kg-traverse

KG Traverse

Perform pathfinder graph traversal starting from a seed entity. Expands outward through causal edges, scores paths by relevance, and prunes low-similarity branches.

When to use

When you need to explore the knowledge graph starting from a specific entity -- finding what depends on it, what it depends on, or discovering indirect relationships. Useful for impact analysis, dependency chains, and understanding code structure.

Steps

1. Seed -- call mcp__claude-flow__agentdb_hierarchical-recall to look up the target entity by name
2. Expand -- call mcp__claude-flow__agentdb_causal-edge to find all edges connected to the seed entity, then recursively expand outward to the specified depth (default: 3)
3. Score -- for each path, compute relevance: cumulative_score = product(edge_weight * semantic_similarity(query, node)) using mcp__claude-flow__agentdb_semantic-route for similarity
4. Prune -- remove paths with cumulative score below 0.3
5. Rank -- sort remaining paths by cumulative score descending
6. Synthesize -- call mcp__claude-flow__agentdb_context-synthesize to combine the top paths into a coherent summary
7. Report -- display the top 10 paths with: path (entity chain), relation types, cumulative score, and synthesized context

CLI alternative

npx @claude-flow/cli@latest memory search --query "relations for ENTITY_NAME" --namespace knowledge-graph