Awesome GitHub RepositoriesLLM Reasoning Workflows

Auto-GPT is an autonomous agent framework designed for creating and deploying AI agents that use large language models to plan and execute complex goals independently. The system provides a comprehensive environment for managing the entire agent lifecycle, from initial design and testing to live production deployment. The project features a low-code workflow designer that allows users to define agent behaviors by connecting functional blocks in a visual interface. It includes an agent marketplace for discovering and deploying pre-configured agent templates and a standardized evaluation tool t

This project provides an advanced English curriculum and a set of instructional guides designed to help non-native speakers move from intermediate to advanced proficiency. It functions as a guide for AI-powered language training, utilizing structured workflows and prompt engineering with large language models to facilitate self-directed study. The system implements AI workflow orchestration, chaining different artificial intelligence models into feedback loops to automate linguistic exercises and corrections. This approach combines multiple AI specializations to coordinate training across lis

Guidance is a control framework and generation orchestrator for large language models. It provides a programming layer to steer model outputs through structured templates, schema enforcement, and logical flow management. The framework distinguishes itself by interleaving model generation with local code execution, enabling the use of loops and conditional branching within a single session. It employs grammar-based token constraints and regular expressions to force models to sample only from tokens that satisfy a specific structural format, ensuring strict adherence to predefined data models.

PocketFlow is a graph-based framework for designing and executing large language model operations and reasoning patterns. It serves as an orchestrator for building goal-oriented autonomous agents, multi-agent systems, and retrieval-augmented generation pipelines. The system is distinguished by its ability to coordinate autonomous AI agents that use shared memory and tools to solve complex goals, supported by a structured output engine that enforces schema-consistent responses. It utilizes graph-based workflow orchestration to manage sequences of model operations and supports supervisor-based

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

This project provides a collection of reference implementations, architectural patterns, and SDK samples for building autonomous agents using large language models. It serves as a multi-language framework for implementing and deploying specialized AI agents across diverse programming environments. The system centers on an orchestration framework that combines deterministic code with adaptive reasoning through structured graph workflows. It utilizes schema-driven integration to connect agents with third-party applications and diverse AI models. The development lifecycle is supported by toolki

Wenda est une plateforme d'orchestration LLM et un moteur de workflow personnalisé conçu pour gérer plusieurs backends de modèles de langage via une interface unifiée. Il fonctionne comme une passerelle IA auto-hébergée qui permet l'exécution de séquences de tâches complexes et de flux de conversation automatisés. Le système utilise des plugins JavaScript pour orchestrer les workflows et déclencher des appels API externes. Il prend en charge la génération augmentée par récupération (RAG) en injectant des données pertinentes provenant de bases de données vectorielles et de fichiers hors ligne dans les prompts pour augmenter la précision des réponses. La plateforme est conçue pour des déploiements sur réseau privé, avec une gestion des accès multi-utilisateurs et la capacité d'exécuter des modèles open source quantifiés pour s'adapter à des contraintes matérielles spécifiques. Elle inclut également un suivi d'historique basé sur les sessions pour maintenir le contexte conversationnel.

This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

OpenSquilla est un framework d'orchestration d'agents LLM conçu pour coordonner des workflows IA multi-étapes et l'exécution d'outils via des graphes orientés acycliques (DAG). Il fonctionne comme un système centralisé pour gérer des packages de compétences spécialisés et exécuter des séquences de raisonnement complexes. Le projet se distingue par une passerelle de routage qui dirige les tâches vers différents fournisseurs d'IA en fonction de la complexité, du coût et de la performance. Il utilise un système de mémoire IA à plusieurs niveaux qui organise les connaissances de travail, épisodiques et sémantiques à l'aide d'embeddings locaux et de SQLite, ainsi qu'un bac à sable d'exécution sécurisé qui isole le code généré par l'agent via des profils de permission basés sur les risques. La plateforme couvre un large éventail de capacités, incluant le déploiement multicanal vers le web et les plateformes de messagerie, la planification automatisée des tâches via cron, et un pont Model Context Protocol pour se connecter à des outils externes. Elle fournit également des outils complets de surveillance et d'observabilité pour suivre les coûts en jetons, auditer les décisions d'exécution et gérer un catalogue de compétences réutilisables. Le système inclut des utilitaires en ligne de commande pour l'initialisation de l'espace de travail et la gestion du cycle de vie des compétences.