Transform your assistant from stateless to genuinely intelligent. You'll implement the three types of memory that make AI assistants feel personal: semantic recall, working memory, and episodic learning from past conversations.
What You'll Build
A personal assistant with a complete memory architecture that can:
- Recall relevant facts about the user during conversations
- Handle infinitely long conversations without overwhelming context
- Learn from past interactions to improve future responses
- Automatically extract and update memories from conversations
What You'll Learn
Semantic Recall on Memories
Make your search functions generic so they work with any data type. You'll build a memory search system that finds relevant user facts based on the current conversation, then inject those memories into the agent's context for personalized responses.
Memory Extraction
Automatically capture permanent information from conversations. You'll create an extraction system that adds new memories, updates existing ones, and removes outdated facts - all without user intervention.
Working Memory for Long Conversations
Solve the context window problem with a sliding window approach. You'll send only recent messages to the LLM while using semantic search to retrieve relevant older messages, enabling conversations that can continue indefinitely.
Episodic Memory from Conversations
Give your assistant the ability to learn from experience. You'll generate LLM summaries of each conversation capturing what worked well and what to avoid, then retrieve relevant past episodes when starting new conversations.