CKG — MCP — graphifymd.com
Context Optimization
for AI Agents.
Your agents retrieve. They should traverse.
97 domain knowledge graphs across mathematics, GPU inference, healthcare, law, robotics, and more. Your agent traverses declared relationships — not text it infers from.
97 domains 68 free 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)
0.488
252 0.786
2 RAG (text-embedding-3-small)
0.123
2,982 0.170
3 GraphRAG (MS global mode)
0.120
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 Optimization  ·  graphifymd.com
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
1 / 5
Agent Team Orchestration  ·  Liu et al. arXiv:2606.30986
Context degrades.
Traversal doesn't.
In multi-agent pipelines, context efficiency collapses 91% across stages with no model change (Liu et al.). Every agent boundary is a lossy handoff. CKG attacks all three root causes.
TOKEN BURDEN
11×
fewer tokens per query · 269 vs 2,982
HANDOFF COST
0
re-retrievals at agent boundaries
COMPRESSION LOSS
Once
graph compressed offline — never again
Structured context doesn't consume your context window. It opens it.