Graph Reasoning (concepts)
This page is the conceptual overview of graph reasoning. For runnable patterns, queries, and analytic recipes, see Graph Reasoning and Analytics.
Graph reasoning refers to using the structure of your knowledge graph to answer questions and drive workflows. It can be done with:
- graph queries (deterministic traversal, filtering, and aggregation)
- LLMs that are grounded in graph-derived context (explain and synthesize)
The best practice is: use the graph for computation, use the LLM for communication.
What graph reasoning looks like in practice
- find connected entities (neighbors, “related to”)
- compute aggregates (counts per status/manufacturer/team)
- build constrained candidate sets (“tickets for this customer”)
- detect patterns (shared components, recurring failure modes)
Combining graph + LLM safely
Recommended flow:
- Run a graph query (or a set of queries) to produce:
- a small, structured dataset (nodes, summaries, counts)
- Provide that structured output to the LLM.
- Ask the LLM to:
- explain findings
- propose next actions
- summarize evidence
Common pitfalls
- Asking the LLM to infer relationships that the graph can provide exactly.
- No grounding: graph reasoning should cite nodes/edges used.
- Oversized context: retrieve only the relevant neighborhood.
Next steps
- Learn the deterministic side: Reference → Graph Query Language
- Build tool-based flows: Custom Endpoints