Awesome Claude Design
Section: External catalogs · official DESIGN.md spec from Google Labs Code, Apache 2.0; see docs/spec.md
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Appears in 4 awesome lists
DESIGN.md是一套专为AI编程代理设计的视觉标识描述规范,它通过提供持久且结构化的设计系统上下文,精准解决了大语言模型在自动化生成代码时因缺乏统一设计规范而导致的界面风格漂移与重复沟通痛点。该项目相较于传统工具的核心优势在于其标准化架构能够彻底消除多智能体协作中的认知碎片化,凭借内置的层级化索引机制实现设计规则的长期记忆而非短暂会话存储,同时依托原生Markdown语法实现了无需额外解析器的无缝工程集成,从而在易用性与稳定性上显著优于依赖私有格式的竞品方案。在底层运作逻辑上,该规范犹如为编程代理配备了一本可随时查阅的“数字化建筑蓝图”,它将抽象的色彩、字体与组件规范转化为带标题层级的树…
Section: External catalogs · official DESIGN.md spec from Google Labs Code, Apache 2.0; see docs/spec.md
Section: Context Delivery & Compaction · Google Labs' specification for describing visual identity systems to coding agents: machine-readable design tokens (YAML front matter) combined with human-readable design rationale (markdown prose) give agents a persistent, structured understanding of design constraints without requiring custom…
Section: 4. Agentic AI & Multi-Agent Systems · A format specification for describing visual identity to coding agents, combining YAML tokens and markdown prose to give agents a structured understanding of design systems. Apache 2.0 licensed.
Section: 大语言对话模型及数据 · DESIGN.md是一套专为AI编程代理设计的视觉标识描述规范,它通过提供持久且结构化的设计系统上下文,精准解决了大语言模型在自动化生成代码时因缺乏统一设计规范而导致的界面风格漂移与重复沟通痛点。该项目相较于传统工具的核心优势在于其标准化架构能够彻底消除多智能体协作中的认知碎片化,凭借内置的层级化索引机制实现设计规则的长期记忆而非短暂会话存储,同时依托原生Markdown语法实现了无需额外解析器的无缝工程集成,从而在易用性与稳定性上显著优于依赖私有格式的竞品方案。在底层运作逻辑上,该规范犹如为编程代理配备了一本可随时查阅的“数字化建筑蓝图”,它将抽象的色彩、字体与组件规范转化为带标题层级的树…
Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube github | docs
February 2026 release making human oversight a native workflow primitive: suspend execution at critical decision points, expose review-and-edit UI mid-flow, and route subsequent execution based on human action (approve/reject/escalate). Demonstrates how HITL transitions from bolt-on approval gates…
Mem0 is an intelligent memory layer for Large Language Models that enhances personalized AI experiences by retaining and utilizing contextual information across various applications. github | website | docs | discord | twitter | github profile | linkedin
Microsoft's multi-agent conversation framework with a complete AgentChat layer covering agent loop, tool integration, termination conditions, and human-in-the-loop. The most comprehensive open-source reference for large-scale multi-agent harness design.
June 2026 harness-first redesign built around the Capability primitive: a single composable unit bundling instructions, tools, lifecycle hooks, and model settings. The split between a small stable core and a fast-moving pydantic-ai-harness lets capabilities graduate as they prove essential, while…
(MIT) is a framework for algorithmically optimizing LM prompts and weights. DSPy introduced typed predictor and signatures to leverage Pydantic for enforcing type constraints on inputs and outputs, improving upon string-based fields.
Semantic Kernel 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. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of…
Minimal browser-automation agent harness with clean separation of tool registration, DOM state injection, action loop, and error recovery. Small codebase, clear structure — the best "minimal viable harness" reference for understanding core loop mechanics.