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 是一个 LLM 编排平台和自定义工作流引擎，旨在通过统一界面管理多个语言模型后端。它充当自托管 AI 网关，能够执行复杂的任务序列和自动化对话流。 该系统利用 JavaScript 插件来编排工作流并触发外部 API 调用。它通过将来自向量存储和离线文件的相关数据注入提示词来支持检索增强生成，从而提高响应准确性。 该平台专为私有网络部署而构建，具有多用户访问管理功能，并能够运行量化的开源模型以适应特定的硬件约束。它还包括基于会话的历史跟踪，以保持对话上下文。

这是一个关于使用 PyTorch 构建神经网络的综合教学资源和课程。它涵盖了深度学习的基本构建块，包括张量操作、自动微分以及模块化神经网络组件的构建。 该仓库是多个专业领域的参考指南。它提供了计算机视觉任务（如图像分类、目标检测和语义分割）的实现细节，以及涉及 Transformer、循环网络和生成模型的自然语言处理工作流。此外，它还包括生成式 AI 的参考资料，专门关注通过扩散模型和对抗网络进行图像合成。 材料延伸至模型优化和部署流水线。它涵盖了通过量化和将模型导出为 ONNX 和 TensorRT 等格式来减小模型大小并提高推理速度的技术。其他能力领域包括用于并行加载的数据工程、使用自定义指标的模型评估，以及开源大语言模型的部署。 该项目主要以一系列 Jupyter Notebook 的形式提供。

OpenSquilla 是一个 LLM 智能体编排框架，旨在利用有向无环图协调多步 AI 工作流和工具执行。它作为一个集中式系统，用于管理专门的技能包并执行复杂的推理序列。 该项目通过一个路由网关脱颖而出，该网关根据复杂性、成本和性能将任务定向到不同的 AI 提供商。它利用多层 AI 记忆系统，通过本地嵌入和 SQLite 组织工作、情景和语义知识，并配有一个安全执行沙盒，通过基于风险的权限配置文件隔离智能体生成的代码。 该平台涵盖了广泛的功能，包括多渠道部署到 Web 和消息平台、通过 cron 进行自动任务调度，以及用于连接外部工具的 Model Context Protocol 网桥。它还提供全面的监控和可观测性工具，用于跟踪 Token 成本、审计运行时决策以及管理可重用技能目录。 该系统包括用于工作区初始化和技能生命周期管理的命令行工具。