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__swarmdo__agentdb_hierarchical-recall to look up the target entity by name
2. Expand -- call mcp__swarmdo__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 * keyword_similarity(query, node)) using mcp__swarmdo__agentdb_pattern-search (the semanticRouter controller is enabled: false in current AgentDB builds; pattern-search is the available substitute and works fine for entity-name + relation-type keyword matches — see upstream/swarmdo#2049). For higher-fidelity semantic similarity, callers can fall back to mcp__swarmdo__embeddings_generate + manual cosine, but that's not required for step 3 to function.
4. Prune -- remove paths with cumulative score below 0.3
5. Rank -- sort remaining paths by cumulative score descending
6. Synthesize -- call mcp__swarmdo__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 @swarmdo/cli@latest memory search --query "relations for ENTITY_NAME" --namespace knowledge-graph