Vanguard Uses Persistent Agent Memory to Track Competitor Strategies Over Time

Developers built Vanguard, an AI agent system designed to help analyze competitor strategies by leveraging persistent memory rather than one-off queries. The team found that standard retrieval-augmented generation (RAG) using top-k semantic search struggles to capture long-term cause-and-effect relationships between events. Vanguard addresses this by connecting historical data points to reveal broader strategic shifts, such as patterns behind pricing changes. A hybrid retrieval approach combining semantic similarity, keyword matching, and temporal relevance was found to produce more meaningful context than any single method alone. The project, built with Go, Python, FastAPI, Next.js, and TypeScript, concludes that structured memory architecture matters more than simply feeding an AI more raw context.