Episodic Memory
Master how AI agents store and retrieve personal experiences, contextual memories, and temporal events
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0 / 5 completedMaintaining Conversational Context
Episodic memory shines in multi-turn conversations. Each exchange builds on previous messages, and agents must maintain coherent context across turns to provide relevant responses.
Context management involves tracking conversation state, resolving references ("it", "that", "the issue"), and carrying forward key information (user goals, preferences, prior decisions).
⚠️ The Context Window Challenge
Problem: Limited Token Budgets
LLMs have finite context windows (e.g., 8K, 32K, 128K tokens). You can't include the entire conversation history every time—it's too expensive and slow.
Solution: Episodic Memory as Context Manager
🛠️ Context Management Strategies
Fixed Window (Recency-Based)
Include last N messages (e.g., 10 most recent turns)
Relevance-Based Selection
Retrieve messages semantically similar to current query
Summarization + Details
Summarize older messages, include recent ones verbatim
Key Facts Extraction
Extract and maintain key entities, preferences, decisions
Interactive: Conversation Context Tracker
Observe how context builds across conversation turns. Hover over messages to see how context accumulates.
🔗 Reference Resolution with Episodic Memory
Users often use pronouns ("it", "this", "that") and implicit references. Episodic memory enables resolution by looking back.