Arthor-Agent

What is Arthor Agent? | Arthor Agent 是什么？

Arthor Agent 是面向安全团队的 AI 助手。它自动化审阅与安全相关的文档、表格和报告（如安全问卷、设计文档、合规证据），结合策略与知识库进行比对，并产出结构化评估报告，包含风险项、合规差距与整改建议。

🚀 Agent Ready: 支持 Model Context Protocol (MCP)，可作为“技能”被 OpenClaw、Claude Desktop 等智能体直接调用。

• 多格式输入：PDF、Word、Excel、PPT、文本，解析为统一格式供大模型使用。

• 知识库（RAG）：上传策略与合规文档，评估时作为参考检索。

• 多模型支持：通过统一接口使用 OpenAI、Claude、千问或 Ollama（本地）。

• 结构化输出：JSON/Markdown 报告，含风险项、合规差距与可执行整改建议。

适合需要在大量项目中扩展安全评估、而人力有限的企业。

Arthor Agent is an AI-powered assistant for security teams. It automates the review of security-related documents, forms, and reports (e.g. Security Questionnaires, design docs, compliance evidence), compares them against your policy and knowledge base, and produces structured assessment reports with risks, compliance gaps, and remediation suggestions.

🚀 Agent Ready: Supports Model Context Protocol (MCP) to be used as a "skill" by OpenClaw, Claude Desktop, and other autonomous agents.

• Multi-format input: PDF, Word, Excel, PPT, text — parsed into a unified format for the LLM.

• Knowledge base (RAG): Upload policy and compliance documents; the agent uses them as reference when assessing.

• Multiple LLMs: Use OpenAI, Claude, Qwen, or Ollama (local) via a single interface.

• Structured output: JSON/Markdown reports with risk items, compliance gaps, and actionable remediations.

Ideal for enterprises that need to scale security assessments across many projects without proportionally scaling headcount.

Why Arthor Agent? | 为什么用 Arthor Agent？

完整问题陈述与产品目标见
See the full problem statement and product goals in

Architecture | 架构

Arthor Agent 以编排器为核心，协调解析、知识库（RAG）、技能（如问卷与策略比对）与 LLM。可按环境选用云端或本地大模型，以及可选集成（如 AAD、ServiceNow）。

Arthor Agent is built around an orchestrator that coordinates parsing, the knowledge base (RAG), skills, and the LLM. You can use cloud or local LLMs and optional integrations (e.g. AAD, ServiceNow) as your environment requires.

flowchart TB
    subgraph User["👤 User / Security Staff | 用户"]
    end
    subgraph Access["Access Layer | 接入层"]
        API["REST API / MCP"]
    end
    subgraph Core["Arthor Agent Core | 核心"]
        Orch["Orchestrator | 编排"]
        Mem["Memory | 记忆"]
        Skill["Skills | 技能"]
        KB["Knowledge Base (RAG) | 知识库"]
        Parser["Parser | 解析"]
    end
    subgraph LLM["LLM Layer | 大模型层"]
        Abst["LLM Abstraction | LLM 抽象"]
    end
    subgraph Backends["LLM Backends | 后端"]
        Cloud["OpenAI / Claude / Qwen"]
        Local["Ollama / vLLM"]
    end

    User --> API
    API --> Orch
    Orch <--> Mem
    Orch --> Skill
    Orch --> KB
    Orch --> Parser
    Orch --> Abst
    Abst --> Cloud
    Abst --> Local

数据流（简要）| Data flow (simplified):

1. 用户上传文档，可选选择场景或项目。 / User uploads documents and selects scenario.

2. Parser 解析器将文件（PDF、Word、Excel、PPT 等）转为统一文本/Markdown。 / Parser converts files to text/Markdown.

3. 编排器从知识库（RAG）加载相关片段并调用技能。 / Orchestrator loads KB chunks (RAG) and invokes Skills.

4. LLM（OpenAI、Ollama 等）生成结构化结论。 / LLM produces structured findings.

5. 返回评估报告（风险、合规差距、整改建议）。 / Returns assessment report (risks, gaps, remediations).

详细架构与组件说明见
Detailed architecture:

Features | 功能概览

路线图（如 AAD/SSO、ServiceNow 集成）见 SPEC.md。

Quick Start | 快速开始

🚀 New Streamlit Frontend: Run the modern dashboard for a full experience:

pip install streamlit plotly pandas
streamlit run frontend/Home.py

📹 Demo Walkthrough | 演示视频

Screenshots | 界面截图

Dashboard view

Assessment Workbench view

Knowledge Base Management view

Option A: Docker（推荐 / Recommended）

前置 Prerequisites: Docker & Docker Compose.

仅启动 API（使用 OpenAI 或本机已运行的 Ollama）： API only (use OpenAI or Ollama on host):

git clone https://github.com/arthurpanhku/Arthor-Agent.git
cd Arthor-Agent
docker compose up -d

需要连同 Ollama 容器 一起启动时： With Ollama in Docker:

docker compose -f docker-compose.yml -f docker-compose.ollama.yml up -d
docker compose exec ollama ollama pull llama2

• API 文档 (Swagger): http://localhost:8000/docs

• 健康检查 (Health): http://localhost:8000/health

Option B: Python（本地开发 / Local Dev）

前置 Prerequisites: Python 3.10+. Optional: Ollama (ollama pull llama2).

git clone https://github.com/arthurpanhku/Arthor-Agent.git
cd Arthor-Agent
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env        # Edit if needed: LLM_PROVIDER=ollama or openai
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

• API docs: http://localhost:8000/docs · Health: http://localhost:8000/health

示例：提交评估 | Example: submit an assessment

可使用仓库内 examples/ 下的示例文件快速试跑。 You can use the sample files in examples/ to try the API.

# 使用示例文本 / Use sample file from repo
curl -X POST "http://localhost:8000/api/v1/assessments" \
  -F "files=@examples/sample.txt" \
  -F "scenario_id=default"

# 响应：{ "task_id": "...", "status": "accepted" } — 用返回的 task_id 查询结果
# Get the result (replace TASK_ID with the returned task_id)
curl "http://localhost:8000/api/v1/assessments/TASK_ID"

示例：上传知识库并检索 | Example: upload to KB and query

# 使用示例策略文件 / Use sample policy from repo
curl -X POST "http://localhost:8000/api/v1/kb/documents" -F "file=@examples/sample-policy.txt"

# 检索（RAG）/ Query the KB (RAG)
curl -X POST "http://localhost:8000/api/v1/kb/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the access control requirements?", "top_k": 5}'

Project layout | 项目结构

Arthor-Agent/
├── app/                  # 应用代码
│   ├── api/              # REST 路由：评估、知识库、健康检查
│   ├── agent/            # 编排与评估流水线
│   ├── core/             # 配置 (pydantic-settings)
│   ├── kb/               # 知识库 (Chroma, 分块, RAG)
│   ├── llm/              # LLM 抽象 (OpenAI, Ollama)
│   ├── parser/           # 文档解析 (PDF, Word, Excel, PPT, 文本)
│   ├── models/           # Pydantic 模型
│   └── main.py
├── tests/                # 自动化测试 (pytest)
├── examples/             # 示例文件（问卷、策略样本）
├── docs/                 # 设计与规格文档
│   ├── 01-architecture-and-tech-stack.md
│   ├── 02-api-specification.yaml
│   ├── 03-assessment-report-and-skill-contract.md
│   ├── 04-integration-guide.md
│   ├── 05-deployment-runbook.md
│   └── schemas/
├── .github/              # Issue/PR 模板、CI (Actions)
├── Dockerfile
├── docker-compose.yml    # 仅 API
├── docker-compose.ollama.yml  # API + Ollama 可选
├── CONTRIBUTING.md       # 贡献指南
├── CODE_OF_CONDUCT.md    # 行为准则
├── CHANGELOG.md
├── SPEC.md
├── LICENSE
├── SECURITY.md
├── requirements.txt
├── requirements-dev.txt  # 测试与开发依赖
├── pytest.ini
└── .env.example

Configuration | 配置

完整选项见
See

Documentation and PRD | 文档与规格

• ARCHITECTURE.md — System architecture: high-level diagram, Mermaid views, component design, data flow, security.系统架构：高层图、Mermaid 视图（逻辑/组件/时序/集成/部署）、组件设计、数据流、安全架构。

• SPEC.md — Product requirements: problem statement, solution, features, security controls.产品需求与规格：问题陈述、方案、架构摘要、功能、安全控制与开放问题。

• CHANGELOG.md — Version history; Releases.版本历史；发布说明。

• Design docs docs/：Architecture, API spec (OpenAPI), contracts, integration guides (AAD, ServiceNow), deployment runbook. Q1 Launch Checklist: docs/LAUNCH-CHECKLIST.md.架构与技术栈、API 规范、评估报告与 Skill 契约、集成指南、部署手册。Q1 发布清单：docs/LAUNCH-CHECKLIST.md。

Contributing | 参与贡献

English: Issues and Pull Requests are welcome. Please read CONTRIBUTING.md for setup, tests, and commit guidelines. By participating you agree to the CODE_OF_CONDUCT.md.

🤖 AI-Assisted Contribution: We encourage using AI tools to contribute! Check out CONTRIBUTING_WITH_AI.md for best practices.

中文：欢迎提交 Issue 与 Pull Request。请先阅读 CONTRIBUTING.md 了解开发环境、测试与提交规范。参与即视为同意 CODE_OF_CONDUCT.md 行为准则。

🤖 AI 辅助贡献：我们也鼓励使用 AI 工具参与贡献！请查看 CONTRIBUTING_WITH_AI.md 获取最佳实践指南。

Security | 安全

• 漏洞报告 / Vulnerability reporting：负责任披露请见 SECURITY.md。See SECURITY.md for responsible disclosure.

• 安全需求 / Security requirements：项目遵循 SPEC §7.2 中定义的安全控制（身份、数据保护、应用安全、运维、供应链）。Follows security controls in SPEC §7.2.

License | 许可证

本项目采用 MIT License，详见 LICENSE 文件。This project is licensed under the MIT License — see the LICENSE file for details.

Star History

Author and links | 作者与链接

• 作者 Author: PAN CHAO (Arthur Pan)

• 仓库 Repository: github.com/arthurpanhku/Arthor-Agent

• 规格与设计文档 SPEC and design docs: 见上文链接。See links above.

若你在组织中使用 Arthor Agent 或希望参与贡献，欢迎通过 GitHub Discussions 或 Issues 联系我们。If you use Arthor Agent in your organization or contribute back, we’d love to hear from you (e.g. via GitHub Discussions or Issues).