What frameworks does Agentic Radar support?
Scan supports LangGraph, CrewAI, n8n, OpenAI Agents, and Autogen. Test currently supports OpenAI Agents. Prompt hardening supports OpenAI Agents, CrewAI, and Autogen.
@splx-ai
基本信息
A security scanner for your LLM agentic workflows
工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Agentic Radar is a security scanner designed to analyze and assess agentic workflows for security and operational insights. It helps developers, researchers, and security professionals understand how agentic systems function and identify potential vulnerabilities.
Install via pip, then use the scan or test commands. For example: agentic-radar scan langgraph -i path/to/folder -o report.html or agentic-radar test openai-agents "path/to/example.py". The test command requires OPENAI_API_KEY or AZURE_OPENAI_API_KEY set as an environment variable.
Scan supports LangGraph, CrewAI, n8n, OpenAI Agents, and Autogen. Test currently supports OpenAI Agents. Prompt hardening supports OpenAI Agents, CrewAI, and Autogen.
Just Python (pip) installed on your machine. Some features like runtime testing require `OPENAI_API_KEY` set as an environment variable.
The tool injects itself into the provided agentic workflow, launches it with simulated adversarial inputs (e.g., for prompt injection), and evaluates results using an oracle LLM based on success conditions aligned with OWASP LLM Top 10.
Yes, you can provide a YAML file with custom test names, inputs, and success conditions using the `--config` option.
Agentic Radar runs locally on your machine. Scans and tests process your code and agent workflows in-place; reports are generated as HTML files that you can save and share. No data is sent to external servers except API calls to OpenAI when using features that require an API key.
This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
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