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Tools for defining and managing agent metadata, system prompts, and configuration settings.
Explore 36 awesome GitHub repositories matching artificial intelligence & ml · Agent Configuration Tools. Refine with filters or upvote what's useful.
Openclaw 是一个用于管理智能体(Agent)执行环境的平台,提供控制智能体生命周期、会话状态和工作区持久化的基础设施。它具有一个处理模型循环、工具调用和流式事件的中心化网关,同时支持多智能体路由和持久化内存管理。该系统旨在规范工具执行签名,并为跨提供商兼容性提供标准化接口。 该平台包括广泛的开发者工具,例如用于工作区管理的命令行界面、诊断日志记录以及允许注册自定义工具和功能的插件架构。它通过事件驱动的钩子、任务调度和与外部服务的集成来支持自动化工作流。安全性通过执行策略、凭据可移植性和智能体操作的审批工作流进行管理。 部署通过自动化基础设施安装程序和容器化网关助手提供支持,并内置了用于备份和配置管理的实用程序。该系统为编排多步工作流提供了结构化格式,并包括用于浏览器自动化和结构化代码补丁的专用工具。
Enables the definition of agent metadata, system prompts, and configuration settings through workspace-specific files.
This project is a community-driven knowledgebase and registry for AI agent configurations. It serves as a centralized repository for system prompts, environment settings, and integration strategies designed to standardize the behavior of various AI-assisted development tools. By capturing these configurations in a structured format, the project enables developers to maintain consistent AI agent performance across different workstations and environments. The repository distinguishes itself through a hierarchical, version-controlled architecture that treats prompt engineering patterns as portab
Optimizes prompt and configuration patterns for AI agents operating within the VSCode development ecosystem.
Ant Design is an enterprise-grade component library and design system framework built for developing complex, data-heavy web applications. It provides a comprehensive collection of pre-built, state-driven interface elements that map data properties to rendered components, ensuring consistent interaction patterns and visual language across large-scale projects. The library distinguishes itself through a robust styling architecture that utilizes design tokens and hierarchical configuration providers to propagate global settings like themes, locale, and layout direction. By employing component-l
Defines standardized formats for embedding design system knowledge into AI-powered coding assistants and development environments.
LobeHub is a comprehensive multi-agent orchestration platform designed for building, configuring, and deploying specialized AI agents. It provides a unified chat-based gateway that allows users to manage autonomous agent teams across web, desktop, and mobile environments. By utilizing a framework that supports persistent memory and granular tool integration, the platform enables the execution of complex, multi-step workflows and domain-specific tasks. The platform distinguishes itself through an interactive artifact renderer that injects dynamic, visual UI elements directly into the chat stre
Curate documentation and guides to help users locate and evaluate the efficacy of specialized agents.
Caveman is a set of tools and configurations designed for large language model token optimization. It focuses on reducing the amount of data processed during AI interactions to lower costs and maximize the available context window. The project implements a fragmented communication style that replaces full grammatical sentences with concise technical keywords. This approach extends to AI context optimization by condensing memory files and tool descriptions, and includes a specialized configuration for generating terse, one-line code reviews and short conventional commit messages. The system i
Optimizes the Claude Code CLI experience by compressing tool descriptions and communication styles.
Graphify is a knowledge retrieval system that transforms directories of source code and documentation into structured, queryable project maps. It utilizes a code-to-graph parser to extract technical metadata and system connectivity, converting a mix of code, SQL schemas, and documentation into a unified graph structure. The project distinguishes itself by integrating these knowledge graphs with AI coding assistants through a Model Context Protocol server and dedicated tool hooks. This allows AI agents to perform lookups and impact analysis on node neighbors and shortest paths to understand ho
Provides configuration profiles and tool hooks to prompt AI agents to query the knowledge graph before accessing source files.
This project is an LLM research workflow framework and academic writing automation tool designed to coordinate the research, drafting, and peer-review processes of scholarly papers. It functions as a scientific manuscript auditor and an AI peer review system that uses multi-agent evaluation to verify citation integrity and score manuscripts against quality rubrics. The system distinguishes itself through a verification suite that employs vision models for figure fidelity auditing and anchor links for claim support verification. It includes a writing style calibration utility that analyzes pre
Manages a structured research to finalization process using Claude Code plugins and configurations.
This project is a build orchestration engine and development toolkit designed for managing large-scale monorepos. It provides a unified workspace environment that maps project relationships and dependencies, enabling the system to perform intelligent impact analysis and execute only the tasks affected by specific code changes. The system distinguishes itself through a persistent daemon that monitors file changes for near-instant feedback and a content-addressable caching mechanism that stores task outputs to prevent redundant computation across local and remote environments. It further suppor
Provides configuration formats for integrating AI coding assistants into the monorepo development workflow.
Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai
Provides tools to package system prompts, instructions, and tool links into deployable agent repositories.
Theia is a modular framework designed for building professional-grade development environments that function as both local desktop applications and remote browser-based services. It provides a comprehensive toolkit for constructing specialized coding tools, allowing developers to assemble custom interfaces and backend logic through a flexible, contribution-based architecture. The platform distinguishes itself through a highly extensible workbench that supports the integration of existing third-party editor plugins and standard language servers. By utilizing a dependency injection container an
Enables or disables specific AI assistants and configures their settings and model assignments through a centralized interface.
This project is a modular, open-source customer relationship management platform built on the Laravel framework. It serves as a comprehensive business application framework designed for tracking sales pipelines, managing business entities, and automating marketing workflows. By providing a self-hosted solution, it enables organizations to maintain full control over their contact data, sales leads, and communication history. The platform distinguishes itself through a highly extensible architecture that allows developers to modify core behavior without altering the underlying source code. It u
Provides specialized instructions to AI tools for generating project-specific code structures via standardized configuration files.
Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut
Decouples agent instructions and tool definitions from code for external configuration.
huashu-design is a design system infrastructure and a set of specialized design engines for high-fidelity HTML prototyping, quality evaluation, and presentation conversion. It provides tools for generating interactive single-file HTML mockups, frame-based motion design, and a visual evaluator that analyzes design quality across five dimensions using radar charts. The system distinguishes itself through a translation pipeline that converts HTML slide decks into editable PowerPoint and PDF objects rather than flat images. It includes a motion design engine that uses a time-slice model to render
Provides a specialized skill for Claude Code to generate high-fidelity HTML prototypes, presentations, and animations.
This project is a Kubernetes-based cloud IDE platform that provisions and manages containerized development environments accessible via a web browser. It functions as a multi-tenant developer platform, enabling teams to launch standardized workspaces directly from Git repositories. The platform implements development environments as code, using declarative YAML configurations and DevContainer-compatible specifications to define toolsets, IDE settings, and runtime dependencies. This ensures repeatable setups through reusable environment templates and standardized workspace stacks, allowing for
Defines AI providers and coding assistant tools through registry configurations.
Airweave is a unified AI knowledge base platform that syncs data from external APIs into a searchable layer for retrieval-augmented generation. It provides a pre-built data connector library and a framework for building custom connectors, enabling the extraction, transformation, and synchronization of structured and unstructured data from SaaS applications. The platform includes a hybrid vector retrieval system that combines semantic, neural, and keyword search strategies to deliver grounded context for AI agents. The platform distinguishes itself through an agentic search engine that iterati
Allows setting a custom base URL for self-hosted instances in MCP settings.
Claude Squad is a terminal-based orchestrator for running multiple AI coding assistants in parallel. It manages the lifecycle of AI agent sessions from a single keyboard-driven interface, allowing users to launch, monitor, pause, resume, and terminate agents without leaving the command line. The tool isolates each agent's work in separate git worktrees, so changes remain on independent branches and never interfere with each other. Before any modifications are committed or pushed, users can review a diff preview of what each agent produced and approve or reject the changes. This diff-based app
Ships a profile launcher that starts different AI coding assistants with user-defined shell commands.
Provides typed agent definitions that bundle roles, tools, memory, and model providers into one configuration.
xcodebuildmcp is a Model Context Protocol server that exposes Xcode build, test, and device management tools for AI coding agents to automate iOS and macOS development workflows. It operates as a background daemon per workspace, communicating tool requests and responses over standard input/output using JSON-RPC messages, and streams progress and results as newline-delimited JSON objects for machine parsing. The project provides an interactive setup wizard and file-based client configuration to install skill files into predefined directories for supported AI coding clients. It manages the full
Configures MCP servers across multiple AI coding clients with automated skill installation and client integration.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Centralizes and validates agent service requests to enforce strict internal action boundaries.
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Provides structured, schema-validated service invocations for agents to ensure predictable argument and result handling.