thinkfleetai/memmesh/graph

graph

⚙️ Graph reasoning requires MemMesh hosted mode. Building the graph works locally: memory_extract_pending → memory_commit_extraction populate typed entities/edges. Multi-hop reasoning/traversal (memory_graph_reason, memory_query_graph, memory_prefetch_related) runs on the hosted engine — set your mm- API key. If those return "unknown tool" on a local install, say so and use search over the extracted entities instead.

MemMesh links memories into a knowledge graph whose edges are bi-temporal (each has valid_from / valid_to). That enables answers a flat store can't give.

Multi-hop reasoning

Answer questions that require chaining edges — "who acquired the company Sarah founded":

{ "name": "memory_graph_reason",
  "arguments": { "anchorEntityId": "<entity id>", "maxHops": 3, "maxPaths": 20 } }

Returns ranked paths (scored by edge weight × recency). The anchor is an entity id — resolve names to ids via a graph query first.

Point-in-time — what did we believe then?

{ "name": "memory_query_graph",
  "arguments": { "subjectId": "<entity id>", "asOf": "2026-01-01T00:00:00Z" } }

Omit asOf for the current view. This reconstructs the graph as it stood on any date — the bi-temporal record, not just the latest state.

Anticipatory retrieval (spreading activation)

Given the memories a session is working with, surface what's most likely needed next:

{ "name": "memory_prefetch_related", "arguments": { "seedMemoryIds": ["<id>","<id>"], "limit": 10 } }

Building the graph

Edges come from client-LLM extraction — the engine hands you a prompt, your own model extracts entities/edges, you commit them (zero engine-side LLM cost):

{ "name": "memory_extract_pending", "arguments": { "projectId": "<repo>", "limit": 10 } }
// run each prompt through your model, then:
{ "name": "memory_commit_extraction", "arguments": { "memoryId": "…", "contentHash": "…", "entities": [...], "edges": [...] } }

Run this loop until extract_pending returns empty to fully populate the graph for reasoning.