Intelligent User Feedback Analysis System Based on Multi-Agent Workflow

The Intelligent User Feedback Analysis system introduced in this article is an open-source intelligent user feedback analysis system entirely built with Python and requiring no paid APIs. It uses multi-agent workflows to automatically process feedback such as app reviews and support emails, enabling issue classification, ticket generation, and metric tracking, and provides a Streamlit visualization dashboard to address the pain points of traditional manual analysis, which is time-consuming and prone to omissions.

In the digital product ecosystem, user feedback is crucial for product iteration, but the massive volume of feedback such as app reviews and support emails makes traditional manual analysis both time-consuming and prone to missing important information. To address this, the open-source Intelligent User Feedback Analysis project was developed, providing an intelligent solution that does not rely on paid APIs.

The system uses a multi-agent architecture, breaking down the analysis process into specialized stages:

1. Data Ingestion Agent: Collects raw feedback from multiple channels, cleans and standardizes it;
2. Classification Agent: Uses local open-source models to classify feedback in multiple dimensions such as issue type, urgency level, and sentiment tendency;
3. Extraction Agent: Identifies structured information like user devices and app versions. Technical Highlights: Entirely based on an open-source tech stack, runs models locally to eliminate API costs, ensures data privacy, and supports offline operation; optimizes inference performance via quantization techniques, and uses message queues and state management for agent collaboration to ensure scalability.

After feedback is classified and extracted, the ticket generation agent automatically creates structured tickets (including issue descriptions, tags, priorities, etc.) according to preset rules; supports ticket aggregation to merge multiple feedback entries for the same issue and avoid duplicate processing; real-time monitoring of feedback trends, classification distribution, response time, and other metrics via the Streamlit dashboard helps teams optimize resource allocation.

The system is applicable to multiple scenarios:

• Mobile app teams: Monitor app store reviews to promptly identify issues with new versions;
• SaaS teams: Handle multi-channel support requests and prioritize high-priority issues;
• E-commerce sector: Analyze product reviews and inquiries to identify quality and logistics pain points;
• Game industry: Track player feedback to find balance issues and bugs. The trend analysis capability helps teams shift from passive response to proactive prevention.

Deployment is simple: Install a Python environment + download pre-trained models to run; detailed configuration documents are provided to adjust classification rules, ticket templates, and dashboard layouts; modular design facilitates expansion, allowing addition of new agents, integration of data sources, and support for a plugin mechanism to connect custom models or notification channels.

The project is fully open-source with clear code and comprehensive documentation, providing practical tools and learning cases for the community; in the future, as open-source model capabilities improve, local deployment solutions will become more popular. This project proves that powerful AI applications can be built without expensive API budgets, lowering the entry barrier for AI technology.

Intelligent User Feedback Analysis demonstrates the potential of multi-agent architecture in text analysis. By breaking down complex tasks to achieve high-quality automated analysis while maintaining process control and data security, it is an excellent open-source solution for building intelligent feedback processing capabilities.