I Built an AI That Never Forgets

Short summary

MindVault solves persistent memory for AI agents using Cognee, a hybrid graph-vector knowledge store combining relational context with semantic search. The authors built a complete free stack with Groq, fastembed, and LanceDB, then detailed four major debugging challenges (rate limiting, embedding costs, dimension mismatch, API format) with solutions. Graph traversal strengthens memory retrieval compared to vector search alone, with full open-source code on GitHub.

• •Built MindVault on Cognee—open-source memory layer combining graph traversal + vector search for AI agents
• •Zero-cost implementation using Groq (free LLM tier), fastembed (local embeddings), LanceDB (vector DB)
• •Detailed 4 critical debugging lessons (rate limiting, embedding dimensions, API format negotiation) applicable to any Cognee deployment

Generated with AI, which can make mistakes.