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Episodic Memory

Master how AI agents store and retrieve personal experiences, contextual memories, and temporal events

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Finding the Right Memories

Storage is only half the battle. Effective retrieval determines whether your agent can actually use its episodic memories to improve interactions.

Agents need multiple retrieval strategies depending on the query: "What did the user say yesterday?" (temporal), "Show me frustrated interactions" (contextual), or "Find events similar to this" (semantic).

🔍 Three Core Retrieval Strategies

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1. Temporal Retrieval (Time-Based)

Retrieve episodes based on when they occurred. Most common for conversation continuity.

Example Queries:
• "Last 10 messages in this conversation"
• "All interactions from yesterday"
• "Events between 2PM - 4PM today"
• "User's first interaction (oldest episode)"
FastSimple indexingChronological order
🎯

2. Contextual Retrieval (Attribute-Based)

Filter episodes by specific context attributes like user, sentiment, topic, or outcome.

Example Queries:
• "All frustrated interactions with User123"
• "Unresolved support tickets"
• "Pricing questions from web chat channel"
• "High urgency events this week"
Precise filteringMulti-conditionStructured data
🧠

3. Semantic Retrieval (Similarity-Based)

Find episodes conceptually similar to a query using vector embeddings.

Example Queries:
• "Similar to when user asked about integrations"
• "Past events related to billing problems"
• "Conversations about authentication"
• "Analogous troubleshooting situations"
Fuzzy matchingConcept-basedEmbeddings required

Interactive: Memory Retrieval Simulator

Select a retrieval strategy to see which episodes get returned from our sample database.

Retrieved Episodes (2 results)

Sorted by: Time (newest first)
2 hours ago
User asked about API rate limits
Technical inquiry, web chat
curious#api_limits
Yesterday
User requested pricing details
Sales inquiry, email
interested#pricing

🚀 Advanced Retrieval Techniques

🔗Hybrid Retrieval

Combine multiple strategies: "Recent + similar + frustrated"

Start with temporal window, apply contextual filters, then rank by semantic similarity.

📊Recency Weighting

Prioritize recent memories over older ones

Apply exponential decay to older episodes—yesterday's events matter more than last month's.

🎭Cue-Dependent Retrieval

Use current context as retrieval cue

"User is frustrated now → retrieve past frustrated interactions" for pattern matching.

⚡Lazy vs Eager Loading

Load only what's needed, when it's needed

Retrieve summaries first, full episodes on demand to optimize for speed.

💼 Real-World: Multi-Turn Support Conversation

User Returns After 1 Hour:
User: "Hi again, any update on my issue?"
Agent retrieval logic:
1️⃣ Temporal: Get last 5 messages from this user (conversation continuity)
2️⃣ Contextual: Filter for unresolved issues from this session
3️⃣ Semantic: Find similar past issues for solution patterns
Agent: "Yes! I see from our chat an hour ago you were experiencing login timeouts. The engineering team identified a session token issue. It's been fixed, and similar users reported success. Can you try logging in again?"
✅ Result: Agent remembered context, provided update, suggested action
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