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Episodic Memory

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

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How Episodes Are Stored

Unlike semantic memory's abstract facts, episodic memory stores rich, multi-dimensional records of events. Each episode is a complete snapshot with temporal, contextual, and relational information.

Think of it as a database row with metadata—not just what happened, but when, where, who, why, and with what outcome.

📦 Episode Data Structure

{
  "episode_id": "ep_20251115_143022_abc123",
  "event": {
    "type": "user_message",
    "content": "Can you help me debug this API error?",
    "action": "request_assistance"
  },
  "temporal": {
    "timestamp": "2025-11-15T14:30:22Z",
    "timezone": "America/Los_Angeles",
    "session_start": "2025-11-15T14:15:00Z",
    "relative": "15 minutes into session"
  },
  "participants": {
    "user_id": "user_12345",
    "user_name": "Alice Developer",
    "agent_id": "agent_debug_assistant",
    "conversation_id": "conv_xyz789"
  },
  "context": {
    "channel": "web_chat",
    "location": "dashboard",
    "prior_topic": "authentication_setup",
    "session_history_length": 8
  },
  "metadata": {
    "sentiment": "frustrated",
    "urgency": "high",
    "intent": "technical_support",
    "entities": ["API", "error", "debug"],
    "topics": ["troubleshooting", "api_integration"]
  },
  "outcome": {
    "resolved": true,
    "satisfaction": 4.5,
    "follow_up_needed": false,
    "resolution_time_seconds": 180
  }
}
Key Insight: Each field adds a dimension for retrieval—you can search by time, user, sentiment, topic, or any combination.

Interactive: Six Dimensions of Context

Click each context dimension to explore what information it captures.

🗄️ Common Storage Strategies

📋Chronological Logs

Simple time-ordered lists of events. Fast to append, easy to replay conversations.

✓Best for: Conversation history, session replay
✓Example: Chat message arrays, log files

🗂️Indexed Databases

Structured storage with indexes on time, user, topic. Fast retrieval by any dimension.

✓Best for: Multi-user systems, complex queries
✓Example: PostgreSQL, MongoDB with indexes

🧠Vector Embeddings

Episodes encoded as vectors for semantic similarity search. Find conceptually related past events.

✓Best for: "Similar to when..." queries
✓Example: Pinecone, Weaviate, Chroma

🕸️Graph Structures

Episodes as nodes, relationships as edges. Trace connections between events over time.

✓Best for: Causal chains, interaction patterns
✓Example: Neo4j, memory graphs

💡 Practical Implementation Tips

1.
Start Simple: Begin with chronological logs (array of messages) before adding complex indexing.
2.
Timestamp Everything: Use ISO 8601 format (2025-11-15T14:30:22Z) for consistent time handling.
3.
Add Metadata Incrementally: Start with basic context (user, session), add sentiment/intent later.
4.
Plan for Scale: Consider retention policies (how long to keep episodes) and archiving strategies.
5.
Balance Detail vs Storage: Not every field needs to be stored—focus on what enables better retrieval.
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