Smarter Memory with Semantic Search in LangGraph

Make your LangGraph agents smarter using the new semantic search in the BaseStore, LangGraph's "long-term memory" primitive! Learn how to build chatbots that remember user preferences across thousands of conversations using semantic similarity matching. This enhancement to our memory agent template enables contextually-aware information retrieval for more personalized interactions. ⏰ *Timestamps* ----------- 00:00 Introduction & Benefits of Semantic Memory 00:20 Core Components: Store & Embeddings 01:05 Quick Implementation Demo 01:31 Integration with Create React Agent 01:48 Implementation Requirements 01:55 Building the Application - Template Setup - Configuration Steps 02:31 Configuration - Store Configuration - Embedding Setup 03:26 Using in code 04:54 Advanced Features - User Segregation - Memory Updates - Index Controls 06:09 Documentation & Next Steps 🔗 *Resources* ----------- 📣 Blog: https://blog.langchain.dev/semantic-search-for-langgraph-memory/ 📚 Documentation: https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/ 💻 Template: https://github.com/langchain-ai/memory-template 📖 BaseStore Reference: https://python.langchain.com/docs/modules/memory/types/base_store 🎥 Original memory agent video: https://www.youtube.com/watch?v=-xkduCeudgY #LangGraph #SemanticSearch #VectorDB #LLM #AIEngineering