CKG — NVIDIA — AI
NVIDIA AI
Developer Docs.
Deterministic traversal context for your agent.
20 NVIDIA AI Developer Documentation Domains. 998 nodes. Your agent traverses declared relationships across the full NVIDIA AI stack — not text it infers from.
20 domains
998 nodes
read-only
How it's built
Every edge is a decision.
REQUIRES
Hard prerequisite — cannot function without it. Drives agent sequencing.
ENABLES
Unlocks a capability. Not strictly required — optimization paths.
RELATES_TO
Conceptual proximity. Not a dependency. Use for disambiguation.
IMPLEMENTS
Concrete instantiation of an abstraction. Maps architecture.
Confidence defers to null — not wrong, just unreviewed. This is the scaffold your agent runs before you add your domain layer on top.
KRB Benchmark · v0.6.2 · open & reproducible
The eval for structured knowledge retrieval.
| # |
System |
Macro F1 |
Tokens/q |
5-Hop F1 |
| 1 |
CKG (ckg-mcp v0.7.6) |
|
252 |
0.786 |
| 2 |
RAG (text-embedding-3-small) |
|
2,982 |
0.170 |
| 3 |
GraphRAG (MS global mode) |
|
3,450 |
— |
CKG F1 improves with hop depth — 0.37 → 0.77 from hop 0 to hop 5. RAG stays flat at ~0.13 regardless of depth. Retrieval has no mechanism for traversing a chain.
↗ danyarm/krb-leaderboard — open & reproducible
Token efficiency
11× fewer tokens.
Context you save, you keep.
269
CKG · tokens/query
vs
2,982
RAG · tokens/query
A grounding pass that costs 269 tokens leaves your context window open for reasoning. Every token saved is a token you can spend on what actually matters to your agent.
Context as a Service
Your domain, compressed.
This is a rapid-start scaffold — not a be-all-end-all. Layer your domain on ours, or let us build a CKG tuned to your exact knowledge gaps.
Layer 1
Context optimization
Free. 97 domains. Install and go.
Layer 2
Agent grounding
Custom domain CKG built for your stack.
Layer 3
Sealed appliance
Bundled CKG + query server for your team.
Start a conversation → graphifymd.com
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