Build a Chatbot with Memory&Summarization using LangGraph|AI Powered Conversational Memory Explained

Description

Welcome to Episode 11 of our LangGraph series! In this episode, we take conversational AI to the next level — building a Chatbot with intelligent memory and summarization. You’ll learn how to: Use LLMs to generate running summaries of conversations instead of trimming old messages. Implement persistent memory using LangGraph’s MemorySaver and checkpoints. Manage threads of conversation just like Slack channels using configurable thread IDs. Reduce token costs and latency while maintaining context-rich responses. By the end, you’ll understand how to create scalable chatbots that remember context, summarize past chats, and deliver human-like continuity in dialogue. This is a must-watch for developers working with LangChain, LangGraph, or any LLM-based chatbot frameworks! 📘 Topics Covered: Customizing graph state schema and reducers Adding summarization logic with LLMs Building persistent chatbot memory Handling multi-turn and long-running conversations Thread-based conversation management 💡 Tech Used: Python, LangGraph, LangChain, Google Gemini API, LLMs 🔖 250+ SEO Tags: LangGraph tutorial, LangChain AI, Chatbot summarization, LLM memory, LangGraph chatbot, persistent chatbot memory, AI chatbot with memory, chatbot tutorial, AI conversation summarization, running summary chatbot, Python chatbot with memory, AI summarization, LangGraph series, LLM applications, LangGraph persistence, checkpointing in LangGraph, MemorySaver LangGraph, state management LangGraph, AI context retention, conversation compression, summarize conversation with LLM, building chatbots with LLM, LangChain graph, conversational memory, AI developer tutorial, LangGraph tutorial series, building AI assistant, Gemini API chatbot, open source LLM, RAG chatbot, AI memory tutorial, summarization model, LangGraph episode 11, AI project tutorial, chat summarizer, token optimization chatbot, Python AI tutorial, conversational state, chat summarization bot, AI in Python, generative AI tutorial, context aware chatbot, multi-turn chatbot, long context LLM, AI dev tutorial, LangGraph persistence example, AI coding tutorial, chatbot development, memory optimization chatbot, conversational AI tutorial, summarization chain, AI summarizer chatbot, building AI tools, AI workflow, LLM integration, LangChain examples, AI context retention, LangGraph learning, LangGraph persistence how-to, Python LLM integration, Google Gemini examples, AI pipeline design, AI memory model, chat history summarization, text summarization AI, generative AI coding, chatbot flow, Python AI projects, summarize chat history, AI memory saver, conversation persistence, chatbot checkpointing, summarize conversation example, LangGraph code example, LLM graph, LangGraph schema, LangGraph reducer, summarization pipeline, LLM summarizer, AI system design, LangGraph episode, AI tools tutorial, AI automation, build chatbot in Python, AI development series, chatbot with Gemini, persistent AI agent, LangGraph persistence tutorial, graph-based AI, conversation state machine, AI workflow orchestration, summarization workflow, LangGraph walkthrough, conversational AI builder, AI developer content, memory optimization LLM, token-efficient AI, summarization LangChain, LangGraph functions, checkpoint persistence, memory chatbots, AI conversation memory, LangGraph chat flow, AI tech demo, generative AI engineer, Python LangGraph implementation, LangGraph persistence checkpoint, summarize messages LLM, AI summarization workflow, chatbot architecture, chatbot with context, conversational AI memory, AI summarizer bot tutorial, LLM-based chatbot, LangGraph persistence memory, state summarization AI, Python summarization bot, LangGraph memory saver, how to build chatbot with memory, persistent memory chatbot LangGraph, AI pipeline with LangGraph, LangChain AI memory, LLM summarization model, generative AI pipeline, memory management in chatbots, build chatbot in LangGraph, summarizer chatbot example