Agentic Radar enables deep parsing of popular frameworks such as CrewAI and LangGraph through an adapter pattern, a multi-framework support capability that significantly reduces the difficulty of understanding heterogeneous AI systems. The tool identifies organizational features specific to each framework, such as CrewAI's agent-task model or LangGraph's state machine definition, and translates them into standardized dependency representations.
Technical realization features include:
- Automatically detect the type of frame used by the project
- Parsing a framework-specific DSL or decorator syntax
- Mapping framework elements to a unified visualization model
For example, when analyzing the LangGraph project, it can accurately present the transfer conditions between state nodes; when dealing with the CrewAI code, it can show the execution order of the task pipeline. This abstraction capability allows team members to get a full picture of the system through visual reports without going into the details of each framework, greatly reducing the threshold for collaborative development and learning new frameworks.
This answer comes from the articleAgentic Radar: Visualization Tool for Agentic Workflow Security Inspection》
