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16 dépôts

Awesome GitHub RepositoriesInfrastructure and Runtime Environments

Covers the underlying server-side execution environments, workspace containers, and deployment infrastructure for hosting agents.

Explore 16 awesome GitHub repositories matching artificial intelligence & ml · Infrastructure and Runtime Environments. Refine with filters or upvote what's useful.

Awesome Infrastructure and Runtime Environments GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • openclaw/openclawAvatar de openclaw

    openclaw/openclaw

    380,031Voir sur GitHub↗

    Openclaw est une plateforme de gestion d'environnements d'exécution d'agents, fournissant l'infrastructure nécessaire pour contrôler les cycles de vie des agents, l'état des sessions et la persistance des espaces de travail. Elle dispose d'une passerelle centralisée qui gère les boucles de modèles, l'invocation d'outils et les événements en streaming, tout en prenant en charge le routage multi-agents et la gestion de la mémoire persistante. Le système est conçu pour normaliser les signatures d'exécution des outils et fournir une interface standardisée pour la compatibilité entre les fournisseurs. La plateforme inclut des outils de développement étendus, tels qu'une interface en ligne de commande pour la gestion des espaces de travail, la journalisation de diagnostic et une architecture de plugins qui permet l'enregistrement d'outils et de capacités personnalisés. Elle prend en charge les flux de travail automatisés via des hooks pilotés par les événements, la planification des tâches et l'intégration avec des services externes. La sécurité est gérée par des politiques d'exécution, la portabilité des identifiants et des flux de travail d'approbation pour les actions des agents. Le déploiement est pris en charge par des installateurs d'infrastructure automatisés et des assistants de passerelle conteneurisés, avec des utilitaires intégrés pour les sauvegardes et la gestion de la configuration. Le système fournit un format structuré pour orchestrer des flux de travail en plusieurs étapes et inclut des outils spécialisés pour l'automatisation des navigateurs et le patch de code structuré.

    Allocates persistent directory structures to serve as long-term memory and file storage for agent operations.

    TypeScriptaiassistantcrustacean
    Voir sur GitHub↗380,031
  • langchain-ai/langchainAvatar de langchain-ai

    langchain-ai/langchain

    139,458Voir sur GitHub↗

    LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

    Offers standardized endpoints for provisioning, configuring, and monitoring the operational lifecycle of assistant instances.

    Pythonagentsaiai-agents
    Voir sur GitHub↗139,458
  • opendevin/opendevinAvatar de OpenDevin

    OpenDevin/OpenDevin

    77,460Voir sur GitHub↗

    OpenDevin is an autonomous software engineering agent and orchestrator designed to execute coding tasks and manage development workflows using large language models. It functions as a centralized control center for managing and switching between various local and cloud artificial intelligence backends. The system utilizes a Docker sandbox environment to isolate autonomous agents in containers, protecting the host filesystem during code execution. It includes an automated engineering workflow tool that integrates with version control and chat services to trigger tasks via webhooks or scheduled

    Ships a centralized server to host and coordinate multiple autonomous agents on a single instance.

    Python
    Voir sur GitHub↗77,460
  • paperclipai/paperclipAvatar de paperclipai

    paperclipai/paperclip

    70,619Voir sur GitHub↗

    Paperclip is an LLM agent orchestration platform and governance suite designed to coordinate teams of autonomous AI agents. It provides a management plane for defining organizational hierarchies, assigning roles, and aligning individual agent tasks with a structured mission tree to ensure work maps to business objectives. The project distinguishes itself through a specialized agent skill registry and workspace manager. It allows for the discovery and injection of reusable workflows into agent runtimes without retraining and provides isolated, sandboxed execution environments with persistent s

    Provides home directories for agents to store and access local files during execution.

    TypeScript
    Voir sur GitHub↗70,619
  • shareai-lab/learn-claude-codeAvatar de shareAI-lab

    shareAI-lab/learn-claude-code

    67,975Voir sur GitHub↗

    This project provides a modular framework for building and orchestrating autonomous AI agents. It functions as an agentic workflow engine that manages the full lifecycle of task execution, including model reasoning, tool invocation, and the integration of results. By utilizing a centralized orchestration platform, the system enables the creation of multi-agent teams that collaborate on complex objectives through structured communication and shared task graphs. The framework distinguishes itself through its focus on persistent, stateful operations and multi-agent coordination. It employs file-

    Provides separate filesystem directories for parallel agents to prevent file conflicts and resource interference.

    Pythonagentagent-developmentai-agent
    Voir sur GitHub↗67,975
  • mastra-ai/mastraAvatar de mastra-ai

    mastra-ai/mastra

    21,221Voir sur GitHub↗

    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

    Provides programmatic access to agent capabilities through server-based APIs for external application interaction.

    TypeScriptagentsaichatbots
    Voir sur GitHub↗21,221
  • pydantic/pydantic-aiAvatar de pydantic

    pydantic/pydantic-ai

    17,791Voir sur GitHub↗

    PydanticAI is a Python framework designed for building production-grade autonomous agents. It provides a unified interface for interacting with diverse language models, enabling developers to construct agents that perform complex tasks through structured data validation, tool execution, and multi-turn conversation management. The library centers on type-safe schema enforcement, ensuring that model inputs and outputs remain consistent and reliable throughout the agent's lifecycle. The framework distinguishes itself through a robust architecture that emphasizes modularity and testability. It ut

    Wraps agent logic in network applications to enable communication with other systems via standardized protocols.

    Pythonagent-frameworkgenaillm
    Voir sur GitHub↗17,791
  • camel-ai/camelAvatar de camel-ai

    camel-ai/camel

    17,253Voir sur GitHub↗

    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

    Wraps agent instances as network-accessible services for integration with external systems.

    Pythonagentai-societiesartificial-intelligence
    Voir sur GitHub↗17,253
  • kilo-org/kilocodeAvatar de Kilo-Org

    Kilo-Org/kilocode

    15,616Voir sur GitHub↗

    Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments. The platform distinguishes itself through its federated task management and policy-based access control, which

    Adds custom workspace creation flows to the environment, enabling the agent to manage non-standard project structures.

    TypeScriptaiai-ageai-coding
    Voir sur GitHub↗15,616
  • lsdefine/genericagentAvatar de lsdefine

    lsdefine/GenericAgent

    13,017Voir sur GitHub↗

    GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

    Spawns child agents with dedicated isolated workspaces to execute parallel sub-tasks.

    Pythonai-agentautomationautonomous-agent
    Voir sur GitHub↗13,017
  • yaoapp/yaoAvatar de YaoApp

    YaoApp/yao

    7,544Voir sur GitHub↗

    Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

    Maintains long-running AI agent sessions in isolated containers that persist across inactivity.

    Goagentagentic-aiagents
    Voir sur GitHub↗7,544
  • openai/openai-cs-agents-demoAvatar de openai

    openai/openai-cs-agents-demo

    6,410Voir sur GitHub↗

    This is a demonstration project from OpenAI that showcases a multi-agent customer service workflow built with the OpenAI Agents SDK. It coordinates several specialized AI agents to handle common airline support tasks such as flight booking and cancellation, refunds and compensation, seat and special service requests, real-time flight information, and airline policy FAQ responses, all within a single conversational interface. The system routes incoming customer requests to the appropriate specialized agent based on intent, and enforces guardrails to block off-topic or malicious requests. It su

    Provides a real, resumable file system and execution environment for each agent to safely handle coding or document tasks.

    Python
    Voir sur GitHub↗6,410
  • siteboon/claudecodeuiAvatar de siteboon

    siteboon/claudecodeui

    6,350Voir sur GitHub↗

    Claudecodeui is an open-source web interface that orchestrates multiple AI coding agents from different providers—including Claude Code, Cursor CLI, Codex, and Gemini CLI—side by side in isolated cloud environments. It functions as a multi-provider orchestration platform, allowing users to run agents from different tools within the same workspace without being locked into a single vendor. The platform runs each agent session inside a hypervisor-level Docker sandbox that isolates filesystem, network, and process access, with sessions persisting in the cloud to survive network disconnection or

    Keeping AI agent sessions running in isolated cloud containers that survive laptop closure, network loss, or sleep.

    JavaScriptanthropicanthropic-aianthropic-claude
    Voir sur GitHub↗6,350
  • voltagent/voltagentAvatar de VoltAgent

    VoltAgent/voltagent

    6,020Voir sur GitHub↗

    Gives each agent a persistent filesystem, sandbox execution, search, and skills available across conversations.

    TypeScriptagentsaiai-agents
    Voir sur GitHub↗6,020
  • nicocha30/ligolo-ngAvatar de nicocha30

    nicocha30/ligolo-ng

    4,289Voir sur GitHub↗

    Ligolo-ng is a network tunneling framework and control server designed for managing remote agents and coordinating network routing. It functions as a reverse tunneling proxy and a site-to-site VPN tool, utilizing a userland TUN interface to pivot network traffic through remote agents. The project distinguishes itself by using a TUN interface routing system to forward TCP, UDP, and ICMP traffic through multiple remote agents. It implements a C2 model where a central server manages remote agents to bypass firewall restrictions and provide direct subnet access to isolated networks. The system c

    Provides a central control server that manages remote agents, handles tunnels, and provides automatic recovery from network failures.

    Gogolangoffensive-securitypentest-tool
    Voir sur GitHub↗4,289
  • i-am-bee/beeai-frameworkAvatar de i-am-bee

    i-am-bee/beeai-framework

    3,304Voir sur GitHub↗

    The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

    Hosts agents and their capabilities as servers to expose them to external clients and platforms.

    Pythonagentsaiai-agent
    Voir sur GitHub↗3,304
  1. Home
  2. Artificial Intelligence & ML
  3. Agentic Systems Frameworks
  4. Integration and Deployment
  5. Infrastructure and Runtime Environments

Explorer les sous-tags

  • Agent Servers2 sous-tagsRuntime environments and APIs designed for hosting, managing, and configuring the execution of agent-based applications.
  • Agent Workspace Environments1 sous-tagPersistent directory structures that provide agents with a dedicated workspace for file access and long-term memory storage.