Episodic Memory
Master how AI agents store and retrieve personal experiences, contextual memories, and temporal events
Your Progress
0 / 5 completedWhat You've Learned
You've mastered how AI agents use episodic memory to store and retrieve personal experiences, enabling coherent conversations, context management, and learning from interactions. Let's review the key concepts.
📋 Knowledge Checklist
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Episodic vs Semantic Memory
Understand that episodic memory stores personal experiences (what/when/who) while semantic memory stores facts
Temporal Context
Episodes must include timestamps for chronological organization and time-based retrieval
Multi-Dimensional Storage
Store episodes with rich context: temporal, participant, spatial, emotional, semantic, and outcome data
Storage Strategies
Know when to use chronological logs, indexed databases, vector embeddings, or graph structures
Temporal Retrieval
Retrieve episodes by time (recent, date range, relative) for conversation continuity
Contextual Retrieval
Filter episodes by attributes (user, sentiment, topic, outcome) for targeted recall
Semantic Retrieval
Use vector similarity to find conceptually related episodes even without exact matches
Hybrid Retrieval
Combine temporal, contextual, and semantic strategies for optimal memory recall
Context Window Management
Balance token budgets by selectively including relevant episodes, not entire history
Reference Resolution
Use episodic memory to resolve pronouns and implicit references in conversations
Conversation Continuity
Maintain coherent multi-turn dialogues by retrieving and applying past context
Practical Implementation
Start with simple chronological storage, add indexing and embeddings as needs grow
⚡ Quick Reference Guide
🗄️When to Use Episodic Memory
- • Multi-turn conversations requiring context continuity
- • Personalized interactions based on past behavior
- • Learning from user feedback and interaction patterns
- • Temporal reasoning (trends, sequences, changes over time)
🔧Implementation Checklist
- ✓ Timestamp all episodes with ISO 8601 format
- ✓ Store user/session identifiers for filtering
- ✓ Include contextual metadata (sentiment, topic, outcome)
- ✓ Implement at least one retrieval strategy (temporal recommended)
- ✓ Add context window management for token efficiency
⚠️Common Pitfalls to Avoid
- ✗ Including entire conversation history in context window
- ✗ Forgetting to filter by user/session (mixing unrelated episodes)
- ✗ Storing episodes without sufficient metadata for retrieval
- ✗ No retention policy (storage grows indefinitely)
- ✗ Ignoring privacy concerns (store sensitive data carefully)
🚀 Next Steps
Ready to continue? The next module explores Vector Databases—the infrastructure powering semantic retrieval in episodic and semantic memory systems.
You'll learn how to encode episodes as vectors, perform similarity searches, and build scalable memory architectures.
→Practice: Implement a simple conversation memory with temporal retrieval
→Explore: Try combining episodic and semantic memory in a hybrid system
→Experiment: Test different context window strategies for your use case
← Context ManagementPrev
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Vector Databases for Memory