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7 个仓库

Awesome GitHub RepositoriesAgent Deployment Tools

Utilities for transitioning AI agents from prototypes to production environments.

Distinguishing note: Focuses on the deployment lifecycle of agents specifically.

Explore 7 awesome GitHub repositories matching artificial intelligence & ml · Agent Deployment Tools. Refine with filters or upvote what's useful.

Awesome Agent Deployment Tools GitHub Repositories

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  • significant-gravitas/auto-gptSignificant-Gravitas 的头像

    Significant-Gravitas/Auto-GPT

    184,987在 GitHub 上查看↗

    Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The

    Ships utilities for transitioning autonomous agents from local prototypes to scalable production triggers.

    Python
    在 GitHub 上查看↗184,987
  • mlflow/mlflowmlflow 的头像

    mlflow/mlflow

    26,554在 GitHub 上查看↗

    Launches agents using a server that provides automatic request validation and tracing for rapid production deployment.

    Pythonagentopsagentsai
    在 GitHub 上查看↗26,554
  • nirdiamant/agents-towards-productionNirDiamant 的头像

    NirDiamant/agents-towards-production

    17,375在 GitHub 上查看↗

    This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings

    Provides utilities for transitioning AI agents from development prototypes to scalable production environments.

    Jupyter Notebookagentagent-frameworkagents
    在 GitHub 上查看↗17,375
  • google/adk-samplesgoogle 的头像

    google/adk-samples

    8,476在 GitHub 上查看↗

    This project provides a collection of reference implementations, architectural patterns, and SDK samples for building autonomous agents using large language models. It serves as a multi-language framework for implementing and deploying specialized AI agents across diverse programming environments. The system centers on an orchestration framework that combines deterministic code with adaptive reasoning through structured graph workflows. It utilizes schema-driven integration to connect agents with third-party applications and diverse AI models. The development lifecycle is supported by toolki

    Implements utilities for transitioning AI agents from initial local prototypes to production environments.

    Pythonadkagent-samplesagents
    在 GitHub 上查看↗8,476
  • chaosblade-io/chaosbladechaosblade-io 的头像

    chaosblade-io/chaosblade

    6,355在 GitHub 上查看↗

    ChaosBlade is an open-source chaos engineering platform that injects faults into applications, containers, Kubernetes clusters, and host systems to test resilience. It functions as a multi-layer fault injection tool, capable of disrupting system resources, Java, C++, NodeJS, and Golang applications, Docker containers, and Kubernetes pods and nodes from a single interface. The platform distinguishes itself through its architecture, which defines chaos experiments as Kubernetes Custom Resource Definitions for native cluster integration, and supports multiple fault injection mechanisms including

    Deploys chaos engineering agents and tools onto hosts or Kubernetes clusters without manual setup.

    Pythonalibabachaos-engineeringchaos-testing
    在 GitHub 上查看↗6,355
  • coleam00/ottomator-agentscoleam00 的头像

    coleam00/ottomator-agents

    5,359在 GitHub 上查看↗

    Ottomator-agents is a framework for building and deploying autonomous AI agents using structured workflow files and source code. It serves as a declarative deployment tool and workflow orchestrator that translates static configuration files into executable sequences of AI agent tasks and logic flows. The system utilizes manifest-driven instantiation and template-driven deployment to create functional agent identities by populating source code templates with user-specified parameters. It incorporates a modular skill system that equips agents with discrete, reusable source code units and toolse

    Provides utilities for instantiating and transitioning AI agents from prototypes to functional deployments.

    Python
    在 GitHub 上查看↗5,359
  • futantan/opengptfutantan 的头像

    futantan/OpenGpt

    3,902在 GitHub 上查看↗

    OpenGpt 是一个智能体编排平台和多模态界面,专为构建和部署专业 AI 人格而设计。它允许用户创建带有自定义系统提示词和行为约束的任务导向型智能体,以自动化专业、创意和技术工作流。 该项目具有一个提示词工程工作流,可将简单的用户输入转换为结构化指令,以提高模型准确性。它通过将向量数据库连接到聊天界面来集成检索增强生成 (RAG),从而实现基于私有数据集的上下文感知响应。 该平台涵盖了广泛的功能,包括针对 PDF 和音频的多模态数据解析、通过个人密钥进行的多提供商 API 管理,以及生成专业文档、功能代码和视觉提示词等多种内容类型。它还包括通过 Google OAuth 进行的内容分析、翻译服务和身份管理工具。

    Provides utilities for transitioning AI agents from prototypes to production environments for specialized tasks.

    TypeScript
    在 GitHub 上查看↗3,902
  1. Home
  2. Artificial Intelligence & ML
  3. Agent Deployment Tools

探索子标签

  • Chaos Engineering Agent DeploymentsDeploys chaos engineering agents and tools onto hosts or Kubernetes clusters without manual setup. **Distinct from Agent Deployment Tools:** Distinct from Agent Deployment Tools: focuses on chaos engineering agents specifically, not general AI agent deployment.