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Level 4

Memory

Master agent memory systems. Learn short-term, long-term, episodic, and semantic memory for AI agents using vector databases and RAG.

📚10 Modules

⏱️5-6 hours

📊Level 4

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Prerequisites

Complete Level 3: Planning

🎯What You'll Learn

✓Short-term vs long-term memory architectures
✓Vector databases for semantic memory
✓Episodic memory and experience replay
✓Retrieval-Augmented Generation (RAG)
✓Context window management

💪Skills You'll Gain

Memory architecturesVector databasesRAGRetrieval strategiesContext management

🏆Learning Outcomes

1Design agent memory systems

2Implement semantic search

3Build RAG systems

4Optimize memory retrieval

📖Interactive Modules (10)

Module 1

Types of Agent Memory

Understand different types of agent memory: short-term, long-term, semantic, episodic.

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Module 2

Short-Term Memory

Learn short-term memory for maintaining context within a single agent session.

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Module 3

Long-Term Memory Storage

Implement persistent long-term memory for agents to remember across sessions.

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Module 4

Semantic Memory

Master semantic memory, storing factual knowledge and concepts for agent reasoning.

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Module 5

Episodic Memory

Learn episodic memory, recording specific events and experiences for context.

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Module 6

Vector Databases for Memory

Use vector databases like Pinecone, Weaviate for efficient agent memory storage and retrieval.

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Module 7

Memory Retrieval Strategies

Implement memory retrieval strategies: semantic search, temporal filtering, relevance ranking.

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Module 8

Memory Consolidation

Learn memory consolidation, compressing and organizing agent experiences over time.

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Module 9

Managing Context Windows

Master context window management, prioritizing important information within LLM token limits.

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Module 10

Interactive Memory Demo

Build an agent with memory capabilities in an interactive hands-on demo.