AI Skill Hub 强烈推荐:体化AI操作系统 是一款优质的Agent工作流。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。
体化AI操作系统 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
体化AI操作系统 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install phyagentos
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install phyagentos
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/PhyAgentOS/PhyAgentOS
cd PhyAgentOS
pip install -e .
# 验证安装
python -c "import phyagentos; print('安装成功')"
# 命令行使用
phyagentos --help
# 基本用法
phyagentos input_file -o output_file
# Python 代码中调用
import phyagentos
# 示例
result = phyagentos.process("input")
print(result)
# phyagentos 配置文件示例(config.yml) app: name: "phyagentos" debug: false log_level: "INFO" # 运行时指定配置文件 phyagentos --config config.yml # 或通过环境变量配置 export PHYAGENTOS_API_KEY="your-key" export PHYAGENTOS_OUTPUT_DIR="./output"
<p> <a href="https://github.com/PhyAgentOS/PhyAgentOS-core/stargazers"> <img src="https://img.shields.io/github/stars/PhyAgentOS/PhyAgentOS-core?style=social" alt="Stars"> </a> <a href="https://github.com/PhyAgentOS/PhyAgentOS-core/network/members"> <img src="https://img.shields.io/github/forks/PhyAgentOS/PhyAgentOS-core?style=social" alt="Forks"> </a> </p> <p> <img src="https://img.shields.io/badge/Python-≥3.11-3776AB?logo=python&logoColor=white" alt="Python"> <img src="https://img.shields.io/badge/Version-v1.0.0-47A882" alt="Version"> <img src="https://img.shields.io/badge/License-MIT-3DA639" alt="License"> <a href="https://arxiv.org/pdf/2607.16636"> <img src="https://img.shields.io/badge/Tech_Report-arXiv-b31b1b?logo=arxiv&logoColor=white" alt="Tech Report"> </a> <a href="https://phy-agent-os.net/"> <img src="https://img.shields.io/badge/Website-online-FF6B35" alt="Website"> </a> <a href="https://github.com/PhyAgentOS/PhyAgentOS-core"> <img src="https://img.shields.io/badge/PRs-Welcome-2EA44F" alt="PRs"> </a> <p> <a href="https://space.bilibili.com/3546880296355920?spm_id_from=333.1007.0.0"> <img src="https://img.shields.io/badge/Bilibili-00A1D6?logo=bilibili&logoColor=white" alt="Bilibili"> </a> <a href="https://www.xiaohongshu.com/user/profile/673d83e3000000001c01a183"> <img src="https://img.shields.io/badge/Xiaohongshu-FF2442?logo=xiaohongshu&logoColor=white" alt="Xiaohongshu"> </a> <a href="https://x.com/phyagentos"> <img src="https://img.shields.io/badge/X-000000?logo=x&logoColor=white" alt="X"> </a> <a href="https://www.linkedin.com/in/phyagent-os-252372401/"> <img src="https://img.shields.io/badge/LinkedIn-0A66C2?logo=linkedin&logoColor=white" alt="LinkedIn"> </a> <a href="https://discord.gg/YJztZ4wUM"> <img src="https://img.shields.io/badge/Discord-5865F2?logo=discord&logoColor=white" alt="Discord"> </a> </p> </p> <p> <sub><a href="README.md">English</a> · <a href="README_zh.md">中文</a> · <a href="docs/README.md">Documentation</a></sub> </p> </div>
---
PhyAgentOS is an agent framework for embodied tasks. The Agent plans high-level Tool calls, the Forge Tool API reports what Gateway executed, the observation collector captures before/after evidence, and the task-level verifier decides whether the user-visible goal was actually achieved.
| Area | Current capability |
|---|---|
| Forge contract | One Query/Action/Session Tool API plane through /tools and /invocations. |
| Async orchestration | Query is synchronous; Action and Session admission return invocation IDs whose state is reconciled through /invocations. |
| Identity validation | Agent task_id, revision_id, Query record ID, Gateway invocation_id, and attempt_id remain distinct. |
| Evidence | Async /ws/images and /ws/state collection with bounded latest-frame buffers, media validation, SHA-256, and per-source sequence boundaries. |
| Verification | off, audit, enforce, and recovery modes with structured per-criterion verdicts. |
| Recovery | Bounded append-only PlanRevisions on the same task, with deadlines and no blind retry of unknown effects. |
| Persistence | SQLite WAL AgentTask event log plus workspace-relative evidence; existing evolution data remains readable. |
| Task experience | Explicit Skill activation, redacted AgentTask episodes, asynchronous reflection, clustered scoped Lessons, and guarded Skill promotion. |
| Skill Runtime | Manifest-v2 bundles, SHA-256 inventories, safe transactional installation, named Dora profiles, persistent health state, and explicit Registry resolution. |
| Agent platform | CLI and multi-channel gateway, provider abstraction, tools, skills, MCP, memory, Cron, Heartbeat, and knowledge workspaces. |
```bash git clone https://github.com/PhyAgentOS/PhyAgentOS-core.git cd PhyAgentOS-core python -m pip install -e .
paos skill install /path/to/<skill-name>-<version>.tar.gz --local paos skill inspect <skill-name> paos skill start <skill-name> --profile <profile> paos skill status <skill-name>
paos skill switch <other-skill-name> --profile <profile> paos skill logs <skill-name> paos skill stop <skill-name>
paos forge-node install <skill-name> <node-id> --archive /path/to/<node>.tar.gz paos forge-node verify <skill-name> <node-id> ```
Each Forge Skill bundle declares its workflow document, required Tool IDs, named runtime profiles, and exact platform/architecture Node locks. Each locked archive has an exact SHA-256 and contains one named root-level executable. For Registry Node downloads, the verified Skill lock supplies the digest when the Registry omits that duplicate field, and the exact size is resolved before the download begins; installation records and verifies the extracted binary hash. python scripts/package_skill.py <bundle-dir> --output-dir <directory> creates a deterministic bundle for publication. The PhyAgentOS source and release packages do not bundle concrete Forge Skills, Forge nodes, models, or simulation assets; obtain only the Skills needed for a deployment and install them explicitly. The integration development guide documents Bundle layout, local validation, immutable publication order, and Registry acceptance.
The configuration file is serialized in camelCase; snake_case keys are also accepted.
{
"agents": {
"defaults": {
"workspace": "~/.PhyAgentOS/workspace",
"model": "openrouter/openai/gpt-4o-mini",
"provider": "openrouter"
},
"verification": {
"serviceEnabled": true,
"evidenceRetention": "failed",
"maxReplansPerEpisode": 2,
"maxVerifierCallsPerRun": 50
},
"evolution": {
"enabled": true,
"scope": "verified_forge_lineage",
"promotionMode": "guarded_auto",
"minSuccessfulEpisodes": 3,
"minLessonEpisodes": 3,
"maxLessonsPerSkill": 8,
"maxEvolutionCallsPerRun": 20
}
},
"providers": {
"openrouter": {
"apiKey": "YOUR_API_KEY"
}
},
"forge": {
"requestTimeoutS": 10,
"pollIntervalS": 0.5,
"executionTimeoutS": 300,
"evidence": {
"requiredImageSources": ["front"],
"captureTimeoutS": 5,
"postCaptureTimeoutS": 5,
"connectionTimeoutS": 2,
"maxArtifactBytes": 8388608,
"associationQuality": "best_effort"
}
},
"resourceRegistry": {
"url": "https://paos-resource-manager.dev.x-era.com"
}
}
The front source is only an example. resourceRegistry.url selects a generic package registry; it may be empty when all artifacts are installed from local bundles or a supplied static index. PAOS connects only to the Gateway URL in the manifest of the explicitly started, healthy Skill Runtime. It never starts or downloads a concrete Skill merely because the Agent starts.
paos agent -m "Inspect Forge capabilities, then place the object in the target area and verify the visible result."
paos gateway ```
Use paos status to inspect the local model/workspace configuration. Use forge_tool_context for a live ToolSpec, binding, readiness, endpoint status, and frame profile.
高质量的AI工作流自动化工具
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,体化AI操作系统 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | PhyAgentOS |
| 原始描述 | 开源AI工作流:PhyAgentOS is a self-evolving embodied AI operating system built on agentic work。⭐248 · Python |
| Topics | embodied-aiagentpython |
| GitHub | https://github.com/PhyAgentOS/PhyAgentOS |
| License | MIT |
| 语言 | Python |
收录时间:2026-06-01 · 更新时间:2026-06-01 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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