Persistent AI Memory Explained (and Built): Cognee + Customer Support Tickets

In this quick walkthrough, we build a baseline stateless RAG support agent (answers each question in isolation)… then upgrade it with Cognee to unlock persistent memory. With Cognee, your agent can: ✅ remember past interactions across sessions ✅ build a knowledge graph from your data ✅ combine vector search + graph traversal for stronger retrieval ✅ improve over time with feedback loops ✅ generate more semantically relevant summaries (ex: recurring customer issues) This demo uses a Kaggle customer support ticket dataset and is fully reproducible. 📚 Reference tutorial (full write-up): https://www.cognee.ai/blog/tutorials/beyond-recall-building-persistent-memory-in-ai-agents-with-cognee ▶️ Chapters 0:00 Intro 0:21 Why stateless RAG breaks (no history, no relationships, no continuity) 0:37 How Cognee adds long-term memory (KG + vectors + feedback) 0:50 Setup: install deps + configure API keys 1:07 Download the support ticket dataset 1:18 Inspect the data (resolutions, response times, ratings, types, status, priority) 1:46 Preprocess: turn tickets into natural-language “documents” 2:00 Build the baseline stateless RAG agent 2:50 Ask questions + see the limitations (no follow-ups, no context reuse) 3:06 Upgrade to Cognee: ingest + persist memory 3:54 Cognify: build the knowledge graph (“under the hood”) 4:15 Visualize the graph 5:00 Cognee search: more context + better semantic relevance 5:22 Feedback mode: strengthen edges & improve retrieval over time 5:40 Better summaries: patterns in a customer’s support history 6:11 Wrap-up + community 🔗 Cognee links (start here) Website: https://www.cognee.ai/ Docs: https://docs.cognee.ai/ GitHub (open source): https://github.com/topoteretes/cognee Cognee Cloud: https://platform.cognee.ai/ 🌍 Follow / join the community Discord: https://discord.gg/NQPKmU5CCg Subreddit: https://www.reddit.com/r/AIMemory/ X (Twitter): https://x.com/cognee_ LinkedIn: https://linkedin.com/company/cognee-ai #Cognee #AIAgents #agentmemory #aimemory #RAG #GraphRAG #KnowledgeGraph #LLM #contextengineering #KnowledgeEngineering #vectorsearch #hyridsearch