The Knowledge Graph Query Tool
Query a governed knowledge graph of entities and relationships so an agent can answer multi-hop questions — how things connect — that flat semantic search alone can’t.
Your answer exists — retrieval just can’t find it
Private knowledge is only useful if an agent can retrieve exactly the right piece and ground its answer in it. Keyword search misses, hosted RAG can’t touch sensitive data, and ungrounded models make things up.
Keyword search misses
The right content is phrased differently than the query.
Ungrounded answers
Without retrieval, models invent instead of cite.
Scale hides signal
The best chunk is buried among thousands of near-matches.
Hosted RAG is off-limits
Sensitive knowledge can’t go to a third-party index.
Knowledge Graph Query, without the risk
Capability
What it does
Answer questions by traversing your knowledge graph.
it queries a knowledge graph of entities and their relationships and returns connected results.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
it traverses typed relationships across the graph, so an agent can answer multi-hop, connection-based questions rather than only matching isolated passages.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Indexing and retrieval run on-premise or in your sovereign cloud, scoped per tenant and audit-logged, so even sensitive knowledge is searchable and citable without any of it leaving your perimeter.
Per-tenant, logged
Parameters
The knowledge_graph_query tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 3 Optional Maximum relationship hops to traverse.
How the Knowledge Graph Query tool works in practice
Knowledge Graph Query is a semantic search & rag tool you assign to a VDF AI agent. It queries a knowledge graph of entities and their relationships and returns connected results. Its hallmarks — Graph, Entities, Multi-hop — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, it traverses typed relationships across the graph, so an agent can answer multi-hop, connection-based questions rather than only matching isolated passages. It expects query and user_id as required inputs, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Knowledge Graph Query when they need to handle connected answers, impact mapping, and investigation. It rarely works alone — pair it with Hybrid Search, RAG Vector Query, and ALL Vectors Search to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Knowledge Graph Query pays back
Connected answers
Answer "which vendors touch this system?" across links.
Impact mapping
Trace dependencies between entities.
Investigation
Follow relationships during due diligence.
Grounding
Give an agent structured, relational context.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the Knowledge Graph Query tool
What is the Knowledge Graph Query tool?
It queries a knowledge graph of entities and their relationships and returns connected results. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
How is this different from vector search?
Vector search finds similar text; a knowledge graph answers how entities connect, enabling multi-hop reasoning.
Can it combine with RAG?
Yes. Graph queries and vector retrieval are often used together for both connections and content.
What inputs does the Knowledge Graph Query tool need?
It requires query and user_id, and optionally accepts max_hops. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Knowledge Graph Query?
Knowledge Graph Query is commonly assigned alongside Hybrid Search, RAG Vector Query, and ALL Vectors Search. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign Knowledge Graph Query to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put Knowledge Graph Query to work
See the Knowledge Graph Query tool assigned to an agent and orchestrated in a governed, on-premise network.