V · MemoryEmerging★

World-Model Graph Memory

also known as World-Model Graph, Planning-Substrate Knowledge Graph

Memory store structured as a typed entity-relation graph used as the agent's authoritative world model for planning — not only for retrieval.

This pattern helps complete certain larger patterns —

  • specialisesKnowledge Graph Memory★— Persist agent memory as entities and relations in a structured graph so symbolic queries (path, neighbour, type) become possible.

Context

A team uses knowledge graphs in agent memory (knowledge-graph-memory, graphrag) primarily for retrieval — query the graph to find relevant facts. The world-model-graph-memory pattern uses the same structure as the planning substrate: the agent reasons over the graph as its model of the world, not just as a retrieval index.

Problem

Knowledge-graph-memory used as retrieval surface alone misses the planning value of the structure. Plans that span entities and relations cannot be expressed if the graph is only queried by similarity. Differs from knowledge-graph-memory by being the agent's *planning substrate*, not just a retrieval index.

Forces

  • Building a graph that supports both retrieval and planning requires richer schema.
  • Planning over a graph is slower than planning over flat text.
  • Graph drift — entities and relations get stale.

Example

A workplace-coordination agent's graph models: people (with roles, locations, availability), meetings (with participants, times, locations), policies (with constraints). Planning 'schedule a meeting with team X next week' is a graph query: find time slots where all team members are free in matching locations under policy constraints. Direct LLM planning would have hallucinated availability.

Diagram

Solution

Therefore:

Graph schema includes typed entities, typed relations, and entity properties suitable for planning queries (preconditions, effects, capabilities). Agent plans by querying the graph: 'what's the path from current state to goal state?' is a graph traversal, not an LLM hallucination. Pair with knowledge-graph-memory, graphrag, mental-model-in-the-loop-simulator, semantic-memory, episodic-memory.

What this pattern forbids. The graph is the planning substrate — plans must be expressible as graph operations; LLM is not used to bypass the graph for planning.

And the patterns that stand alongside it, or against it —

  • complementsGraphRAG★— Build an LLM-extracted entity-and-relation knowledge graph plus hierarchical community summaries, then answer global queries via map-reduce over those summaries.
  • complementsMental-Model-In-The-Loop Simulator·— Run candidate multi-step strategies inside an internal simulator of the environment before committing in the real world — broader than simulate-before-actuate (single action) by simulating multi-step strategies.
  • complementsSemantic Memory★— Maintain a dedicated store of what the agent holds to be true about the user and the world, separate from event records (episodic) and learned how-to (procedural).
  • complementsWorld Model as Tool·— Let a planning agent invoke a generative world model as a tool to roll out hypothetical futures before committing to an action, treating the world model as a callable simulator rather than a training target.

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References

Provenance