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44 个仓库

Awesome GitHub RepositoriesDevelopment and Runtime Environments

Provides the foundational infrastructure, IDEs, and sandboxed environments required to build, test, and deploy autonomous agentic systems.

Explore 44 awesome GitHub repositories matching artificial intelligence & ml · Development and Runtime Environments. Refine with filters or upvote what's useful.

Awesome Development and Runtime Environments GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • kamranahmedse/developer-roadmapkamranahmedse 的头像

    kamranahmedse/developer-roadmap

    357,434在 GitHub 上查看↗

    Developer Roadmap 是一个社区驱动的平台,提供结构化的、基于图谱的软件工程学习路径。它作为一个综合知识仓库,将技术领域组织成可视化序列,以指导专业技能获取和职业成长。 该项目通过协作生态系统脱颖而出,使用户能够贡献路线图、策划行业最佳实践并维护个人职业档案。它集成了诊断评估框架来评估技术熟练度,帮助开发者识别知识缺口,并通过有针对性的学习序列为专业面试做准备。 除了核心映射能力外,该平台还提供实用的项目创意和交互式辅导,以巩固工程概念。它为社区提供了一个共享资源、跟踪技能进步和导航复杂技术领域的中心化空间。

    Executes autonomous agents in dedicated, isolated environments to minimize potential security risks.

    TypeScriptangular-roadmapbackend-roadmapblockchain-roadmap
    在 GitHub 上查看↗357,434
  • awesome-selfhosted/awesome-selfhostedawesome-selfhosted 的头像

    awesome-selfhosted/awesome-selfhosted

    299,516在 GitHub 上查看↗

    这是一个由社区策划的开源软件目录,专为在私有服务器环境和家庭实验室中部署而设计。它作为发现主流云服务独立自托管替代方案的综合资源,使用户能够保持对数字基础设施的完全数据所有权和控制权。 该目录通过层级分类法构建,将庞大的应用程序集合组织成逻辑类别,范围从媒体管理和数据分析到私有通信和团队生产力工具。它通过协作同行评审流程脱颖而出,社区成员验证每个提交的质量和相关性,以确保目录保持准确和可靠。 该项目涵盖了广泛的能力领域,包括基础设施自动化、基于容器的服务部署和声明式配置管理。这些工具协助用户维护可复现的服务器环境,并管理私有硬件上的复杂服务依赖。 该目录作为版本控制仓库进行维护,确保所有更新和社区驱动的变更都是可追踪且透明的。

    Provides an integrated environment for building and running AI applications with support for retrieval-augmented generation.

    awesomeawesome-listcloud
    在 GitHub 上查看↗299,516
  • anthropics/skillsanthropics 的头像

    anthropics/skills

    151,506在 GitHub 上查看↗

    This project provides a standardized framework for extending the functional range of artificial intelligence agents through a registry of modular, declarative instructions. It enables agentic workflow automation by allowing developers to define task-specific behaviors and operational constraints that guide how agents interact with external tools and execute multi-step processes. The system distinguishes itself through a directory-based discovery model and a plugin-registry architecture that facilitates the distribution of specialized workflows. By utilizing a schema-driven specification that

    Maintains a central directory of functional modules that agents can discover and utilize to perform specialized tasks.

    Pythonagent-skills
    在 GitHub 上查看↗151,506
  • x1xhlol/system-prompts-and-models-of-ai-toolsx1xhlol 的头像

    x1xhlol/system-prompts-and-models-of-ai-tools

    141,061在 GitHub 上查看↗

    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

    Streamlines the management of task-tracking configurations and workflows for AI agents.

    aiboltcluely
    在 GitHub 上查看↗141,061
  • anthropics/claude-codeanthropics 的头像

    anthropics/claude-code

    132,728在 GitHub 上查看↗

    Anthropic's terminal-native AI coding agent.

    Interprets natural language commands to independently modify code, execute test suites, and manage project file structures.

    Pythonaiclideveloper-tools
    在 GitHub 上查看↗132,728
  • shubhamsaboo/awesome-llm-appsShubhamsaboo 的头像

    Shubhamsaboo/awesome-llm-apps

    114,725在 GitHub 上查看↗

    This repository serves as a comprehensive collection of resources, templates, and starter code for building artificial intelligence applications. It provides a centralized hub for developers to access practical implementations of common workflows, including retrieval-augmented generation pipelines and autonomous agent loops, alongside educational materials designed to support rapid prototyping and experimentation. The project distinguishes itself by offering a dual focus on technical implementation and critical analysis. It provides a library of lightweight, single-file agents and tutorials f

    Deployment guides assist in hosting and maintaining persistent, self-improving agents that operate continuously.

    Pythonagentsllmspython
    在 GitHub 上查看↗114,725
  • openhands/openhandsOpenHands 的头像

    OpenHands/OpenHands

    77,330在 GitHub 上查看↗

    OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It

    Connects agents to external databases and development tools using standardized protocols with configurable safety and approval policies.

    Pythonagentartificial-intelligencechatgpt
    在 GitHub 上查看↗77,330
  • addyosmani/agent-skillsaddyosmani 的头像

    addyosmani/agent-skills

    60,849在 GitHub 上查看↗

    Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated

    Deploys targeted sub-agent definitions to perform domain-specific reviews and task delegations.

    Shellagent-skillsantigravityantigravity-ide
    在 GitHub 上查看↗60,849
  • datawhalechina/hello-agentsdatawhalechina 的头像

    datawhalechina/hello-agents

    59,685在 GitHub 上查看↗

    This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the construction of systems that utilize language models to plan, manage memory, and execute multi-step tasks through iterative reasoning loops and tool-based actions. The framework distinguishes itself by offering specialized capabilities for interacting with graphical user interfaces and legacy software, allowing agents to perceive visual elements and perform actions like a human user. It supports complex, cross-application workflows through graph-based orchestration and provid

    Provides a centralized registry for defining and distributing functional tools to autonomous agents.

    Pythonagentllmrag
    在 GitHub 上查看↗59,685
  • notifirehq/notifirenotifirehq 的头像

    notifirehq/notifire

    39,138在 GitHub 上查看↗

    Notifire is a multi-channel notification infrastructure designed to route and dispatch alerts across email, SMS, push, and chat providers through a unified interface. It functions as an agent communication gateway that normalizes inbound and outbound messages between chat platforms and AI agents for consistent data processing. The system includes a notification workflow engine that uses branching conditions and batching capabilities to design delivery sequences and reduce user fatigue. It also provides a pre-built notification center component, allowing web applications to embed a real-time i

    Routes user messages from various chat platforms to an AI agent and delivers the response back.

    TypeScript
    在 GitHub 上查看↗39,138
  • github/awesome-copilotgithub 的头像

    github/awesome-copilot

    35,119在 GitHub 上查看↗

    Awesome Copilot is a comprehensive framework for autonomous software development, providing the infrastructure to orchestrate multi-agent teams and automate complex coding workflows. It functions as a centralized platform for managing AI-driven development, enabling developers to deploy specialized agents that interact with local files, terminal commands, and external APIs to execute end-to-end software delivery tasks. The project distinguishes itself through its focus on governance and extensibility, offering a suite of security controls, policy-based execution guardrails, and audit trails t

    Initializes repository-specific instructions, selects specialized agents, and configures external server connections to extend assistant capabilities.

    Pythonaigithub-copilothacktoberfest
    在 GitHub 上查看↗35,119
  • langchain-ai/deepagentslangchain-ai 的头像

    langchain-ai/deepagents

    25,006在 GitHub 上查看↗

    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 a containerized runtime and cloud environment for hosting AI graphs with integrated secrets and scaling.

    Pythonagentsdeepagentslangchain
    在 GitHub 上查看↗25,006
  • a2aproject/a2aa2aproject 的头像

    a2aproject/A2A

    24,404在 GitHub 上查看↗

    A2A is a standardized framework designed to enable interoperability, discovery, and orchestration among independent artificial intelligence agents. It provides a common communication protocol that allows heterogeneous agents to exchange data, verify identities, and collaborate across diverse programming languages and computing environments. By establishing a unified messaging standard, the project facilitates the creation of complex, multi-agent workflows where tasks are routed and managed between specialized services. The project distinguishes itself through a capability-based architecture t

    A discovery service that exposes agent capabilities, metadata, and service endpoints through standardized digital cards for automated service selection.

    Shella2aa2a-mcpa2a-protocol
    在 GitHub 上查看↗24,404
  • mastra-ai/mastramastra-ai 的头像

    mastra-ai/mastra

    21,221在 GitHub 上查看↗

    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

    Defines default memory, filesystem, and sandbox configurations to ensure consistent execution environments for all created agents.

    TypeScriptagentsaichatbots
    在 GitHub 上查看↗21,221
  • letta-ai/lettaletta-ai 的头像

    letta-ai/letta

    21,168在 GitHub 上查看↗

    Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

    Allows updating the configuration and definition of registered tools within the agent environment.

    Pythonaiai-agentsllm
    在 GitHub 上查看↗21,168
  • vercel-labs/agent-skillsvercel-labs 的头像

    vercel-labs/agent-skills

    20,764在 GitHub 上查看↗

    Agent Skills is a centralized registry and management system designed for the discovery, auditing, and integration of reusable procedural modules into automated agent workflows. It provides a structured environment for sourcing verified capabilities that extend the functional range of AI agents, enabling the development and scaling of complex, multi-step automated processes. The platform distinguishes itself through a security-first approach to module integration, utilizing audit-verified data to ensure that capabilities meet safety requirements before they are deployed. It incorporates a dec

    Provides a centralized registry for discovering, auditing, and integrating reusable procedural modules into automated agent workflows.

    JavaScript
    在 GitHub 上查看↗20,764
  • claude-code-best/claude-codeclaude-code-best 的头像

    claude-code-best/claude-code

    20,272在 GitHub 上查看↗

    Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level

    Tracks and manages active coding environments and AI agent sessions through a centralized server.

    TypeScript
    在 GitHub 上查看↗20,272
  • livekit/livekitlivekit 的头像

    livekit/livekit

    19,358在 GitHub 上查看↗

    LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it

    Provides back-end systems and registries that support the deployment, integration, and tracking of AI agents.

    Gogolangmedia-serversfu
    在 GitHub 上查看↗19,358
  • camel-ai/camelcamel-ai 的头像

    camel-ai/camel

    17,253在 GitHub 上查看↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Provides registries for searching and identifying available server tools to expand agent capabilities.

    Pythonagentai-societiesartificial-intelligence
    在 GitHub 上查看↗17,253
  • iofficeai/aionuiiOfficeAI 的头像

    iOfficeAI/AionUi

    16,621在 GitHub 上查看↗

    AionUi is an AI agent orchestration platform designed to manage and coordinate multiple autonomous assistants within a local environment. It functions as a framework for executing background processes and scheduled tasks that operate independently of the user interface, ensuring that automated workflows continue to run without manual oversight. The platform distinguishes itself through a local-first approach to document generation and file manipulation, allowing users to create and modify office files directly on their hardware to maintain data privacy. It supports parallel agent execution, e

    Provides a unified integration layer for connecting multiple agents to a shared set of functional tools.

    TypeScriptacpaiai-agent
    在 GitHub 上查看↗16,621
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  2. Artificial Intelligence & ML
  3. Agentic Systems Frameworks
  4. Development and Runtime Environments

探索子标签

  • AI Agent Infrastructure6 个子标签Back-end systems and registries that support the deployment, integration, and tracking of AI agents.
  • Agent Environments3 个子标签Configurable environments and workspaces designed for the execution and management of AI agents.
  • Manual Agent Loaders2 个子标签Utilities for programmatically registering and initializing custom agent definitions from local file systems.
  • Shell Execution AbstractionsControlled interfaces that allow AI agents to execute terminal commands while capturing output and error streams.