WFGY

What is WFGY?

WFGY provides an open-source framework designed to assist developers in debugging Large Language Model (LLM) agents and Retrieval Augmented Generation (RAG) pipelines. It offers a structured approach to identifying and resolving issues within these complex systems. The core of WFGY is its 16-mode ProblemMap, a visualization tool that helps users systematically diagnose problems by categorizing them across different facets of agent and RAG functionality. This map aids in pinpointing the root cause of unexpected behavior, whether it stems from retrieval, generation, or agentic decision-making processes.

Complementing the ProblemMap, WFGY includes practical triage checklists. These checklists guide users through a series of diagnostic steps, ensuring that common issues are addressed efficiently and thoroughly. This methodical approach is particularly useful when dealing with the inherent unpredictability of LLM-based applications. By breaking down the debugging process into manageable steps, developers can save significant time and effort in troubleshooting, leading to more stable and reliable AI systems.

The tool is ideal for developers and researchers working with LLM agents and RAG systems who require a systematic method for debugging. Its open-source nature allows for flexibility and customization, enabling users to integrate it into their existing workflows. WFGY aims to demystify the debugging process for complex AI pipelines, making it more accessible and manageable for a wider range of technical users.

Key features

• 16-mode ProblemMap
• Triage checklists
• LLM agent debugging
• RAG pipeline analysis
• Open-source framework
• Problem visualization

Use cases

• Diagnosing retrieval failures in RAG.
• Debugging LLM agent decision-making logic.
• Identifying generation errors in AI responses.
• Systematically troubleshooting complex AI pipelines.
• Improving the reliability of LLM applications.

Pros & cons

FAQ