{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-28T06:20:51.496Z","headline":"OpenSpace 教程：用 Skills、MCP 和 Lineage 构建自进化 AI 智能体","description":"OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。教程演示了从环境配置、稀疏仓库克隆到实时任务执行的全流程，智能体在执行 payroll 计算任务后自动生成 FIX、DERIVED 和 CAPTURED 三类技能，并存入 SQLite 数据库。后续 warm-task 可复用已进化技能，降低重复调用成本。","url":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/","mainEntityOfPage":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/","datePublished":"2026-07-25T07:54:21.000Z","dateModified":"2026-07-25T07:54:21.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.marktechpost.com/2026/07/25/building-self-evolving-ai-agents-with-openspace-using-skills-mcp-lineage-and-low-cost-reuse","https://aihot.virxact.com/items/cms02sraa03igrop1epde5neu"],"canonicalUrl":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/","dateCreated":"2026-07-25T07:54:21.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"marktechpost.com source article","url":"https://www.marktechpost.com/2026/07/25/building-self-evolving-ai-agents-with-openspace-using-skills-mcp-lineage-and-low-cost-reuse","datePublished":"2026-07-25T07:54:21.000Z","provider":{"@type":"Organization","name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/25/building-self-evolving-ai-agents-with-openspace-using-skills-mcp-lineage-and-low-cost-reuse"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cms02sraa03igrop1epde5neu","datePublished":"2026-07-25T07:54:21.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cms02sraa03igrop1epde5neu"}}],"aggregationSource":"MarkTechPost（RSS）","originalPublisher":{"name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/25/building-self-evolving-ai-agents-with-openspace-using-skills-mcp-lineage-and-low-cost-reuse"},"article":{"id":"cms02sraa03igrop1epde5neu","slug":"cms02sraa03igrop1epde5neu","url":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/","title":"OpenSpace 教程：用 Skills、MCP 和 Lineage 构建自进化 AI 智能体","title_en":"Building Self-Evolving AI Agents with OpenSpace Using Skills， MCP， Lineage， and Low-Cost Reuse","summary":"OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。教程演示了从环境配置、稀疏仓库克隆到实时任务执行的全流程，智能体在执行 payroll 计算任务后自动生成 FIX、DERIVED 和 CAPTURED 三类技能，并存入 SQLite 数据库。后续 warm-task 可复用已进化技能，降低重复调用成本。","source":"MarkTechPost（RSS）","sourceUrl":"https://www.marktechpost.com/2026/07/25/building-self-evolving-ai-agents-with-openspace-using-skills-mcp-lineage-and-low-cost-reuse","aiHotUrl":"https://aihot.virxact.com/items/cms02sraa03igrop1epde5neu","publishedAt":"2026-07-25T07:54:21.000Z","category":"技巧观点","score":56,"selected":false,"articleBody":["In this tutorial,：https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials/blob/main/Agentic%20AI%20Codes/HKUDS_OpenSpace_Self_Evolving_AI_Agents_Marktechpost.ipynb we build and examine an OpenSpace ：https://github.com/HKUDS/OpenSpace workflow, progressing from environment setup and sparse repository cloning to live task execution, skill evolution, and MCP-based agent integration. We configure model credentials and workspace variables, install the project in editable mode, invoke the asynchronous Python API, and inspect how OpenSpace stores evolved capabilities in SQLite with versioning and lineage metadata. We also create a custom SKILL.md, connect host-agent skills, test warm-task reuse, launch the streamable HTTP MCP server, and analyze the showcase evolution database to understand how FIX, DERIVED, and CAPTURED skills support lower-cost, reusable agent behavior.","We verify the Python runtime, define the required API credentials, and configure the OpenSpace model and optional cloud access settings. We clone the repository with sparse checkout, install the package in editable mode, and confirm that the OpenSpace command-line tools are available. We then create the workspace and skill directories, write the environment configuration files, export the required variables, and detect whether live LLM execution is enabled.","We initialize asynchronous execution in Google Colab and define a reusable function to submit tasks via the OpenSpace Python API. We run an initial payroll-generation task and inspect any skills that OpenSpace evolves during post-execution analysis. We also examine the SQLite database structure, display stored records, and verify that the skill registry and type definitions are accessible programmatically.","We submit a related payroll task to observe how OpenSpace reuses or derives capabilities from previously generated skills. We create a custom SKILL.md that instructs the agent to analyze CSV files and produce structured Markdown reports. We then install the OpenSpace host skills, generate a demonstration dataset, and execute the custom capability through the same evolving agent workflow.","We start the OpenSpace MCP server using the streamable HTTP transport and bind it to a local Colab endpoint. We probe the endpoint to confirm that the server process is running, even when a basic HTTP request returns an MCP-specific response status. We also generate an example MCP host configuration that external agents can use to access the OpenSpace workspace and skill directories.","We conditionally upload the custom skill to the OpenSpace cloud community when a valid cloud API key is available. We inspect the repository’s showcase SQLite database to study the stored skills, metadata, quality information, and complete evolution lineage. We finally aggregate skills by origin type and summarize the Colab environment, custom capability, MCP integration, and self-evolving workflow that we establish throughout the tutorial.","In conclusion, we established a practical OpenSpace environment that demonstrates how agent capabilities are executed, persisted, reused, and progressively improved. We worked directly with the Python API, local skill directories, SQLite-backed registries, MCP transports, and optional cloud skill-sharing commands, giving us visibility into both the user-facing workflow and the underlying skill-engine architecture. We also verified how related tasks can reuse previously evolved knowledge, how custom skills become discoverable at runtime, and how lineage records expose the development history of each capability, leaving us with a strong foundation for building self-evolving, cost-efficient agent systems in Colab.","Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us ：https://forms.gle/wbash1wF6efRj8G58","Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions."],"articleImages":[{"sourceUrl":"https://www.marktechpost.com/wp-content/uploads/2026/07/blog6171-1-100x70.png","alt":"Designing High-Performance GPU Kernels with TileLang: Tensor-Core GEMM, Fused Softmax, FlashAttention, and Autotuning","afterParagraph":8,"url":"/media/articles/cms02sraa03igrop1epde5neu/80925b48ffa65ad2.webp"}],"mediaStatus":"ok","articleBodyZh":["在本教程中：https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials/blob/main/Agentic%20AI%20Codes/HKUDS_OpenSpace_Self_Evolving_AI_Agents_Marktechpost.ipynb，我们构建并检查一个 OpenSpace 流程：https://github.com/HKUDS/OpenSpace，从环境设置和稀疏仓库克隆开始，到实时任务执行、技能演化以及基于 MCP 的代理集成。我们配置模型凭证和工作区变量，以可编辑模式安装项目，调用异步 Python API，并检查 OpenSpace 如何在 SQLite 中存储演化能力，包括版本控制和血统元数据。我们还创建了自定义的 SKILL.md，连接主机代理技能，测试暖任务重用，启动可流式 HTTP MCP 服务器，并分析展示数据库以了解 FIX、DERIVED 和 CAPTURED 技能如何支持低成本、可重用的代理行为。","我们验证 Python 运行时，定义所需的 API 凭证，并配置 OpenSpace 模型及可选的云访问设置。我们使用稀疏检出克隆仓库，以可编辑模式安装包，并确认 OpenSpace 命令行工具可用。然后我们创建工作区和技能目录，编写环境配置文件，导出所需变量，并检测是否启用实时 LLM 执行。","我们在 Google Colab 中初始化异步执行，并定义一个可重用函数，通过 OpenSpace Python API 提交任务。我们运行一个初始的工资生成任务，并检查 OpenSpace 在后续执行分析中演化的任何技能。我们还检查 SQLite 数据库结构，显示存储的记录，并验证技能注册表和类型定义是否可以通过程序访问。","我们提交一个相关的工资任务，以观察 OpenSpace 如何重用或派生之前生成的技能能力。我们创建一个自定义 SKILL.md，指导代理分析 CSV 文件并生成结构化 Markdown 报告。然后我们安装 OpenSpace 主机技能，生成演示数据集，并通过相同的演化代理工作流程执行自定义能力。","我们使用可流式的 HTTP 传输启动 OpenSpace MCP 服务器，并将其绑定到本地 Colab 端点。我们探测该端点以确认服务器进程正在运行，即使一个基本的 HTTP 请求返回的是 MCP 特定的响应状态。我们还生成了一个示例 MCP 主机配置，外部代理可以使用它来访问 OpenSpace 工作区和技能目录。","当存在有效的云 API 密钥时，我们会有条件地将自定义技能上传到 OpenSpace 云社区。我们检查了存储库的 showcase SQLite 数据库，以研究存储的技能、元数据、质量信息以及完整的进化谱系。最后，我们按照来源类型汇总技能，并总结整个教程中建立的 Colab 环境、自定义能力、MCP 集成和自我进化的工作流程。","总之，我们建立了一个实际的 OpenSpace 环境，展示了代理能力如何被执行、持久化、重用以及逐步改进。我们直接使用 Python API、本地技能目录、SQLite 支持的注册表、MCP 传输以及可选的云端技能共享命令，从而能够了解用户端的工作流程以及底层技能引擎的架构。我们还验证了相关任务如何重用之前进化的知识，自定义技能如何在运行时变得可发现，以及谱系记录如何揭示每个能力的开发历史，为在 Colab 中构建自我进化、成本高效的代理系统奠定了坚实的基础。","需要与我们合作推广您的 GitHub 仓库 OR Hugging Face 页面 OR 产品发布 OR 网络研讨会等吗？请联系： https://forms.gle/wbash1wF6efRj8G58","Sana Hassan，是 Marktechpost 的咨询实习生，同时也是 IIT Madras 的双学位学生，热衷于将技术和 AI 应用于解决现实世界问题。他对解决实际问题充满兴趣，为 AI 与现实生活解决方案的交汇带来了新的视角。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：产品与工具类动态的价值取决于它是否解决明确场景、能否进入工作流，以及交付、价格和数据安全是否可接受。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察产品是否开放使用、用户反馈、定价、集成能力和后续版本更新。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-07-28T06:29:10.511Z","sourceHash":"b91d0eec776b2cb5","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["技巧观点","MarkTechPost（RSS）"],"translations":{"zh-CN":{"title":"OpenSpace 教程：用 Skills、MCP 和 Lineage 构建自进化 AI 智能体","summary":"OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。教程演示了从环境配置、稀疏仓库克隆到实时任务执行的全流程，智能体在执行 payroll 计算任务后自动生成 FIX、DERIVED 和 CAPTURED 三类技能，并存入 SQLite 数据库。后续 warm-task 可复用已进化技能，降低重复调用成本。","category":"技巧观点","source":"marktechpost.com","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace 教程：用 Skills、MCP 和 Lineage 构建自进化 AI 智能体 - Aioga AI资讯","description":"OpenSpace 通过 Skills、MCP 和 Lineage 元数据实现低成本的 AI 智能体自进化。教程演示了从环境配置、稀疏仓库克隆到实时任务执行的全流程，智能体在执行 payroll 计算任务后自动生成 FIX、DERIVED 和 CAPTURED 三类技能，并存入 SQLite 数据库。后续 warm-task 可复用已进化技能，降低重复调用成...","url":"https://www.aioga.com/news/cms02sraa03igrop1epde5neu/"},"en":{"title":"OpenSpace Tutorial: Building Self-Evolving AI Agents with Skills, MCP, and Lineage","summary":"OpenSpace achieves low-cost AI agent self-evolution through Skills, MCP, and Lineage metadata. The tutorial demonstrates the full process from environment setup and sparse repository cloning to real-time task execution. After the agent performs the payroll calculation task, it automatically generates three types of skills: FIX, DERIVED, and CAPTURED, and stores them in an SQLite database. Subsequent warm-tasks can reuse the evolved skills, reducing the cost of repeated calls.","category":"Insights","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace Tutorial: Building Self-Evolving AI Agents with Skills, MCP, and Lineage - Aioga AI News","description":"OpenSpace achieves low-cost AI agent self-evolution through Skills, MCP, and Lineage metadata. The tutorial demonstrates the full process from environment setup and sparse reposito...","url":"https://www.aioga.com/en/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:42:45.793Z"},"ja":{"title":"OpenSpace チュートリアル：Skills、MCP、Lineage を用いて自己進化する AI エージェントを構築する","summary":"OpenSpace は、Skills、MCP、Lineage のメタデータを通じて、低コストで AI エージェントの自己進化を実現します。チュートリアルでは、環境設定、スパースリポジトリのクローン作成からリアルタイムタスクの実行までの全プロセスを示しています。エージェントは payroll 計算タスクを実行した後、自動的に FIX、DERIVED、CAPTURED の三種類のスキルを生成し、SQLite データベースに保存します。後続の warm-task では、既に進化したスキルを再利用でき、重複呼び出しのコストを削減します。","category":"ヒントと視点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace チュートリアル：Skills、MCP、Lineage を用いて自己進化する AI エージェントを構築する - Aioga AIニュース","description":"OpenSpace は、Skills、MCP、Lineage のメタデータを通じて、低コストで AI エージェントの自己進化を実現します。チュートリアルでは、環境設定、スパースリポジトリのクローン作成からリアルタイムタスクの実行までの全プロセスを示しています。エージェントは payroll 計算タスクを実行した後、自動的に FIX、DERIVED、CAPTU...","url":"https://www.aioga.com/ja/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:42:55.268Z"},"ko":{"title":"OpenSpace 튜토리얼: Skills, MCP 및 Lineage를 사용하여 자기 진화 AI 에이전트 구축","summary":"OpenSpace는 Skills, MCP 및 Lineage 메타데이터를 통해 저비용 AI 에이전트의 자기 진화를 구현합니다. 튜토리얼에서는 환경 구성, 희소 저장소 클론에서 실시간 작업 실행까지의 전체 과정을 시연하며, 에이전트는 payroll 계산 작업 수행 후 FIX, DERIVED 및 CAPTURED 세 가지 유형의 스킬을 자동으로 생성하고 SQLite 데이터베이스에 저장합니다. 이후 warm-task에서는 이미 진화한 스킬을 재사용하여 반복 호출 비용을 줄일 수 있습니다.","category":"인사이트","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace 튜토리얼: Skills, MCP 및 Lineage를 사용하여 자기 진화 AI 에이전트 구축 - Aioga AI 뉴스","description":"OpenSpace는 Skills, MCP 및 Lineage 메타데이터를 통해 저비용 AI 에이전트의 자기 진화를 구현합니다. 튜토리얼에서는 환경 구성, 희소 저장소 클론에서 실시간 작업 실행까지의 전체 과정을 시연하며, 에이전트는 payroll 계산 작업 수행 후 FIX, DERIVED 및 CAPTURED 세 가지 유형의...","url":"https://www.aioga.com/ko/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:43:40.503Z"},"es":{"title":"Tutorial de OpenSpace: Construyendo agentes de IA autoevolutivos con Skills, MCP y Lineage","summary":"OpenSpace logra la autoevolución de agentes de IA de bajo costo a través de Skills, MCP y metadatos de Lineage. El tutorial demuestra todo el proceso desde la configuración del entorno, la clonación de repositorios dispersos hasta la ejecución de tareas en tiempo real. Después de que el agente realiza la tarea de cálculo de nómina, genera automáticamente tres tipos de habilidades: FIX, DERIVED y CAPTURED, y las almacena en una base de datos SQLite. Las tareas posteriores en caliente pueden reutilizar las habilidades ya evolucionadas, reduciendo el costo de llamadas repetidas.","category":"Ideas","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Tutorial de OpenSpace: Construyendo agentes de IA autoevolutivos con Skills, MCP y Lineage - Aioga Noticias de IA","description":"OpenSpace logra la autoevolución de agentes de IA de bajo costo a través de Skills, MCP y metadatos de Lineage. El tutorial demuestra todo el proceso desde la configuración del ent...","url":"https://www.aioga.com/es/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:43:33.160Z"},"fr":{"title":"Tutoriel OpenSpace : Construire des agents IA auto-évolutifs avec Skills, MCP et Lineage","summary":"OpenSpace réalise une auto-évolution à faible coût des agents intelligents AI via les métadonnées Skills, MCP et Lineage. Le tutoriel montre le processus complet depuis la configuration de l'environnement, le clonage du dépôt sparse jusqu'à l'exécution des tâches en temps réel. Après l'exécution de la tâche de calcul de la paie, l'agent génère automatiquement trois types de compétences : FIX, DERIVED et CAPTURED, et les enregistre dans une base de données SQLite. Les tâches ultérieures (warm-task) peuvent réutiliser les compétences déjà évoluées, réduisant le coût des appels répétés.","category":"Analyses","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Tutoriel OpenSpace : Construire des agents IA auto-évolutifs avec Skills, MCP et Lineage - Aioga Actualités IA","description":"OpenSpace réalise une auto-évolution à faible coût des agents intelligents AI via les métadonnées Skills, MCP et Lineage. Le tutoriel montre le processus complet depuis la configur...","url":"https://www.aioga.com/fr/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:44:23.197Z"},"de":{"title":"OpenSpace Tutorial: Aufbau autonom evolvierender KI-Agenten mit Skills, MCP und Lineage","summary":"OpenSpace ermöglicht die kostengünstige Selbstentwicklung von KI-Agenten durch Skills-, MCP- und Lineage-Metadaten. Das Tutorial zeigt den gesamten Prozess von der Umgebungskonfiguration über das Klonen von spärlichen Repositories bis hin zur Echtzeitaufgabenausführung. Der Agent erstellt nach der Ausführung von Payroll-Berechnungsaufgaben automatisch die drei Arten von Fähigkeiten FIX, DERIVED und CAPTURED und speichert sie in einer SQLite-Datenbank. Nachfolgende Warm-Tasks können die bereits entwickelten Fähigkeiten wiederverwenden, um die Kosten für wiederholte Aufrufe zu senken.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace Tutorial: Aufbau autonom evolvierender KI-Agenten mit Skills, MCP und Lineage - Aioga KI-News","description":"OpenSpace ermöglicht die kostengünstige Selbstentwicklung von KI-Agenten durch Skills-, MCP- und Lineage-Metadaten. Das Tutorial zeigt den gesamten Prozess von der Umgebungskonfigu...","url":"https://www.aioga.com/de/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:44:24.289Z"},"pt-BR":{"title":"Tutorial OpenSpace: Construindo Agentes de IA Autoevolutivos com Skills, MCP e Lineage","summary":"OpenSpace implementa a autoevolução de agentes de IA de baixo custo através de metadados Skills, MCP e Lineage. O tutorial demonstra todo o processo, desde a configuração do ambiente e clonagem do repositório esparso até a execução de tarefas em tempo real. Após executar a tarefa de cálculo de folha de pagamento, o agente gera automaticamente três tipos de habilidades, FIX, DERIVED e CAPTURED, e as armazena em um banco de dados SQLite. Tarefas subsequentes (warm-task) podem reutilizar as habilidades já evoluídas, reduzindo o custo de chamadas repetidas.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Tutorial OpenSpace: Construindo Agentes de IA Autoevolutivos com Skills, MCP e Lineage - Aioga Notícias de IA","description":"OpenSpace implementa a autoevolução de agentes de IA de baixo custo através de metadados Skills, MCP e Lineage. O tutorial demonstra todo o processo, desde a configuração do ambien...","url":"https://www.aioga.com/pt-BR/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:45:11.685Z"},"ru":{"title":"Учебник OpenSpace: создание самовоспроизводящегося AI-агента с помощью Skills, MCP и Lineage","summary":"OpenSpace реализует низкозатратную самою эволюцию AI-агентов через метаданные Skills, MCP и Lineage. В учебном пособии демонстрируется полный процесс от настройки среды, клонирования разреженного репозитория до выполнения задач в реальном времени. После выполнения задачи по расчету заработной платы агент автоматически создает три типа навыков: FIX, DERIVED и CAPTURED, и сохраняет их в базу данных SQLite. Последующие warm-task могут повторно использовать уже эволюционировавшие навыки, снижая затраты на повторные вызовы.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Учебник OpenSpace: создание самовоспроизводящегося AI-агента с помощью Skills, MCP и Lineage - Aioga Новости ИИ","description":"OpenSpace реализует низкозатратную самою эволюцию AI-агентов через метаданные Skills, MCP и Lineage. В учебном пособии демонстрируется полный процесс от настройки среды, клонирован...","url":"https://www.aioga.com/ru/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:45:11.484Z"},"ar":{"title":"دليل OpenSpace: بناء وكيل ذكاء اصطناعي يتطور ذاتيًا باستخدام المهارات وMCP وLineage","summary":"يحقق OpenSpace التطور الذاتي منخفض التكلفة للوكيل الذكي AI من خلال مهارات Skills وMCP والبيانات الوصفية Lineage. يظهر الدليل التعليمي العملية الكاملة من إعداد البيئة واستنساخ المستودع النادر إلى تنفيذ المهام في الوقت الفعلي، حيث يقوم الوكيل تلقائيًا بإنشاء ثلاثة أنواع من المهارات بعد تنفيذ مهمة حساب الرواتب وهي FIX وDERIVED وCAPTURED، وتخزينها في قاعدة بيانات SQLite. يمكن إعادة استخدام المهارات التي تم تطويرها في المهام المستقبلية warm-task لتقليل تكلفة الاستدعاءات المتكررة.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"دليل OpenSpace: بناء وكيل ذكاء اصطناعي يتطور ذاتيًا باستخدام المهارات وMCP وLineage - Aioga أخبار الذكاء الاصطناعي","description":"يحقق OpenSpace التطور الذاتي منخفض التكلفة للوكيل الذكي AI من خلال مهارات Skills وMCP والبيانات الوصفية Lineage. يظهر الدليل التعليمي العملية الكاملة من إعداد البيئة واستنساخ المست...","url":"https://www.aioga.com/ar/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:45:53.555Z"},"hi":{"title":"OpenSpace ट्यूटोरियल: Skills, MCP और Lineage का उपयोग करके आत्म-विकसित AI एजेंट बनाना","summary":"OpenSpace कौशल, MCP और Lineage मेटाडेटा के माध्यम से कम लागत वाले AI एजेंट की स्व-विकासशीलता को सक्षम बनाता है। ट्यूटोरियल में पर्यावरण सेटअप, स्पार्स रिपॉजिटरी क्लोनिंग से लेकर रियल-टाइम टास्क निष्पादन तक की पूरी प्रक्रिया दिखाई गई है, एजेंट पेरोल गणना कार्य को निष्पादित करने के बाद स्वचालित रूप से FIX, DERIVED और CAPTURED तीन प्रकार के कौशल उत्पन्न करता है और उन्हें SQLite डेटाबेस में संग्रहित करता है। बाद के वार्म-टास्क में पहले से विकसित कौशलों का पुन: उपयोग किया जा सकता है, जिससे पुनरावृत्ति लागत कम होती है।","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace ट्यूटोरियल: Skills, MCP और Lineage का उपयोग करके आत्म-विकसित AI एजेंट बनाना - Aioga AI समाचार","description":"OpenSpace कौशल, MCP और Lineage मेटाडेटा के माध्यम से कम लागत वाले AI एजेंट की स्व-विकासशीलता को सक्षम बनाता है। ट्यूटोरियल में पर्यावरण सेटअप, स्पार्स रिपॉजिटरी क्लोनिंग से लेकर रि...","url":"https://www.aioga.com/hi/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:45:57.950Z"},"it":{"title":"Tutorial OpenSpace: Costruire agenti intelligenti AI auto-evolutivi con Skills, MCP e Lineage","summary":"OpenSpace realizza l'auto-evoluzione a basso costo degli agenti AI attraverso i metadati Skills, MCP e Lineage. Il tutorial dimostra l'intero processo, dalla configurazione dell'ambiente, la clonazione di repository sparsi, fino all'esecuzione in tempo reale delle attività; l'agente genera automaticamente tre tipi di competenze, FIX, DERIVED e CAPTURED, dopo aver eseguito il calcolo della payroll, e le memorizza nel database SQLite. Le attività successive a caldo (warm-task) possono riutilizzare le competenze già evolute, riducendo il costo delle chiamate ripetute.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Tutorial OpenSpace: Costruire agenti intelligenti AI auto-evolutivi con Skills, MCP e Lineage - Aioga Notizie IA","description":"OpenSpace realizza l'auto-evoluzione a basso costo degli agenti AI attraverso i metadati Skills, MCP e Lineage. Il tutorial dimostra l'intero processo, dalla configurazione dell'am...","url":"https://www.aioga.com/it/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:46:44.064Z"},"nl":{"title":"OpenSpace handleiding: Zelfontwikkelende AI-agent bouwen met Skills, MCP en Lineage","summary":"OpenSpace realiseert kosteneffectieve AI-agent zelf-evolutie via Skills, MCP en Lineage metadata. De tutorial toont het volledige proces van omgevingconfiguratie en het klonen van een spaarzame repository tot realtime taakuitvoering. Nadat de agent een payroll-berekeningstaak heeft uitgevoerd, genereert deze automatisch drie soorten vaardigheden: FIX, DERIVED en CAPTURED, die worden opgeslagen in een SQLite-database. Latere warm-tasks kunnen de geëvolueerde vaardigheden hergebruiken, waardoor de kosten van herhaalde aanroepen worden verminderd.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace handleiding: Zelfontwikkelende AI-agent bouwen met Skills, MCP en Lineage - Aioga AI-nieuws","description":"OpenSpace realiseert kosteneffectieve AI-agent zelf-evolutie via Skills, MCP en Lineage metadata. De tutorial toont het volledige proces van omgevingconfiguratie en het klonen van...","url":"https://www.aioga.com/nl/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:46:46.427Z"},"tr":{"title":"OpenSpace Eğitimi: Skills, MCP ve Lineage Kullanarak Kendini Geliştiren AI Ajanı Oluşturma","summary":"OpenSpace, Skills, MCP ve Lineage meta verileri aracılığıyla düşük maliyetli AI zekâ ajanlarının kendi kendine evrimleşmesini sağlar. Eğitim, ortam yapılandırmasından seyrek depo klonlamaya ve gerçek zamanlı görev yürütmeye kadar tüm süreci gösterir; ajan, bordro hesaplama görevini gerçekleştirdikten sonra otomatik olarak FIX, DERIVED ve CAPTURED olmak üzere üç tür beceri oluşturur ve bunları SQLite veritabanına kaydeder. Sonraki sıcak görevlerde, gelişmiş beceriler yeniden kullanılabilir, böylece tekrar çağırma maliyeti düşer.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace Eğitimi: Skills, MCP ve Lineage Kullanarak Kendini Geliştiren AI Ajanı Oluşturma - Aioga AI Haberleri","description":"OpenSpace, Skills, MCP ve Lineage meta verileri aracılığıyla düşük maliyetli AI zekâ ajanlarının kendi kendine evrimleşmesini sağlar. Eğitim, ortam yapılandırmasından seyrek depo k...","url":"https://www.aioga.com/tr/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:47:27.710Z"},"vi":{"title":"Hướng dẫn OpenSpace: Xây dựng tác nhân AI tự tiến hóa bằng Skills, MCP và Lineage","summary":"OpenSpace thông qua metadata Skills, MCP và Lineage thực hiện tiến hóa tự động của AI với chi phí thấp. Hướng dẫn minh họa toàn bộ quy trình từ cấu hình môi trường, sao chép kho sparse đến thực hiện nhiệm vụ trực tiếp, khi thực hiện nhiệm vụ tính lương, AI tự động tạo ra ba loại kỹ năng FIX, DERIVED và CAPTURED, và lưu vào cơ sở dữ liệu SQLite. Các warm-task tiếp theo có thể tái sử dụng các kỹ năng đã tiến hóa, giảm chi phí gọi lặp lại.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Hướng dẫn OpenSpace: Xây dựng tác nhân AI tự tiến hóa bằng Skills, MCP và Lineage - Tin tức AI Aioga","description":"OpenSpace thông qua metadata Skills, MCP và Lineage thực hiện tiến hóa tự động của AI với chi phí thấp. Hướng dẫn minh họa toàn bộ quy trình từ cấu hình môi trường, sao chép kho sp...","url":"https://www.aioga.com/vi/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:47:31.632Z"},"id":{"title":"Tutorial OpenSpace: Membangun Agen AI yang Mengalami Evolusi Sendiri dengan Skills, MCP, dan Lineage","summary":"OpenSpace mewujudkan evolusi mandiri agen AI dengan biaya rendah melalui metadata Skills, MCP, dan Lineage. Tutorial ini menunjukkan seluruh proses dari konfigurasi lingkungan, kloning repositori jarang hingga pelaksanaan tugas secara real-time. Setelah agen mengeksekusi tugas perhitungan payroll, secara otomatis menghasilkan tiga jenis keterampilan: FIX, DERIVED, dan CAPTURED, dan menyimpannya di database SQLite. Tugas warm-task berikutnya dapat menggunakan kembali keterampilan yang telah berevolusi, sehingga mengurangi biaya panggilan yang berulang.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Tutorial OpenSpace: Membangun Agen AI yang Mengalami Evolusi Sendiri dengan Skills, MCP, dan Lineage - Berita AI Aioga","description":"OpenSpace mewujudkan evolusi mandiri agen AI dengan biaya rendah melalui metadata Skills, MCP, dan Lineage. Tutorial ini menunjukkan seluruh proses dari konfigurasi lingkungan, klo...","url":"https://www.aioga.com/id/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:48:12.346Z"},"th":{"title":"คู่มือ OpenSpace: ใช้ Skills, MCP และ Lineage ในการสร้างปัญญาประดิษฐ์ที่ปรับตัวเองได้","summary":"OpenSpace ใช้ Skills, MCP และเมตาดาต้า Lineage เพื่อให้เอเยนต์ AI สามารถวิวัฒนาการตัวเองด้วยต้นทุนต่ำ บทเรียนสาธิตขั้นตอนทั้งหมดตั้งแต่การตั้งค่าสภาพแวดล้อม การโคลนคลังข้อมูลแบบบาง ไปจนถึงการทำงานแบบเรียลไทม์ หลังจากที่เอเยนต์ทำงานคำนวณเงินเดือนเสร็จแล้ว จะสร้างทักษะสามประเภท ได้แก่ FIX, DERIVED และ CAPTURED โดยอัตโนมัติ และจัดเก็บในฐานข้อมูล SQLite งาน warm-task ต่อไปสามารถนำทักษะที่วิวัฒนาการแล้วมาใช้ซ้ำ เพื่อลดต้นทุนการเรียกใช้งานซ้ำ","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"คู่มือ OpenSpace: ใช้ Skills, MCP และ Lineage ในการสร้างปัญญาประดิษฐ์ที่ปรับตัวเองได้ - ข่าว AI Aioga","description":"OpenSpace ใช้ Skills, MCP และเมตาดาต้า Lineage เพื่อให้เอเยนต์ AI สามารถวิวัฒนาการตัวเองด้วยต้นทุนต่ำ บทเรียนสาธิตขั้นตอนทั้งหมดตั้งแต่การตั้งค่าสภาพแวดล้อม การโคลนคลังข้อมูลแบบบาง...","url":"https://www.aioga.com/th/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:48:26.098Z"},"pl":{"title":"OpenSpace Tutorial: Tworzenie samorozwijającego się agenta AI przy użyciu Skills, MCP i Lineage","summary":"OpenSpace realizuje nisko kosztową samoewolucję agentów AI poprzez metadane Skills, MCP i Lineage. Tutorial demonstruje cały proces od konfiguracji środowiska, klonowania rzadkiego repozytorium po wykonywanie zadań w czasie rzeczywistym. Po wykonaniu zadania obliczeniowego payroll, agent automatycznie generuje trzy rodzaje umiejętności: FIX, DERIVED i CAPTURED, i zapisuje je w bazie danych SQLite. Kolejne zadania typu warm-task mogą ponownie wykorzystywać już ewoluowane umiejętności, zmniejszając koszty powtarzanych wywołań.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"OpenSpace Tutorial: Tworzenie samorozwijającego się agenta AI przy użyciu Skills, MCP i Lineage - Aioga Wiadomości AI","description":"OpenSpace realizuje nisko kosztową samoewolucję agentów AI poprzez metadane Skills, MCP i Lineage. Tutorial demonstruje cały proces od konfiguracji środowiska, klonowania rzadkiego...","url":"https://www.aioga.com/pl/news/cms02sraa03igrop1epde5neu/","contentTranslated":true,"sourceHash":"42fe48b451757a22","translatedAt":"2026-07-26T13:49:06.817Z"}}}}