Build Your Own Chatbot With Memory (No Coding Required)

Want to build your own long-term memory chatbot without writing a full AI system from scratch? Github: https://github.com/patmakesapps/CortexLTM In this video, I walk you step-by-step through setting up CortexLTM — a schema-driven long-term memory layer for LLM apps and agents. By the end, you’ll have: A working chatbot Threaded conversation memory Rolling summaries Semantic search with pgvector Cross-chat “master memory” A modular, swappable memory backend No frontend build. No complex agent framework. Just clean infrastructure. What You’ll Learn: How to install VS Code & Python How to fork and clone the CortexLTM repo How to create a Python virtual environment How to install dependencies How to configure environment variables How to set up Supabase (Postgres + pgvector) How to run the SQL migrations How to generate API keys (Groq + OpenAI) How to run the CLI memory harness How CortexLTM handles summarization + semantic retrieval Architecture Overview: CortexLTM sits between your app and your infrastructure. It provides: Structured event logging Meaningful-turn detection Rolling episodic summaries Topic shift detection via embeddings Master memory extraction Semantic search using pgvector Providers are swappable. Storage is Postgres. Memory is structured — not just raw chat logs. GitHub Repository: ⭐ Star the repo and fork it here: https://github.com/patmakesapps/CortexLTM Tech Stack: Python 3.12 Supabase (Postgres) pgvector OpenAI Embeddings (text-embedding-3-small) Groq (Llama 3.1) dotenv psycopg2 Why CortexLTM? Most chatbots: Store raw logs Grow context infinitely Forget things across chats Can’t semantically search memory CortexLTM: Compresses context intelligently Preserves durable identity facts Enables cross-thread recall Keeps memory structured and scalable This is memory infrastructure for serious AI systems. If you found this helpful: 👍 Like 💬 Comment your build ideas ⭐ Star the repo 🔔 Subscribe for deeper AI infrastructure tutorials