Agastya910

Agastya910 / agentarmor

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8-layer defense-in-depth security for agentic AI. Covers OWASP ASI Top 10 across ingestion, storage, context, planning, execution, output, inter-agent, and identity layers.

22
1
100% credibility
Found Mar 15, 2026 at 22 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AgentArmor is a security toolkit that adds multiple protective layers to AI agents to prevent issues like prompt tricks, data leaks, and unsafe actions.

How It Works

1
🔍 Discover AgentArmor

You hear about a simple protector that keeps your AI helpers safe from tricks and leaks while building smart assistants.

2
📥 Get it ready

Download the protector and set it up quickly on your computer, like installing a helpful app.

3
🛡️ Turn on safety shields

Switch on multiple layers of protection that watch every step your AI takes, feeling secure right away.

4
🔗 Link your AI helper

Connect your AI agent to the protector and pick easy rules for what it can and can't do.

5
🧪 Test for dangers

Run quick checks with pretend threats to see the protector block them in action.

✅ Safe AI in action

Your AI helper now works confidently, staying protected from risks while helping you every day.

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AI-Generated Review

What is agentarmor?

AgentArmor is a Python security framework for agentic AI apps, delivering 8-layer defense-in-depth across ingestion, storage, context, planning, execution, output, inter-agent, and identity layers. It covers the OWASP ASI Top 10 risks in the full data lifecycle—at rest, in transit, and in use—via a unified pipeline that intercepts actions and enforces policies. Users get CLI tools like `agentarmor scan` and `serve` for proxy mode, plus YAML configs for custom rules.

Why is it gaining traction?

Unlike point solutions for prompt guards or output filters, AgentArmor secures the entire agentic stack end-to-end, with integrations for LangChain, OpenAI, MCP servers, and OpenClaw identity files. Standout hooks include MCP server scanning for risks like insecure tools or HTTP, plus a red team suite to test OWASP ASI coverage. Developers notice instant value from decorators like `@armor.shield` on tools and tamper-proof audit logs.

Who should use this?

AI engineers building multi-agent systems with LangChain or CrewAI, especially in finance or RAG pipelines needing PII redaction and execution sandboxes. Teams evaluating MCP tools before deployment, or securing OpenClaw agents against host compromise. Ideal for devs hardening production agents without stitching together guards.

Verdict

Early alpha (20 stars, 1.0% credibility) with solid docs and tests, but low maturity means expect rough edges—prototype it for agentic workflows now. Worth watching for layer 8 problem GitHub solutions in agentic AI.

(198 words)

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