Notion MCP Server V2

# 🧩 Frameworks for Building Agentic Memory: Theory Before Code Now that we've explored how memory systems work conceptually and seen how to design our own chatbot agents, it's time to talk about **frameworks**. Frameworks allow an easier development, especially when it comes to handling: - Memory storage - Retrieval logic - Agent loops - Prompt construction As this space matures, choosing the right foundation becomes critical for long-term stability and scalability. --- ## 🥇 LeTTA: Ideal for Custom-Built Long-Term Memory Solutions LeTTA introduced many of the ideas that pushed the field of long-term agentic memory forward. They've published a framework that embodies their research and philosophy. However, I personally do not recommend using Leda as an out-of-the-box solution in production environments, and here's why: - ⚠️ It's relatively **new** and **immature** in terms of ecosystem and tooling - 🧩 Larger, well-supported platforms like LangChain already offer **equivalent capabilities** - 🧪 The **sample code** they provided in their public course **is already outdated and non-functional** - 🔧 Debugging and extending the framework may require significant manual effort GitHub repository: [Link](https://github.com/letta-ai/letta) --- ## 🔄 LangChain: Reliable Long-Term Memory Out-of-the-Box While developing this project, I noticed that **LangChain introduced built-in support for long-term memory** in its agent framework — and it's both robust and well-documented. LangChain currently supports **two distinct memory strategies**, and we'll explore both in this session. --- ## 🧠 Strategy 1: Hybrid Memory with Vector  This architecture uses a **hybrid memory system**: - 🟣 **VectorDB**: Stores embeddings of past messages for semantic recall - 🟠 **GraphDB**: Stores structured information about the user — like their interests, behavior, and network This approach gives agents the ability to: - Remember facts over time - Understand the user's profile - Adapt responses dynamically to context and history LangChain handles: - ✅ Retrieval - ✅ Prompt construction - ✅ Context formatting ...all under the hood. --- ## 💻 Let's See It in Action Now let's walk through the code together. We'll cover: - How to initialize memory stores - How to wire them into an agent - And how the agent uses them to build intelligent, context-aware prompts in real time Let's dive in!