Build a Conversational AI Agent on Harper in 5 Minutes

Building AI agents usually means stitching together a database, a vector store, a caching layer, an API server, and a deployment pipeline. Five services, five sets of credentials, and a weekend gone. We built an agent that does all of that on Harper — database, vector search, semantic cache, API, and deployment in one runtime. The full source is open. Here's how to get it running. What You Get A conversational chat agent powered by Claude with: Semantic memory — every message is embedded and stored. Ask a question from three conversations ago and it remembers, powered by Harper's built-in HNSW vector index. Semantic caching — ask the same question twice (or a rephrased version) and it answers instantly from Harper at $0.00 LLM cost. Over time, a popular agent builds a dense cache that handles most queries for free. Web search — Anthropic's built-in server-side search, no extra API key. Local embeddings — bge-small-en-v1.5 runs locally via llama.cpp inside Harper. No embedding API, no e