arthurpanhku

arthurpanhku / Arthor-Agent

Public

AI agent for security teams: automate assessment of documents, questionnaires & reports. Multi-format parsing, RAG knowledge base, OpenAI/Ollama. Risks, compliance gaps, remediations. MIT.

ai-agent compliance document-parsing fastapi llm
10
2
100% credibility
Found Mar 07, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Arthor Agent is an AI tool that automates security reviews of documents like questionnaires and reports by comparing them to uploaded policies and generating structured risk assessments with remediation advice.

How It Works

1
🔍 Discover Arthor Agent

You find this friendly AI helper that checks security papers for risks and gaps, perfect for your team's busy reviews.

2
📥 Download it easily

Grab the ready package and place it on your computer, no hassle.

3
🚀 Launch with one click

Run the simple start button, and your dashboard pops up ready to use.

4
📚 Add your guidelines

Upload your company's security rules and policies so the helper knows your standards.

5
📄 Upload a document

Choose a questionnaire or report file, pick an expert role like auditor, and hit review – exciting part begins!

6
⏳ Watch it analyze

Sit back as the AI reads your file, compares to rules, and builds insights.

✅ Receive clear report

Get a neat summary of risks, missing checks, and easy fix steps to keep your projects secure.

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AI-Generated Review

What is Arthor-Agent?

Arthor-Agent is a Python-based AI tool that automates security reviews of documents like questionnaires, design docs, and compliance reports. Upload PDFs, Word files, Excel sheets, or PPTs via its REST API or Streamlit dashboard; it parses them, queries a RAG knowledge base of your policies, and uses OpenAI or local Ollama models to output structured JSON/Markdown reports flagging risks, gaps, and remediations. Deploy with one Docker command and integrate as an MCP skill for agent github claude or OpenClaw workflows.

Why is it gaining traction?

It stands out with agent-ready MCP support for chaining into agent github openai or claude setups, plus local LLM runs via Ollama to avoid vendor lock-in. Developers notice the quick one-click deploy.sh, built-in Streamlit UI for assessments and KB management, and security features like RBAC and prompt guards—ideal for agent security bench testing without setup hassle.

Who should use this?

Security engineers buried in vendor questionnaires or pre-prod design reviews, compliance teams scaling SOC2/ISO audits across projects, and DevSecOps leads building agent security aws pipelines. Perfect for enterprises where manual doc triage bottlenecks releases.

Verdict

Try it for prototyping agent security guard workflows—solid docs, tests, and MIT license make it dev-friendly despite 10 stars and 1.0% credibility score signaling early maturity. Polish for prod with persistent storage, but great agent github repo starter for security automation.

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