Semantic Memory

Semantic memory is meaning-based memory that stores facts, relationships, and concepts in a structured way, enabling agents to understand connections, reason about entities, and synthesize insights. Unlike raw text or embeddings, semantic memory captures explicit relationships—"Alice works at Acme," "Project Alpha depends on Project Beta"—that support logical reasoning and graph traversal.

Semantic memory answers questions like "Who works where?", "What projects are related?", or "Which tasks depend on this one?" It represents knowledge as entities (people, places, things) and relationships (works at, mentions, depends on), forming a queryable network that agents use for reasoning, planning, and synthesis.

The outcome is agents that understand meaning and connections, not just keywords or similarity—enabling intelligent responses grounded in structured knowledge.

Why it matters

• Enables relationship reasoning: Agents can answer "Who owns tasks blocking Project X?" by traversing relationships—not just keyword matching.
• Supports synthesis and summarization: Semantic memory allows agents to connect facts across documents and generate coherent summaries or insights.
• Reduces hallucinations: Structured facts ground agent responses in verified knowledge, preventing plausible-but-wrong answers.
• Powers personalization: Understanding "Alice prefers async communication" or "Bob is expert in authentication" enables context-aware interactions.
• Improves search precision: Semantic queries like "tasks owned by Alice" are more precise than keyword or embedding similarity alone.
• Facilitates multi-hop reasoning: Agents can follow chains of relationships—"Alice works at Acme, Acme uses Graphlit, therefore Alice might know about Graphlit."

How it works

Semantic memory operates through extraction, linking, and graph storage:

• Ingestion → Content (documents, messages, events) flows into the system.
• Knowledge Graph → Entity extraction identifies people, companies, projects, tasks, and concepts. Relationship extraction captures connections: "owns," "mentions," "depends on," "located in."
• Entity Linking → Multiple references to the same entity ("Alice Johnson," "Alice J.," "ajohnson@company.com") are resolved to a single canonical identity.
• Semantic Storage → Entities and relationships are stored in a graph structure optimized for traversal and queries.
• Retrieval/Assembly → Agents query semantic memory using relationship patterns: "Find all tasks owned by people who work at Acme" or "Show me projects related to authentication."

This structure enables reasoning and synthesis that embedding-based similarity search cannot achieve.

Comparison & confusion to avoid

Examples & uses

Team knowledge graph
Semantic memory stores: "Alice works at Acme," "Alice owns Task 123," "Task 123 blocks Project Alpha," "Bob is expert in authentication." An agent can answer "Who at Acme owns tasks blocking Alpha?" by traversing relationships: Acme → Alice → Task 123 → blocks → Alpha.

Customer relationship memory
Semantic memory captures: "CustomerCo uses ProductX," "CustomerCo reported IssueY," "IssueY relates to FeatureZ," "FeatureZ is owned by EngineerA." When CustomerCo asks about status, the agent queries relationships to provide context-aware updates.

Research synthesis agent
Semantic memory links: "PaperA cites PaperB," "AuthorX wrote PaperA," "ConceptY is discussed in PaperB." An agent can synthesize "Who are the key authors in ConceptY?" by traversing citation and authorship relationships.

Best practices

• Extract relationships explicitly: Don't rely on embeddings to capture "Alice works at Acme"—use structured extraction and store it as a relationship.
• Canonicalize entities: Ensure "Alice Johnson," "Alice J.," and "ajohnson@acme.com" resolve to one identity—fragmented entities break reasoning.
• Model bidirectional relationships: If "Alice works at Acme," also store "Acme employs Alice"—enables queries from either direction.
• Use typed relationships: "owns," "blocks," "mentions" have different meanings—don't collapse them into generic "related to."
• Combine with temporal memory: Semantic facts have timestamps—"Alice worked at Acme from 2020-2023"—enables time-aware reasoning.
• Support relationship confidence scores: Extracted relationships may have uncertainty—track confidence and surface it in queries.

Common pitfalls

• Confusing embeddings with semantic structure: Embeddings capture similarity, not explicit relationships—you need both for robust memory.
• No entity linking: If every mention creates a new entity, memory fragments—"Project Alpha" and "Alpha Project" should be one entity.
• Over-relying on extraction accuracy: Extraction has errors—include human-in-the-loop review and correction mechanisms.
• Ignoring relationship types: Treating all relationships as generic "related to" loses meaning—typed relationships enable precise queries.
• Static semantic memory: Facts change—Alice moves to a new company, tasks get reassigned—semantic memory must support updates.

See also

• Agent Memory — Core concept of persistent agent context
• Temporal Memory — Time-aware memory that complements semantic facts
• Knowledge Graph — Graph structure for storing semantic memory
• Entity Linking — Resolving multiple references to canonical identities
• Fact Extraction — Extracting structured facts from unstructured content

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See how Graphlit implements Semantic Memory with knowledge graphs → Agent Memory Platform