Use pre-built Jupyter notebooks to create conversational AI.
Develop AI chatbots with knowledge graphs, episodic memory, and semantic memory, for applications like customer support or recommendation systems.
This skill helps you build more advanced and personalized AI agents by providing a collection of pre-built and tested notebooks that you can customize and experiment with.
"A founder wants to create a conversational AI assistant that can help customers book travel itineraries. She uses the Agent Memory Techniques skill to set up a Jupyter notebook that incorporates a knowledge graph and episodic memory. After 1-2 hours of setup, she's able to ask Claude to retrieve customer travel history and suggest personalized booking options."
Beginners should pick this up when they want to explore the basics of conversational AI and start building simple chatbots.
Senior engineers and professionals will appreciate this skill when they need to build advanced AI agents with more complex conversational flows and knowledge management.
Don't confuse this skill with traditional AI models โ these notebooks are designed to be flexible and adaptable, allowing you to create custom AI solutions.
Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.
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