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Building an AI-Powered Product Price Insurance Agent with LangGraph & Streamlit
- Creating an AI-powered Product Price Insurance Agent using LangGraph & Streamlit to find market values for insurance claims and price comparisons.
- Solution involves automation with AI to replace manual searching, copying/pasting, and inconsistent data formats.
- Implemented a 3-stage workflow with LangGraph providing state management, error handling, and conditional routing.
- Tech stack includes LangGraph for orchestration, Bright Data for web scraping, Google Gemini for LLM, Streamlit for chat interface, and Python for implementation.
- Detailed implementation covers state definition, product search, price extraction, and report generation.
- Graph assembly using LangGraph for creating and compiling the insurance price analysis graph.
- Streamlit interface designed as a chat-style app for user interaction with real-time progress updates.
- Implemented a testing suite to validate each node individually for reliability.
- Results show successful product URL finding, accurate price extraction, and professional report generation.
- Key learnings include the importance of structured outputs, power of MCP integration, simplicity of LangGraph, and real-time progress updates.
- Real-world applications include insurance claims, procurement decisions, market research, and consumer shopping.
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