LLMStack

It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It is an open-source library designed to help developers build conversational streaming user interfaces in JavaScript and TypeScript. The SDK supports React/Next.js, Svelte/SvelteKit, and Vue/Nuxt as well as Node.js, Serverless, and the Edge Runtime.

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It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.

It empowers teams to easily create powerful bots using a guided, no-code graphical interface without the need for data scientists or developers. It addresses many of the major issues with bot building in the industry today. It eliminates the gap between the subject matter experts and the development teams building the bots, and the long latency between teams recognizing an issue and updating the bot to address it. It removes the complexity of exposing teams to the nuances of conversational AI and the need to write complex code. And, it minimizes the IT effort required to deploy and maintain a custom conversational solution.

It is an open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.

It is a Rust ecosystem of libraries for running inference on large language models, inspired by llama.cpp. On top of llm, there is a CLI application, llm-cli, which provides a convenient interface for running inference on supported models.

It is an open-source, drag & drop UI to build your customized LLM flow. It is built on top of LangChainJS, with the aim to make it easy for people to visualize and build LLM apps.

It is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.

It is the easiest way for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders.

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It is an easy to use desktop app for experimenting with local and open-source Large Language Models (LLMs). It supports any ggml Llama, MPT, and StarCoder model on Hugging Face (Llama 2, Orca, Vicuna, Nous Hermes, WizardCoder, MPT, etc.)

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It is an open-source product analytics suite for LLM-based applications. Iterate faster on your application with a granular view of exact execution traces, quality, cost, and latency.

It aims to enable developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable.

It is a full-stack application and tool suite that enables you to turn any document, resource, or piece of content into a piece of data that any LLM can use as reference during chatting. This application runs with very minimal overhead as by default the LLM and vectorDB are hosted remotely, but can be swapped for local instances.

It is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, it empowers agents to work together seamlessly, tackling complex tasks.

Transform basic prompts into expert-level AI instructions. Enhance, benchmark & optimize prompts for ChatGPT, Claude, Gemini & more.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.

X is an AI voice and chat assistant that automates customer support, lead generation, and engagement across websites, CRMs, and WhatsApp

It is a chat interface that lets you interact with Ollama. It offers features such as code syntax highlighting, Markdown and LaTeX support, local RAG integration, and prompt preset support. It can be installed using Docker or Kubernetes.

It is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation.

It is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. It achieves this by allowing you to define plugins that can be chained together in just a few lines of code.

It is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). It unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools.

Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT, etc.). It offers production-ready tools to build NLP backend services, e.g., question answering or semantic search.

It is an autonomous AI Agent platform that empowers users to create and deploy customizable autonomous AI agents directly in the web. Simply assign a name and goal to your AI agent, and watch as it embarks on an exciting journey to accomplish the assigned objective.

It provides all you need to build and deploy computer vision models, from data annotation and organization tools to scalable deployment solutions that work across devices.

Oculer is an end-to-end AI video engine that turns a simple text idea into a fully produced Instagram Reel, or YouTube Short. Unlike basic motion graphic tools, Oculer creates complete storytelling videos — including script, storyboard, motion graphics, sound syncing, subtitles, and final editing — automatically.

Is an all-in-one AI coding platform that allows you build apps and websites by chatting with AI. YouWare enables full-stack code generation and deployment with a shareable URL instantly. no code, no setup, no hassle.

It is an open-source monitoring & observability for AI apps and agents. It is designed to be usable with any model, not just OpenAI. It is easy to integrate and simple to self-host.

It is a light package to simplify calling OpenAI, Azure, Cohere, Anthropic, Huggingface API Endpoints. Manages input/output translation.

It is a lightning-fast inference platform that helps you serve your large language models (LLMs). Use a state-of-the-art, open-source model or fine-tune and deploy your own at no additional cost.

It is an open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. Run inference with any open-source large-language models, deploy to the cloud or on-premise, and build powerful AI apps.

It is a powerful generative large language model that is designed to improve search accuracy and provide personalized recommendations. It is capable of performing a range of generative AI tasks, including text summarization, and text generation, etc.

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It is a dev-first open-source autonomous AI agent framework that enables developers to build, manage & run useful autonomous agents quickly and reliably.

It is designed to provide a flexible framework to define and deploy large language model apps without having to write any execution code.

It is a library for creating semantic cache for LLM queries. Slash your LLM API costs by 10x, and boost speed by 100x.

It is a general video interaction platform based on large language models. Build a chatbot for video understanding, processing, and generation.

It is a tool that enables fast and efficient local LLM finetuning. It uses a manual autograd engine and Flash Attention v2 to achieve 2-5x speedup and 50% memory reduction compared to QLoRA, without compromising accuracy.

It is the easiest way to customize and serve LLMs. In LLM Engine, models can be accessed via Scale's hosted version or by using the Helm charts in the repository to run model inference and fine-tuning in your own infrastructure.

It is a simple-to-use, open-source evaluation framework for LLM applications. It is similar to Pytest but specialized for unit testing LLM applications. It evaluates performance based on metrics such as hallucination, answer relevancy, RAGAS, etc., using LLMs and various other NLP models locally on your machine.

It is the interface between your app and hosted LLMs. It streamlines API requests to OpenAI, Anthropic, Mistral, LLama2, Anyscale, Google Gemini, and more with a unified API.

It is a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs).

It is a Multi-agent Meta programming framework that assigns different roles to GPTs to form a collaborative software entity for complex tasks. It takes a one-line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.

It is a self-hardening firewall for large language models. Protect your models and your users from adversarial attacks: prompt injections, prompt and PII leakage, toxic language, and more!

It is a framework to easily create LLM powered bots over any dataset. It abstracts the entire process of loading a dataset, chunking it, creating embeddings, and then storing it in a vector database.

It is a low code platform to rapidly annotate data, train and then deploy custom Natural Language Processing (NLP) models. It takes care of model training, data selection and deployment for you. You upload your data and we provide an annotation interface for you to teach a classifier. As you label we train a model, work out what data is most valuable and then deploy the model for you.

It enables LLMs to use tools by invoking APIs. Given a natural language query, it comes up with the semantically and syntactically correct API to invoke.