Search Results for "structure"
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Showing 22 open source projects for "structure"
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OpenClaw Chinese Translation
Open source personal AI assistant Chinese version
...It focuses on producing accurate and up-to-date translations of tutorials, API references, configuration guides, and explanatory materials so that learners don’t struggle with language barriers when working with the original project. The repository organizes translated articles, diagrams, and examples in a way that mirrors the structure of the original codebase, helping users correlate documentation with the actual implementation. It also includes localized explanations of conceptual topics such as agent reasoning, message handling, workflow design, and best practices. -
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OpenClaw CN
The Chinese version of OpenClaw
...It includes translated documentation, localized examples, and language-specific nuances so that developers in the Chinese ecosystem can adopt and contribute without a language barrier. The repository mirrors the structure of the upstream project but adds Chinese translations of core workflows, prompts, guidelines, and best practices for building multi-agent systems or AI applications. Beyond simple translation, the project often curates region-specific integrations or tooling recommendations that resonate with local developer environments and platforms. ... -
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agents.md
A simple, open format for guiding coding agents
...Instead of putting everything in README or doc files (which are more human-oriented and might mix high-level narrative), AGENTS.md is intended to surface agent-relevant details that help them “do the right thing” (tests, style, project structure, tooling). -
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Qwen Code
Qwen Code is a coding agent that lives in the digital world
Qwen Code is a command-line AI workflow tool designed to enhance developer productivity by leveraging the power of Qwen3-Coder models. Adapted from the Google Gemini CLI, it features an enhanced parser optimized specifically for Qwen-Coder models, enabling deep code understanding and manipulation. The tool supports querying and editing large codebases beyond traditional context limits, making it ideal for modern, complex projects. Qwen Code automates various development workflows, including... -
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CodeGraph
Pre-indexed code knowledge graph for Claude Code, Codex, Cursor
...Instead of forcing agents to repeatedly scan files with grep, glob, and read commands, it builds a searchable knowledge graph of symbols, relationships, call graphs, and code structure. The project uses deterministic parsing rather than LLM summaries, which makes its indexed data more predictable and grounded in the actual source tree. It is designed for tools such as Claude Code, Codex, Cursor, and OpenCode, with MCP support for agent access. CodeGraph stores project data locally, which helps reduce token usage and repeated file exploration. ... -
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html-ppt
AgentSkill with 24 themes, 31 layouts, 20+ animations
html-ppt-skill is an AI-oriented skill designed to generate presentation slides using HTML as the underlying structure instead of traditional PowerPoint formats. It enables users to create visually structured, web-based slide decks that can be rendered in browsers or converted into presentation formats. The system focuses on translating structured ideas into clean layouts, combining content organization with lightweight styling. It integrates well with AI workflows, allowing automated generation of presentations from prompts or documents. ... -
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PixelRAG
The beginning of scalable pixel-native search
...It renders web pages, PDFs, and images into screenshot tiles, then performs retrieval over those visual representations. This approach preserves layout, tables, charts, diagrams, infographics, and other visual structure that traditional HTML or text parsing can miss. The project includes tools for rendering, chunking, embedding, indexing, and serving visual search indexes. It also provides a hosted API with a prebuilt Wikipedia index, plus local pipelines for building indexes from custom documents. PixelRAG can be used with Claude through the pixelbrowse skill, giving agents the ability to inspect pages visually instead of relying only on raw markup. -
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OpenGame
Open Agentic Coding for Games
OpenGame is an open-source project aimed at providing a flexible framework or toolkit for building games, likely focusing on accessibility, modularity, and collaborative development. It appears designed to simplify the process of creating interactive experiences by offering reusable components and structured architecture for game logic, rendering, and input handling. The project likely supports experimentation with different gameplay mechanics and encourages customization through open-source... -
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Letta Code
The memory-first coding agent
...Unlike traditional session-based coding tools, Letta Code attaches a long-lived agent to a working directory so that the agent accumulates memory about a project’s structure, preferences, and history, effectively acting as a collaborative partner rather than a stateless helper. Users can initialize and connect the agent to various models, including popular large language models, and issue commands, refactor code, or ask context-aware questions directly in the terminal, with memory retained across multiple interactions. -
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9arm-skills
Agent skills loaded by Claude Code
...The productivity side provides reusable patterns for non-code tasks and daily workflows. 9arm-skills is most useful for users who want their AI agents to behave with more discipline, consistency, and context-specific structure. -
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Agently
AI Agent Application Development Framework
Build AI agent native application in very little code. Easy to interact with AI agents in code using structure data and chained-calls syntax. Enhance AI Agent using plugins instead of rebuilding a whole new agent. Agently is a development framework that helps developers build AI agent native applications really fast. You can use and build AI agents in your code in an extremely simple way. -
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effective-html
Agent skill for elegant and simple html plans, architecture diagrams
...The repository includes separate skills for standard HTML artifacts, full-screen HTML diagrams, and HTML plan pages. It is designed to work with agent tooling such as Skills, Claude Code plugins, and Codex plugins. The skill emphasizes strong visual structure, high-quality SVG use, minimal prose, and deliverables that can be opened, shared, or annotated. It is useful for teams that want AI agents to produce clearer visual explanations instead of plain text planning documents. -
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Agent Learning Hub
A curated AI Agent learning roadmap
Agent Learning Hub is a curated learning roadmap and resource collection for understanding AI agents. It is maintained as a central README-style knowledge hub rather than a software runtime or framework. The project organizes high-quality articles, official blogs, papers, open-source projects, engineering experience, and learning paths into a sequence that users can follow. It is especially useful for beginners and intermediate learners who want a structured entry point into the fast-moving... -
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Harmonist
Portable AI agent orchestration with mechanical protocol enforcement
...The project uses Python, has no runtime dependencies beyond the standard library, and is positioned as a drop-in agent coordination pack. Its purpose is to bring structure, review discipline, and repeatable process control to AI-assisted development. -
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Codebase to Course
A Claude Code skill that turns any codebase into an HTML course
...It is implemented as a skill for Claude Code and is designed to help users understand how a codebase works without requiring a formal computer science background. The tool analyzes the structure and behavior of a project and generates a visually rich, scroll-based course that includes diagrams, animations, and contextual explanations. It pairs real code with plain-English interpretations, allowing learners to follow execution flows and grasp concepts intuitively. The generated course also includes interactive quizzes and glossary tooltips to reinforce understanding through application rather than memorization. ... -
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Mantic.sh
A structural code search engine for Al agents
Mantic.sh is a context-aware, structural code search engine designed specifically for use with AI coding agents and developers who need deep, semantically relevant search across large codebases. Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that literal search might miss. It uses local embeddings and code graph awareness so that queries like “authentication flow” return not just superficially matching text but contextually related code across multiple repositories. ... -
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VoltAgent
Open Source TypeScript AI Agent Framework
An AI Agent Framework provides the foundational structure and tools needed to build applications powered by autonomous agents. These agents, often driven by Large Language Models (LLMs), can perceive their environment, make decisions, and take actions to achieve specific goals. Building such agents from scratch involves managing complex interactions with LLMs, handling state, connecting to external tools and data, and orchestrating workflows. -
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Khazix Skills
Digital Life Kazik Open Source AI Skills Collection
Khazix Skills project is an automation framework designed to transform GitHub repositories into structured, reusable AI agent skills. It acts as a pipeline that analyzes a repository’s metadata, extracts relevant information such as README content and commit hashes, and converts it into a standardized skill format that can be integrated into agent ecosystems. The system emphasizes lifecycle management by embedding versioning, traceability, and metadata directly into generated skill files,... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes. -
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Actionbook
Browser action engine for AI agents. 10× faster, resilient by design
Actionbook is an AI-centric automation framework that equips intelligent agents with the ability to interact with real live web pages in a reliable and scalable way, eliminating the guesswork involved in navigating modern dynamic sites. Instead of having agents blindly scrape HTML or blindly try to click things, Actionbook supplies up-to-date action manuals and verified DOM structure, letting agents know exactly how to click, type, and navigate complex interfaces such as SPAs or streaming UIs. This design makes browsing up to 10× faster and far more resilient than ad-hoc approaches that break on minor page changes, because the action manuals codify expected flows and DOM targets. It provides multiple integration paths — a Rust-based CLI, MCP server support for AI IDEs, and a JavaScript SDK — so developers can plug it into a wide range of agent pipelines and toolchains. -
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opc-methodology
The second edition of "One-person Enterprise Methodology"
...It also encourages iterative refinement, helping users improve prompt performance through systematic adjustments. Overall, OPC Methodology provides a practical approach to prompt engineering that prioritizes clarity, structure, and repeatability. -
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Multi-Agent Particle Envs
Code for a multi-agent particle environment used in a paper
...Scenarios are designed to model cooperative, competitive, and mixed interactions among agents, making it useful for testing algorithms in multi-agent settings. The project includes built-in scenarios such as navigation to landmarks, cooperative tasks, and adversarial setups. Although archived, its concepts and code structure remain foundational for more advanced libraries like PettingZoo, which extended and maintained this environment.
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