AIエージェント
deepteam
Open-source red-teaming framework that stress-tests LLMs and AI agents.
★ 3.0k スターApache-2.0
ガイドを見る →PyRITは、microsoftが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: microsoft/PyRIT）。GitHubスターは4,592、フォークは936で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
Microsoft's open framework for red-teaming generative AI systems.
Start with one concrete task rather than a grand architecture.
Clone the repo and check the examples folder for a quickstart.
Install packages, set environment variables, and point it at your model.
Execute the demo workflow, then adapt it to your own data.
Systematic attack generation.
Find issues before deployment.
Built by Microsoft's AI red team.
Add your own attack strategies.
Aligned with RAI practices.
Free for commercial security work.
Download the source and move into the project folder.
git clone https://github.com/microsoft/PyRIT.git
cd PyRITCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pymicrosoft/PyRIT is its home on GitHub, with 936 forks so far. The contributors tab and recent commits show how actively it is maintained.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
The toolkit is free; only the models it guards might come from a paid provider.
PyRIT finds vulnerabilities; Guardrails enforces runtime protections. Use PyRIT during security review and Guardrails in production - they complement each other.
Follow the README quickstart, then browse the examples directory for patterns that match what you want to build.
Open-source red-teaming framework that stress-tests LLMs and AI agents.
JavaScript framework for multi-agent systems with a Kanban-inspired design.
Microsoft's trainer for AI agents - reinforcement learning for almost any agent framework
Go framework for production agent systems: graphs, tools, memory, A2A, MCP.
Test prompts, agents and RAGs — LLM evals, red teaming and model comparisons from the CLI
Microsoft's Playwright-powered MCP server for browser automation.
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — PyRITが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でPyRITとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
Koog is a JVM (Java and Kotlin) framework for building predictable, fault-tolerant and enterprise-ready AI agents across all platforms – from backend service…
Convene 18 legendary thinkers as an AI council that debates your hardest questions — inside Claude Code
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
LongMemory: local-first memory for AI agents with temporal truth, provenance, and hybrid recall
Rivet is Ironclad's visual IDE and TypeScript library for building complex AI agents and prompt chains
M-flow is a bio-inspired cognitive memory engine for AI agents — a graph alternative to flat RAG