Semantic Memory in AI Agents - Facts & Knowledge

What is Semantic Memory?

Semantic memory is a type of long-term memory that stores general knowledge, facts, and concepts independent of specific experiences. Unlike episodic memory which records events, semantic memory contains information abstracted from particular instances.

Examples in AI Agents

Semantic memory in AI agents might include:

User preferences ("prefers Python over JavaScript")

Learned facts ("user works at Acme Corp")

Domain knowledge ("the API rate limit is 100 requests/minute")

Relationship information ("Alice is Bob's manager")

Semantic vs Episodic Memory

| Aspect | Semantic | Episodic |

|--------|----------|----------|

| Content | Facts & concepts | Events & experiences |

| Time | Context-independent | Time-stamped |

| Example | "User prefers dark mode" | "User asked about dark mode on Jan 15" |

Building Semantic Memory

Agents build semantic memory through:

Extracting facts from conversations

Consolidating repeated patterns

User profile updates

Knowledge base integration

Entity extraction and linking

Storage Approaches

Common storage patterns for semantic memory:

**Key-Value Stores**: Simple fact storage

**Knowledge Graphs**: Relationship-rich representations

**Vector Databases**: Semantic similarity search

**Structured Databases**: Queryable fact tables

Retrieval Methods

Semantic memories can be retrieved via:

Direct key lookup

Semantic similarity search

Graph traversal

SQL-like queries

Natural language queries

Memory Updates

Unlike episodic memory which is append-only, semantic memory requires:

Conflict resolution for contradicting facts

Confidence scoring for uncertain information

Temporal validity tracking

Source attribution