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 एक LLM ऑर्केस्ट्रेशन प्लेटफ़ॉर्म और कस्टम वर्कफ़्लो इंजन है जिसे एक एकीकृत इंटरफ़ेस के माध्यम से कई लैंग्वेज मॉडल बैकएंड को मैनेज करने के लिए डिज़ाइन किया गया है। यह एक सेल्फ-होस्टेड AI गेटवे के रूप में कार्य करता है जो जटिल टास्क सीक्वेंस और ऑटोमेटेड कन्वर्सेशन फ़्लो के निष्पादन को सक्षम बनाता है। सिस्टम वर्कफ़्लो को ऑर्केस्ट्रेट करने और बाहरी API कॉल को ट्रिगर करने के लिए JavaScript प्लगइन्स का उपयोग करता है। यह रिस्पॉन्स सटीकता बढ़ाने के लिए प्रॉम्प्ट्स में वेक्टर स्टोर्स और ऑफ़लाइन फ़ाइलों से प्रासंगिक डेटा इंजेक्ट करके रिट्रीवल ऑगमेंटेड जनरेशन (RAG) का समर्थन करता है। प्लेटफ़ॉर्म को प्राइवेट नेटवर्क डिप्लॉयमेंट के लिए बनाया गया है, जिसमें मल्टी-यूज़र एक्सेस मैनेजमेंट और विशिष्ट हार्डवेयर बाधाओं के भीतर फिट होने के लिए क्वांटाइज़्ड ओपन सोर्स मॉडल चलाने की क्षमता शामिल है। इसमें कन्वर्सेशनल कॉन्टेक्स्ट बनाए रखने के लिए सेशन-आधारित हिस्ट्री ट्रैकिंग भी शामिल है।

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 is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution using directed acyclic graphs. It functions as a centralized system for managing specialized skill packages and executing complex reasoning sequences. The project distinguishes itself through a routing gateway that directs tasks to different AI providers based on complexity, cost, and performance. It utilizes a multi-tier AI memory system that organizes working, episodic, and semantic knowledge using local embeddings and SQLite, alongside a secure execution sandbox that isolat