{
  "schemaVersion": "1.0",
  "item": {
    "slug": "agent-os",
    "name": "Agent Os",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/cryptocana/agent-os",
    "canonicalUrl": "https://clawhub.ai/cryptocana/agent-os",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/agent-os",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agent-os",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "ARCHITECTURE.md",
      "BUILD_SUMMARY.md",
      "README.md",
      "SKILL.md",
      "core/agent.js",
      "core/executor.js"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/agent-os"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/agent-os",
    "agentPageUrl": "https://openagent3.xyz/skills/agent-os/agent",
    "manifestUrl": "https://openagent3.xyz/skills/agent-os/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/agent-os/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Agent OS — Persistent Agent Operating System",
        "body": "Agents that remember. Learn. Coordinate."
      },
      {
        "title": "What It Does",
        "body": "Agent OS enables multi-agent project execution with persistent memory:\n\nAgent Memory — Each agent remembers past tasks, lessons learned, success rates\nTask Decomposition — Break high-level goals into executable task sequences\nSmart Routing — Assign tasks to agents based on capability fit\nExecution Tracking — Live progress board showing what every agent is doing\nState Persistence — Project state survives restarts (resume mid-project)"
      },
      {
        "title": "Installation",
        "body": "clawhub install nova/agent-os"
      },
      {
        "title": "Basic Usage",
        "body": "const { AgentOS } = require('agent-os');\n\nconst os = new AgentOS('my-project');\n\n// Register agents with capabilities\nos.registerAgent('research', '🔍 Research', ['research', 'planning']);\nos.registerAgent('design', '🎨 Design', ['design', 'planning']);\nos.registerAgent('dev', '💻 Development', ['development']);\n\nos.initialize();\n\n// Run a project\nconst result = await os.runProject('Build a feature', [\n  'planning',\n  'design',\n  'development',\n]);\n\nconsole.log(result.progress); // 100"
      },
      {
        "title": "Agent",
        "body": "Persistent worker with:\n\nMemory — Past tasks, lessons learned, success rates\nState — Current task, progress, blockers\nCapabilities — What it's good at (research, design, development, etc.)"
      },
      {
        "title": "TaskRouter",
        "body": "Decomposes goals into executable tasks:\n\nBreaks \"Build a feature\" into: plan → design → develop → test\nMatches tasks to agents based on capability fit\nTracks dependencies (task A must finish before task B)"
      },
      {
        "title": "Executor",
        "body": "Runs tasks sequentially:\n\nAssigns tasks to agents\nTracks progress in real-time\nPersists state so projects survive restarts\nHandles blockers and errors"
      },
      {
        "title": "AgentOS",
        "body": "Orchestrates everything:\n\nRegister agents\nInitialize system\nRun projects\nGet status"
      },
      {
        "title": "Architecture",
        "body": "AgentOS (top-level orchestration)\n├── Agent (persistent worker)\n│   ├── Memory (lessons, capabilities, history)\n│   └── State (current task, progress)\n├── TaskRouter (goal decomposition)\n│   ├── Templates (planning, design, development, etc.)\n│   └── Matcher (task → agent assignment)\n└── Executor (task execution)\n    ├── Sequential runner\n    ├── Progress tracking\n    └── State persistence"
      },
      {
        "title": "State Persistence",
        "body": "All state is saved to the data/ directory:\n\n[agent-id]-memory.json — Agent knowledge base\n[agent-id]-state.json — Current agent status\n[project-id]-project.json — Project task list + status\n\nThis means:\n✅ Projects survive restarts\n✅ Agents remember past work\n✅ Resume mid-project seamlessly"
      },
      {
        "title": "File Structure",
        "body": "agent-os/\n├── core/\n│   ├── agent.js          # Agent class\n│   ├── task-router.js    # Task decomposition\n│   ├── executor.js       # Execution scheduler\n│   └── index.js          # AgentOS class\n├── ui/\n│   ├── dashboard.html    # Live progress UI\n│   ├── dashboard.js      # Dashboard logic\n│   └── style.css         # Styling\n├── examples/\n│   └── research-project.js  # Full working example\n├── data/                 # Auto-created (persistent state)\n└── package.json"
      },
      {
        "title": "AgentOS",
        "body": "new AgentOS(projectId?)\nregisterAgent(id, name, capabilities)\ninitialize()\nrunProject(goal, taskTypes)\ngetStatus()\ngetAgentStatus(agentId)\ntoJSON()"
      },
      {
        "title": "Agent",
        "body": "startTask(task)\nupdateProgress(percentage, message)\ncompleteTask(output)\nsetBlocker(message)\nrecordError(error)\nlearnLesson(category, lesson)\nreset()\ngetStatus()"
      },
      {
        "title": "TaskRouter",
        "body": "decompose(goal, taskTypes)\nmatchAgent(taskType)\ngetTasksForAgent(agentId, tasks)\ncanExecuteTask(task, allTasks)\ngetNextTask(tasks)\ncompleteTask(taskId, tasks, output)\ngetProjectStatus(tasks)"
      },
      {
        "title": "Executor",
        "body": "initializeProject(goal, taskTypes)\nexecute()\nexecuteTask(task)\ngetStatus()"
      },
      {
        "title": "Example: Research + Design + Development",
        "body": "See examples/research-project.js for the canonical example:\n\nnpm start\n\nThis demonstrates:\n\n✅ 3 agents with different capabilities\n✅ 12 tasks across 3 phases (planning, design, development)\n✅ Sequential execution with progress tracking\n✅ State persistence to disk\n✅ Final status report\n\nExpected output:\n\n✅ Registered 3 agents\n📋 Task Plan: 12 tasks\n🚀 Starting execution...\n✅ [Task 1] Complete\n✅ [Task 2] Complete\n...\n📊 PROJECT COMPLETE - 100% progress"
      },
      {
        "title": "What's Coming (v0.2+)",
        "body": "HTTP server + live dashboard\nParallel task execution (DAG solver)\nCapability learning system (auto-score agents)\nSmart agent routing (match to best agent)\nFailure recovery + retry logic\nCost tracking (token usage per agent)\nHuman checkpoints (review high-risk outputs)"
      },
      {
        "title": "Philosophy",
        "body": "Agents should remember what they learn.\n\nMost agent frameworks are stateless. Agent OS keeps persistent memory so agents:\n\nRemember — No redundant context resets\nLearn — Capability scores improve over time\nCoordinate — Shared state prevents duplication\nCost less — Less context = cheaper API calls"
      },
      {
        "title": "License",
        "body": "MIT\n\nBuilt with ❤️ by Nova for OpenClaw\n\nSee README.md and ARCHITECTURE.md for complete documentation."
      }
    ],
    "body": "Agent OS — Persistent Agent Operating System\n\nAgents that remember. Learn. Coordinate.\n\nWhat It Does\n\nAgent OS enables multi-agent project execution with persistent memory:\n\nAgent Memory — Each agent remembers past tasks, lessons learned, success rates\nTask Decomposition — Break high-level goals into executable task sequences\nSmart Routing — Assign tasks to agents based on capability fit\nExecution Tracking — Live progress board showing what every agent is doing\nState Persistence — Project state survives restarts (resume mid-project)\nQuick Start\nInstallation\nclawhub install nova/agent-os\n\nBasic Usage\nconst { AgentOS } = require('agent-os');\n\nconst os = new AgentOS('my-project');\n\n// Register agents with capabilities\nos.registerAgent('research', '🔍 Research', ['research', 'planning']);\nos.registerAgent('design', '🎨 Design', ['design', 'planning']);\nos.registerAgent('dev', '💻 Development', ['development']);\n\nos.initialize();\n\n// Run a project\nconst result = await os.runProject('Build a feature', [\n  'planning',\n  'design',\n  'development',\n]);\n\nconsole.log(result.progress); // 100\n\nCore Concepts\nAgent\n\nPersistent worker with:\n\nMemory — Past tasks, lessons learned, success rates\nState — Current task, progress, blockers\nCapabilities — What it's good at (research, design, development, etc.)\nTaskRouter\n\nDecomposes goals into executable tasks:\n\nBreaks \"Build a feature\" into: plan → design → develop → test\nMatches tasks to agents based on capability fit\nTracks dependencies (task A must finish before task B)\nExecutor\n\nRuns tasks sequentially:\n\nAssigns tasks to agents\nTracks progress in real-time\nPersists state so projects survive restarts\nHandles blockers and errors\nAgentOS\n\nOrchestrates everything:\n\nRegister agents\nInitialize system\nRun projects\nGet status\nArchitecture\nAgentOS (top-level orchestration)\n├── Agent (persistent worker)\n│   ├── Memory (lessons, capabilities, history)\n│   └── State (current task, progress)\n├── TaskRouter (goal decomposition)\n│   ├── Templates (planning, design, development, etc.)\n│   └── Matcher (task → agent assignment)\n└── Executor (task execution)\n    ├── Sequential runner\n    ├── Progress tracking\n    └── State persistence\n\nState Persistence\n\nAll state is saved to the data/ directory:\n\n[agent-id]-memory.json — Agent knowledge base\n[agent-id]-state.json — Current agent status\n[project-id]-project.json — Project task list + status\n\nThis means: ✅ Projects survive restarts ✅ Agents remember past work ✅ Resume mid-project seamlessly\n\nFile Structure\nagent-os/\n├── core/\n│   ├── agent.js          # Agent class\n│   ├── task-router.js    # Task decomposition\n│   ├── executor.js       # Execution scheduler\n│   └── index.js          # AgentOS class\n├── ui/\n│   ├── dashboard.html    # Live progress UI\n│   ├── dashboard.js      # Dashboard logic\n│   └── style.css         # Styling\n├── examples/\n│   └── research-project.js  # Full working example\n├── data/                 # Auto-created (persistent state)\n└── package.json\n\nAPI Reference\nAgentOS\nnew AgentOS(projectId?)\nregisterAgent(id, name, capabilities)\ninitialize()\nrunProject(goal, taskTypes)\ngetStatus()\ngetAgentStatus(agentId)\ntoJSON()\n\nAgent\nstartTask(task)\nupdateProgress(percentage, message)\ncompleteTask(output)\nsetBlocker(message)\nrecordError(error)\nlearnLesson(category, lesson)\nreset()\ngetStatus()\n\nTaskRouter\ndecompose(goal, taskTypes)\nmatchAgent(taskType)\ngetTasksForAgent(agentId, tasks)\ncanExecuteTask(task, allTasks)\ngetNextTask(tasks)\ncompleteTask(taskId, tasks, output)\ngetProjectStatus(tasks)\n\nExecutor\ninitializeProject(goal, taskTypes)\nexecute()\nexecuteTask(task)\ngetStatus()\n\nExample: Research + Design + Development\n\nSee examples/research-project.js for the canonical example:\n\nnpm start\n\n\nThis demonstrates:\n\n✅ 3 agents with different capabilities\n✅ 12 tasks across 3 phases (planning, design, development)\n✅ Sequential execution with progress tracking\n✅ State persistence to disk\n✅ Final status report\n\nExpected output:\n\n✅ Registered 3 agents\n📋 Task Plan: 12 tasks\n🚀 Starting execution...\n✅ [Task 1] Complete\n✅ [Task 2] Complete\n...\n📊 PROJECT COMPLETE - 100% progress\n\nWhat's Coming (v0.2+)\nHTTP server + live dashboard\nParallel task execution (DAG solver)\nCapability learning system (auto-score agents)\nSmart agent routing (match to best agent)\nFailure recovery + retry logic\nCost tracking (token usage per agent)\nHuman checkpoints (review high-risk outputs)\nPhilosophy\n\nAgents should remember what they learn.\n\nMost agent frameworks are stateless. Agent OS keeps persistent memory so agents:\n\nRemember — No redundant context resets\nLearn — Capability scores improve over time\nCoordinate — Shared state prevents duplication\nCost less — Less context = cheaper API calls\nLicense\n\nMIT\n\nBuilt with ❤️ by Nova for OpenClaw\n\nSee README.md and ARCHITECTURE.md for complete documentation."
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/cryptocana/agent-os",
    "publisherUrl": "https://clawhub.ai/cryptocana/agent-os",
    "owner": "cryptocana",
    "version": "0.1.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/agent-os",
    "downloadUrl": "https://openagent3.xyz/downloads/agent-os",
    "agentUrl": "https://openagent3.xyz/skills/agent-os/agent",
    "manifestUrl": "https://openagent3.xyz/skills/agent-os/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/agent-os/agent.md"
  }
}