# Graph Reasoning

# Graph Reasoning

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:

1. Run a graph query (or a set of queries) to produce:
   • a small, structured dataset (nodes, summaries, counts)
2. Provide that structured output to the LLM.
3. 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