Best AI tools for< Monitor Llm Workflows >

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.

CrewAI is a leading multi-agent platform that enables users to streamline workflows across industries with powerful AI agents. Users can build and deploy automated workflows using any LLM and cloud platform. The platform offers tools for building, deploying, monitoring, and improving AI agents, providing complete visibility and control over automation processes. CrewAI is trusted by industry leaders and used in over 60 countries, offering a comprehensive solution for multi-agent automation.

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.

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.

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

OpenLIT is an AI application designed as an Observability tool for GenAI and LLM applications. It empowers model understanding and data visualization through an interactive Learning Interpretability Tool. With OpenTelemetry-native support, it seamlessly integrates into projects, offering features like fine-tuning performance, real-time data streaming, low latency processing, and visualizing data insights. The tool simplifies monitoring with easy installation and light/dark mode options, connecting to popular observability platforms for data export. Committed to OpenTelemetry community standards, OpenLIT provides valuable insights to enhance application performance and reliability.

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.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

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.

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.

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.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

deepset is an AI platform that offers enterprise-level products and solutions for AI teams. It provides deepset Cloud, a platform built with Haystack, enabling fast and accurate prototyping, building, and launching of advanced AI applications. The platform streamlines the AI application development lifecycle, offering processes, tools, and expertise to move from prototype to production efficiently. With deepset Cloud, users can optimize solution accuracy, performance, and cost, and deploy AI applications at any scale with one click. The platform also allows users to explore new models and configurations without limits, extending their team with access to world-class AI engineers for guidance and support.

FriendliAI is a generative AI infrastructure company that offers efficient, fast, and reliable generative AI inference solutions for production. Their cutting-edge technologies enable groundbreaking performance improvements, cost savings, and lower latency. FriendliAI provides a platform for building and serving compound AI systems, deploying custom models effortlessly, and monitoring and debugging model performance. The application guarantees consistent results regardless of the model used and offers seamless data integration for real-time knowledge enhancement. With a focus on security, scalability, and performance optimization, FriendliAI empowers businesses to scale with ease.

Lunary is an AI developer platform designed to bring AI applications to production. It offers a comprehensive set of tools to manage, improve, and protect LLM apps. With features like Logs, Metrics, Prompts, Evaluations, and Threads, Lunary empowers users to monitor and optimize their AI agents effectively. The platform supports tasks such as tracing errors, labeling data for fine-tuning, optimizing costs, running benchmarks, and testing open-source models. Lunary also facilitates collaboration with non-technical teammates through features like A/B testing, versioning, and clean source-code management.

StreamDeploy is an AI-powered cloud deployment platform designed to streamline and secure application deployment for agile teams. It offers a range of features to help developers maximize productivity and minimize costs, including a Dockerfile generator, automated security checks, and support for continuous integration and delivery (CI/CD) pipelines. StreamDeploy is currently in closed beta, but interested users can book a demo or follow the company on Twitter for updates.

Knostic AI is an AI application that focuses on Copilot Readiness for Enterprise AI Security. It helps organizations locate and remediate data leaks from AI searches, ensuring data security and compliance. Knostic offers solutions to prevent data leakage, map knowledge boundaries, recommend permission adjustments, and provide independent verification of security posture readiness for AI adoption.

Krux AI is an advanced artificial intelligence tool designed to streamline and optimize various business processes. It leverages cutting-edge machine learning algorithms to provide actionable insights and predictions for data-driven decision-making. With its user-friendly interface and powerful capabilities, Krux AI empowers users to enhance efficiency, productivity, and profitability across different industries.

Portkey is a monitoring and improvement tool for Gen AI apps, helping teams enhance cost, performance, and accuracy. It integrates quickly, monitors LLM requests, and boosts app resilience, security, performance, and accuracy. The tool offers a product walkthrough and easy integration with OpenAI Python and Node libraries.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

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.

Instructor is a Python library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses. Get ready to supercharge your LLM workflows!

A package to help benchmark various LLM related tasks. The benchmarks are organized by end-to-end use cases, and utilize LangSmith heavily. We have several goals in open sourcing this: * Showing how we collect our benchmark datasets for each task * Showing what the benchmark datasets we use for each task is * Showing how we evaluate each task * Encouraging others to benchmark their solutions on these tasks (we are always looking for better ways of doing things!)

TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. Ollama is designed to be easy to use and accessible to developers of all levels. It is open source and available for free on GitHub.

OpenLLMetry is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application. Because it uses OpenTelemetry under the hood, it can be connected to your existing observability solutions - Datadog, Honeycomb, and others. It's built and maintained by Traceloop under the Apache 2.0 license. The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly.

Fast-LLM is an open-source library designed for training large language models with exceptional speed, scalability, and flexibility. Built on PyTorch and Triton, it offers optimized kernel efficiency, reduced overheads, and memory usage, making it suitable for training models of all sizes. The library supports distributed training across multiple GPUs and nodes, offers flexibility in model architectures, and is easy to use with pre-built Docker images and simple configuration. Fast-LLM is licensed under Apache 2.0, developed transparently on GitHub, and encourages contributions and collaboration from the community.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

ControlFlow is a Python framework designed for building agentic AI workflows. It provides a structured approach for defining tasks, assigning specialized AI agents, and orchestrating complex behaviors. By balancing AI autonomy with precise oversight, users can create sophisticated AI-powered applications with confidence. ControlFlow offers a task-centric architecture, structured results with type-safe outputs, specialized agents for efficient problem-solving, ecosystem integration with LangChain models, flexible control over workflows, multi-agent orchestration, and native observability and debugging capabilities.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

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