AI45Lab

AI45Lab / TrinityGuard

Public

TrinityGuard: A Unified Framework for Safeguarding Multi-Agent System Safety

214
23
100% credibility
Found Feb 17, 2026 at 64 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

TrinityGuard is a comprehensive safety toolkit for testing and monitoring multi-agent AI systems against 20 types of security risks across single-agent, inter-agent, and system-level threats.

How It Works

1
🔍 Discover TrinityGuard

You learn about a helpful toolkit that checks AI helper teams for hidden dangers before they cause trouble.

2
📥 Get the toolkit

Download the safety checker and set it up on your computer in a few simple steps.

3
🔗 Connect your AI team

Link the toolkit to your group of AI helpers so it can watch how they work together.

4
🛡️ Run safety scans

Push the button to test your AI team against common risks like sneaky instructions or bad teamwork.

5
👀 Watch them work safely

Turn on live monitoring to catch problems as your AI helpers chat and solve tasks.

6
📊 Review safety reports

Get clear summaries of risks found, with tips on how to make your team stronger.

✅ Safe AI team ready

Your AI helpers now work securely, giving you peace of mind for all their adventures.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 64 to 214 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is TrinityGuard?

TrinityGuard is a Python framework for safeguarding multi-agent systems with pre-deployment testing and runtime monitoring across 20 security risks, from single-agent jailbreaks to system-wide cascading failures. Wrap any AutoGen-based setup, run tests like `safety_mas.run_manual_safety_tests(["jailbreak"])` to catch vulnerabilities early, then enable monitoring with `start_runtime_monitoring()` before executing tasks via `run_task()`. It delivers structured reports, alerts, and extensible plugins without rewriting your agents.

Why is it gaining traction?

It covers all 20 risks in a unified way with LLM judges for smart analysis and fallback patterns, plus progressive monitoring that activates checks dynamically to cut compute costs. Framework-agnostic design means quick integration with AutoGen group chats or workflows, and custom logic for monitor selection keeps it flexible for real workloads. Developers grab it for the complete risk library and easy extensibility via config files.

Who should use this?

AI engineers building production multi-agent systems with AutoGen for tasks like data analysis or automation pipelines. Security teams auditing agent robustness against prompt injections or goal drift in collaborative setups. Researchers prototyping safe MAS before scaling.

Verdict

Promising alpha for multi-agent safety in Python—61 stars and 1.0% credibility score reflect early stage, but full 20-risk implementation and solid docs make it usable now. Test it if you're deploying agents; contribute to push maturity.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.