by splx-ai
A security scanner for your LLM agentic workflows
# Add to your Claude Code skills
git clone https://github.com/splx-ai/agentic-radarGuides for using ai agents skills like agentic-radar.
Last scanned: 5/3/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-03T06:26:17.218Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}agentic-radar is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by splx-ai. A security scanner for your LLM agentic workflows. It has 1,042 GitHub stars.
Yes. agentic-radar passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/splx-ai/agentic-radar" and add it to your Claude Code skills directory (see the Installation section above).
agentic-radar is primarily written in Python. It is open-source under splx-ai on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh agentic-radar against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
The Agentic Radar is designed to analyze and assess agentic systems for security and operational insights. It helps developers, researchers, and security professionals understand how agentic systems function and identify potential vulnerabilities.
It allows users to create a security report for agentic systems, including:
The comprehensive HTML report summarizes all findings and allows for easy reviewing and sharing.
Agentic Radar includes mapping of detected vulnerabilities to well-known security frameworks 🛡️.
If you only care about visualization, try out the Agentic Visualizer.
It is a web-based tool that allows you to visualize agentic workflows in a user-friendly way.
There are none! Just make sure you have Python (pip) installed on your machine.
pip install agentic-radar
# Check that it is installed
agentic-radar --version
Some features require extra installations, depending on the targeted agentic framework. See more below.
CrewAI extras are needed when using one of the following features in combination with CrewAI:
You can install Agentic Radar with extra CrewAI dependencies by running:
pip install "agentic-radar[crewai]"
[!WARNING] This will install the
crewai-toolspackage which is only supported on Python versions >= 3.10 and < 3.13. If you are using a different python version, the tool descriptions will be less detailed or entirely missing.
OpenAI Agents extras are needed when using one of the following features in combination with OpenAI Agents:
You can install Agentic Radar with extra OpenAI Agents dependencies by running:
pip install "agentic-radar[openai-agents]"
Agentic Radar now supports two main commands:
scanScan code for agentic workflows and generate a report.
agentic-radar scan [OPTIONS] FRAMEWORK:{langgraph|crewai|n8n|openai-agents|autogen}
Example:
agentic-radar scan langgraph -i path/to/langgraph/example/folder -o report.html
testTest agents in an agentic workflow for various vulnerabilities. Requires OPENAI_API_KEY set as environment variable.
agentic-radar test [OPTIONS] FRAMEWORK:{openai-agents} ENTRYPOINT_SCRIPT_WITH_ARGS
Example:
agentic-radar test openai-agents "path/to/openai-agents/example.py"
See more about this feature here.
Agentic Prompt Hardening automatically improves detected system prompts in your agentic workflow and displays them in the report. It transforms simple agent instructions into high-quality structured system prompts which follow best prompt engineering practices.
[!NOTE]
Currently supported frameworks (with more to come): OpenAI Agents, CrewAI, Autogen
It is quite straightforward to use:
Set your OPENAI_API_KEY environment variable by running export OPENAI_API_KEY=<api_key>.
Run Agentic Radar with the --harden-prompts flag, for example:
agentic-radar scan openai-agents --harden-prompts -i examples/openai-agents/
basic/lifecycle_example -o report.html
Agentic Radar now supports testing your agent workflows at runtime to identify critical vulnerabilities through simulated adversarial inputs.
This includes automated testing for:
Currently supported for:
The test command launches your agentic workflow with a test suite designed to simulate malicious or adversarial inputs. These tests are designed based on real-world attack scenarios aligned with the OWASP LLM Top 10.
[!NOTE]
This feature requires OPENAI_API_KEY or AZURE_OPENAI_API_KEY set as an environment variable. You can set it via command line or inside a .env file.
Test is run like:
agentic-radar test <framework> "<path/to/the/workflow/main.py any-necessary-args>"
For example:
agentic-radar test openai-agents "examples/openai-agents/basic/lifecycle_example.py"
The tool injects itself into the agentic workflow provided by user, detects necessary information and runs the prepared tests.
You can pass custom tests to Agentic Radar by creating a YAML file with the following structure:
include_default_tests: false
tests:
- name: TestA
input: Mention A.
success_condition: Attack succeeded if A is mentioned.
- name: TestB
input: Mention B.
success_condition: Attack succeeded if B is mentioned.
name is the name of the test, input is the input text to be passed to the agent, and success_condition is a description of what constitutes a