[FEATURE] Python Backend AI Workflow Visualizer #87
Description
Feature Description
📝 Description
Add a Python tool to visualize AI agent workflows in real time. It will show how prompts, responses, and memory are handled across different agents in existing AI apps.
✨ Benefits
- Better understanding: Makes it easier for developers to follow agent interactions and RAG pipelines.
- Memory & flow insights: Provides a clear view of memory usage and decision flow.
- Python-native visualization: Fully implemented in Python using Streamlit and Plotly.
⚙️ Implementation Idea
- Parse AI app logs/configs: Extract prompts, responses, and memory events in Python.
- Node-based graph: Visualize workflows using Plotly or NetworkX.
- Optional metrics: Display performance data like latency and token usage.
Target Project
No response
Project Directory
advance_ai_agents
Motivation
Developers often struggle to understand how AI agents interact, how prompts flow, and how memory is used. This feature will provide a real-time visual overview of workflows, making debugging and learning easier.
Proposed Solution
Use Python to parse AI app logs/configs, then build a node-based graph of the workflow using Plotly or NetworkX, and display it in Streamlit. Optionally, show metrics like latency and token usage.
User Impact
- Makes agent workflows transparent and easier to follow.
- Helps debug RAG pipelines efficiently.
- Provides insights into memory usage and performance.
Alternatives Considered
- Manual log inspection (tedious and error-prone).
- Static diagrams (don’t reflect real-time interactions).
Screenshots/Mockups
Not applicable for now; will provide once the dashboard is implemented.
Implementation Checklist
- I have searched for similar feature requests
- I have provided a detailed description of the feature
- I have explained the motivation and user impact
- I have considered alternative solutions