for unit-testing LLM outputs. DeepEval incorporates research to evaluate LLM results based on metrics like G-Eval (hallucination), answer relevancy, RAGAS etc. This uses LLMs as well as various other NLP models which run locally on your computer for evaluation. DeepEval can handle any implementation, whether it's RAG, fine-tuning or LangChain or LlamaIndex. It allows you to easily determine the best hyperparameters for your RAG pipeline. You can also prevent drifting and even migrate from OpenAI to your own Llama2 without any worries. The framework integrates seamlessly with popular frameworks and supports synthetic dataset generation using advanced evolution techniques. It also allows for efficient benchmarking and optimizing of LLM systems.

With confidence, collaborate on prompts, monitor and evaluate LLM apps. Agenta is an integrated platform that allows teams to build robust LLM applications quickly. Create a playground where your team can experiment together. Comparing different prompts, embeddings, and models in a systematic way before going into production is key. Share a link with the rest of your team to get human feedback. Agenta is compatible with all frameworks, including Langchain, Lama Index and others. Model providers (OpenAI, Cohere, Huggingface, self-hosted, etc.). You can see the costs, latency and chain of calls for your LLM app. You can create simple LLM applications directly from the UI. If you want to create customized applications, then you will need to use Python to write the code. Agenta is model-agnostic, and works with any model provider or framework. Our SDK is currently only available in Python.

Models can be opaque about what data they failed to perform well on and why. Galileo offers a variety of tools that allow ML teams to quickly inspect and find ML errors up to 10x faster. Galileo automatically analyzes your unlabeled data and identifies data gaps in your model. We get it - ML experimentation can be messy. It requires a lot data and model changes across many runs. You can track and compare your runs from one place. You can also quickly share reports with your entire team. Galileo is designed to integrate with your ML ecosystem. To retrain, send a fixed dataset to the data store, label mislabeled data to your labels, share a collaboration report, and much more, Galileo was designed for ML teams, enabling them to create better quality models faster.

BenchLLM allows you to evaluate your code in real-time. Create test suites and quality reports for your models. Choose from automated, interactive, or custom evaluation strategies. We are a group of engineers who enjoy building AI products. We don't want a compromise between the power, flexibility and predictability of AI. We have created the open and flexible LLM tool that we always wanted. CLI commands are simple and elegant. Use the CLI to test your CI/CD pipeline. Monitor model performance and detect regressions during production. Test your code in real-time. BenchLLM supports OpenAI (Langchain), and any other APIs out of the box. Visualize insightful reports and use multiple evaluation strategies.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.

It's not enough to just look at a few examples. To get actionable insights about how to improve your models, gather feedback from end-users at large. With the GPT improvement engine, you can easily A/B test models. You can only go so far with prompts. Fine-tuning your best data will produce better results. No coding or data science required. Integration in one line of code You can experiment with ChatGPT, Claude and other language model providers without having to touch it again. If you have the right tools to customize models for your customers, you can build innovative and defensible products on top APIs. Copy AI allows you to fine tune models based on the best data. This will allow you to save money and give you a competitive edge. This technology allows for magical product experiences that delight more than 2 million users.

Promptfoo identifies and eliminates LLM risks prior to their being shipped into production. Its founders are experienced in launching and scaling AI for over 100M users, using automated red-teaming, testing, and compliance to overcome security, regulatory, and compliance issues. Promptfoo is the most widely used tool in this area, with more than 20,000 users, thanks to its open source, developer first approach. Custom probes that are tailored to your application and identify the failures you care about. Not just generic jailbreaks or prompt injections. With a command-line, live reloads and caching, you can move quickly. No SDKs or cloud dependencies. Open-source software used by teams that serve millions of users, and supported by a vibrant community. Build RAGs, models and prompts that are reliable, based on benchmarks that are specific to your use-case. Automated red teaming and pentesting will help you secure your apps. Accelerate evaluations by using caching, concurrency and live reloading.

Automatic output evaluation and testing will turbocharge your team's prompt development by ensuring high quality LLM outputs. With the ability to save and organize prompt configurations, you can easily design and organize your prompts. Automated tests will give you comprehensive results in just seconds. This will save you time and increase your efficiency. Structure your prompt configurations precisely, and then deploy them instantly to your own software applications. Design, test and deploy prompts as quickly as you can think. Your team can help you bridge the gap between technical execution of prompts and their real-world relevance. PromptPoint is a natively no-code platform that allows anyone in your team to create and test prompt configurations. Connecting seamlessly with hundreds of large languages models allows you to maintain flexibility in a world of many models.

Get a complete picture of the performance of your model. Log inputs and out-puts, and enrich them with metadata. Find out what your model is doing and where it can be improved. Monitor for errors, and identify underperforming cohorts or use cases. The best models are based on user data. To retrain your model, you can programmatically gather examples that are unusual or underperforming. When changing your model or prompt, stop manually reviewing thousands outputs. Apps powered by LLM can be evaluated programmatically. Detect and fix degradations fast. Monitor new deployments and edit your app in real-time. Connect your data sources to your self-hosted model or third-party model. Our serverless streaming dataflow engines can handle large amounts of data. Gantry is SOC-2-compliant and built using enterprise-grade authentication.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Narrow AI: Remove the Engineer from Prompt Engineering Narrow AI automatically writes, monitors and optimizes prompts on any model. This allows you to ship AI features at a fractional cost. Maximize quality and minimize costs Reduce AI costs by 95% using cheaper models Automated Prompt Optimizer: Improve accuracy - Achieve faster response times with lower latency models Test new models within minutes, not weeks - Compare the performance of LLMs quickly - Benchmarks on cost and latency for each model - Deploy the optimal model for your usage case Ship LLM features up to 10x faster - Automatically generate expert level prompts - Adapt prompts as new models are released - Optimize prompts in terms of quality, cost and time

Lisapet.ai, an advanced AI prompt-testing platform, accelerates the development and deployment of AI features. It was developed by a team that manages a SaaS platform powered by AI with over 15M users. It automates prompt tests, reducing manual work and ensuring reliable outcomes. The AI Playground is a key feature, as are parameterized prompts and structured outputs. Work together seamlessly with automated testing suites, detailed reporting, and real-time analysis to optimize performance and reduce costs. Lisapet.ai helps you ship AI features faster, with greater confidence.

TruLens, an open-source Python Library, is designed to evaluate and track Large Language Model applications. It offers fine-grained instruments, feedback functions and a user-interface to compare and iterate app versions. This facilitates rapid development and improvement of LLM based applications. Tools that allow scalable evaluation of the inputs, outputs and intermediate results of LLM applications. Instrumentation that is fine-grained and stack-agnostic, and comprehensive evaluations can help identify failure modes. A simple interface allows developers to compare versions of their application, facilitating informed decisions and optimization. TruLens supports a variety of use cases, such as question-answering and summarization. It also supports retrieval-augmented generation and agent-based apps.

Prompt hunt's advanced AI model, called Chroma, along with a library of styles and templates that have been verified, makes creating art simple and accessible. Prompt Hunt gives you the tools to unleash your creativity and create stunning art and assets in minutes, whether you're an experienced artist or a novice. We know how important privacy is, so we provide this feature to our users. Templates in Prompt hunt are pre-designed structures or frameworks that simplify the process of creating artwork without the need for complex prompt engineers. The template will handle the work behind the scenes and generate the desired output by simply entering the subject and clicking "create". Anyone can create their own templates with Prompt Hunt. You can choose to share or keep your designs private.

Set up your workspace easily by integrating GPT language models with your provider key for unparalleled performance. LLM Spark's GPT templates can be used to create AI applications quickly. Or, you can start from scratch and create unique projects. Test and compare multiple models at the same time to ensure optimal performance in multiple scenarios. Save versions and history with ease while streamlining development. Invite others to your workspace so they can collaborate on projects. Semantic search is a powerful search tool that allows you to find documents by meaning and not just keywords. AI applications can be made accessible across platforms by deploying trained prompts.

Promptologer supports the next generation of prompt engineers and entrepreneurs. Promptologer allows you to display your collection of GPTs and prompts, share content easily with our blog integration and benefit from shared traffic through the Promptologer eco-system. Your all-in one toolkit for product development, powered by AI. UserTale helps you plan and execute your product strategy with ease, while minimizing ambiguity. It does this by generating product requirements and crafting insightful personas for users and business models. Yippity's AI powered question generator can automatically convert text into multiple-choice, true/false or fill-in the blank quizzes. The different prompts can produce a variety of outputs. We provide you with a platform to deploy AI web applications exclusive to your team. This allows team members the ability to create, share and use company-approved prompts.

We believe that the most effective and differentiated applications won't only call out via an API to a language model. LangChain supports several modules. We provide examples, how-to guides and reference docs for each module. Memory is the concept that a chain/agent calls can persist in its state. LangChain provides a standard interface to memory, a collection memory implementations and examples of agents/chains that use it. This module outlines best practices for combining language models with your own text data. Language models can often be more powerful than they are alone.

Mirascope is a powerful, flexible and user-friendly library that simplifies the process of working with LLMs through a unified interface. It works across various supported providers including OpenAI, Anthropic Mistral Gemini Groq Cohere LiteLLM Azure AI Vertex AI and Bedrock. Mirascope is a flexible, powerful and user-friendly LLM library that simplifies working with LLMs. It has a unified interface and works across multiple supported providers including OpenAI, Anthropic Mistral, Gemini Groq Cohere LiteLLM Azure AI Vertex AI and Bedrock. Mirascope is a powerful and flexible library that allows you to create robust, powerful applications. Mirascope's response models allow you to structure the output of LLMs and validate it. This feature is especially useful when you want to make sure that the LLM response follows a certain format or contains specific fields.