Clyren

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.

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 is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set.

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.

It is an open-source library/framework for building autonomous language agents. The library is carefully engineered to support important features including long-short term memory, tool usage, web navigation, multi-agent communication, and brand new features including human-agent interaction and symbolic control.

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 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 is an AI-powered task management system. The system uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks.

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

It is a new scripting language to automate your interaction with a Large Language Model (LLM), namely OpenAI. The ultimate goal is to create a natural language programming experience. The syntax of GPTScript is largely natural language, making it very easy to learn and use.

It is a code-first agent framework for seamlessly planning and executing data analytics tasks. It interprets user requests through coded snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics tasks.

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It is the toolkit for evaluating and developing robust and reliable AI agents. Build compliant virtual employees with observability, evals, and replay analytics. No more black boxes and prompt guessing.

It is a library of modular components and an orchestration framework. Inspired by a microservices approach, it gives developers all the components they need to build robust, stable & reliable AI applications and experimental autonomous agents.

It is a platform for building AI Agents. It’s designed to be simple to use, but powerful enough to build complex Agents. Our SDK enables you to build Agents in Python, and our CLI makes it easy to deploy them to the cloud.

It lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Its purpose is to make it easy to build and experiment with both existing and entirely new Controller strategies for improving LLM generations.

Framework for writing Crossref Event Data agents.

It offers a versatile framework that streamlines the process of creating custom multi-agent environments for large language models (LLMs).