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\n\n# Context Optimization for AI Agents\n\n### Agent traversal · Agent team orchestration · 97 domains · MCP-native\n\n**Your agents retrieve. They should traverse.**\n\n[![PyPI version](https://img.shields.io/pypi/v/ckg-mcp?color=0f6e56&label=PyPI)](https://pypi.org/project/ckg-mcp/)\n[![Downloads](https://img.shields.io/pypi/dm/ckg-mcp?color=0f6e56&label=installs%2Fmo)](https://pypi.org/project/ckg-mcp/)\n[![Python](https://img.shields.io/pypi/pyversions/ckg-mcp?color=0f6e56)](https://pypi.org/project/ckg-mcp/)\n[![License: MIT](https://img.shields.io/badge/license-MIT-0f6e56)](LICENSE)\n[![Domains](https://img.shields.io/badge/domains-97-0f6e56)](https://graphifymd.com)\n[![Free](https://img.shields.io/badge/free-68_domains-0f6e56)](https://pypi.org/project/ckg-mcp/)\n[![F1: 0.471 · 4× RAG](https://img.shields.io/badge/F1-0.471_%C2%B74%C3%97_RAG-0f6e56)](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)\n[![KRB v0.6.2](https://img.shields.io/badge/benchmark-KRB_v0.6.2-13201c)](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)\n[![Built by Graphify.md](https://img.shields.io/badge/built_by-Graphify.md-0f6e56)](https://graphifymd.com)\n\n> **Read-only.** The server can only return edges that exist in the data.\n> It returns nothing rather than inferring a path that isn't there.\n\n[**Get Pro →**](https://graphifymd.com/pro) · [**Benchmark →**](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf) · [**graphifymd.com →**](https://graphifymd.com)\n\n

\n\n

\n \n $\"ckg-mcp$ \n \n

\n\n---\n\n## Context Optimization — The Problem\n\nEvery agent that reasons about a domain — HIPAA, GPU inference, calculus, contract law — does one of three things:\n\n| Approach | What breaks |\n|---|---|\n| Long system prompt | No structure. Drifts with every model update. Cannot traverse. |\n| RAG retrieval | Probabilistic. Accuracy degrades at each hop. Expensive per query. |\n| Fine-tuning | 6-month cycle. Stale by delivery. Retrains when knowledge shifts. |\n\nAll three share the same failure: the agent **re-infers** domain structure on every query instead of reading structure that was declared once.\n\nIn our open benchmark (KRB v0.6.2 — [reproduce it yourself](https://github.com/Yarmoluk/ckg-benchmark)):\nRAG achieves **0.123 macro F1** on multi-hop domain queries. CKG achieves **0.471**. At 5 hops, the gap widens: RAG 0.170, CKG 0.772.\n\nThe token cost compounds the accuracy problem: the average RAG query costs **2,982 tokens**. The average CKG traversal costs **269** — measured across 19 benchmark domains.\n\nThese numbers are ours, on our benchmark. The dataset is [public on HuggingFace](https://huggingface.co/datasets/danyarm/ckg-benchmark). Run it yourself.\n\n---\n\n## Agent Traversal — The Solution\n\nA **Compressed Knowledge Graph (CKG)** is a domain structured for traversal, not retrieval.\n\nNot a document. Not a vector index. A pre-compiled DAG of concepts, typed dependency relationships, and prerequisite chains — compressed to the minimum tokens that carry the maximum structure. Served over MCP. Traversed deterministically.\n\n```\nAgent asks: \"What does TensorRT-LLM require to run on Hopper?\"\n\nCKG returns: TensorRT-LLM\n ├─ [REQUIRES] CUDA Toolkit\n │ ├─ [ENABLES] cuBLAS\n │ └─ [ENABLES] CUDA Driver API\n ├─ [REQUIRES] FP8/FP4 Quantization\n │ └─ [REQUIRES] Hopper SM90 Architecture\n └─ [ENABLES] Triton Inference Server\n └─ [ENABLES] NIM Microservice Runtime\n\n 269 tokens · declared edges only · no inference at query time\n\nRAG would: ~2,982 tokens · probabilistic retrieval · degrades at 3+ hops\n```\n\n**You go from prompting the domain into existence to asking questions inside it.**\n\n---\n\n## One Use Case — Becoming Nemotron-Enabled\n\nPerplexity is a model aggregator. Shortest route to Nemotron — click the dropdown, select the model, query. But it's their pipeline, their infrastructure. Your queries go through their system.\n\nIf you want to run Nemotron yourself — sovereign, private, on your own hardware — you need to navigate NVIDIA's stack: NGC API key, NIM container, Enterprise License, inference endpoint. The dependency chain is non-obvious. Most developers hit the wrong door first.\n\n**Without CKG:** search the docs, hit the Enterprise License wall, spend two hours finding `build.nvidia.com`.\n\n**With CKG:**\n```\nquery_ckg(\"Nemotron Model\", \"nvidia-nim\")\n→ [REQUIRES] Model Weights\n → [REQUIRES] NGC Container Registry\n → [REQUIRES] NGC API Key ← start here\n\nquery_ckg(\"NIM Docker Container\", \"nvidia-nim\")\n→ [REQUIRES] NGC API Key\n→ [REQUIRES] NVIDIA AI Enterprise License ← production path\n→ [ENABLES] NIM Microservice ← what you're building toward\n```\n\nTwo traversals. Correct path. No wrong doors. The graph knew — the model just told you.\n\n### Typed edges carry semantic meaning\n\n| Edge type | Meaning | Agent use |\n|---|---|---|\n| `REQUIRES` | Hard prerequisite — must exist first | Sequencing, gap detection |\n| `ENABLES` | Unlocks a downstream capability | Optimization paths |\n| `RELATES_TO` | Conceptual proximity | Disambiguation |\n| `IMPLEMENTS` | Concrete realization of an abstraction | Architecture mapping |\n| `CONTRASTS_WITH` | Meaningful opposition | Tradeoff reasoning |\n\n### Every domain is a plain-text DAG\n\n```\nConceptID, ConceptLabel, Dependencies, TaxonomyID\n1, Taylor Series, \"\", Analysis\n2, Power Series, \"\", Analysis\n3, Convergence, \"2:REQUIRES\", Analysis\n4, Higher-Order Der., \"5:REQUIRES\", Calculus\n5, Derivative, \"6:REQUIRES\", Calculus\n6, Continuity, \"7:REQUIRES\", Calculus\n```\n\nNo embeddings. No probabilistic retrieval. Built once, reviewed once, traversed forever.\n\n

\n $\"ckg-mcp$ \n

\n\n---\n\n## Agent Team Orchestration — The Scale Story\n\nSingle-agent traversal is the efficiency gain. Multi-agent orchestration is where it compounds.\n\nLiu et al. ([arXiv:2606.30986](https://arxiv.org/abs/2606.30986)) measure **Context Transaction Cost (CTC)**: the tax paid every time context crosses an agent boundary. Their finding: context efficiency collapses from 18.2 in Q1 to 1.6 by Q4 across pipeline stages — **91% degradation with no model change**.\n\nCKG addresses all three root causes they identify:\n\n| CTC component | What it is | CKG's response |\n|---|---|---|\n| Token Latency Burden | Compute cost of transmitting context | 269 tokens instead of 2,982 |\n| Handoff Cost | Serialization loss at agent boundaries | `get_prerequisites()` replaces re-retrieval |\n| Compression Loss | Information destroyed when context is summarized | The graph is the compressed form — done once, offline |\n\nWhen agent A hands off to agent B, neither re-retrieves the domain. They both traverse the same declared graph. **Structured context doesn't consume your context window — it opens it.**\n\n

\n $\"ckg-mcp$ \n

\n\n---\n\n## Quickstart\n\n```bash\nuvx ckg-mcp # no install — runs immediately\n# or\npip install ckg-mcp # Python ≥ 3.10\n```\n\n### Claude Desktop\n\n```json\n{\n \"mcpServers\": {\n \"ckg\": { \"command\": \"uvx\", \"args\": [\"ckg-mcp\"] }\n }\n}\n```\n\n### Claude Code\n\n```bash\nclaude mcp add ckg -- uvx ckg-mcp\n```\n\n### Cursor / Cline / Windsurf / any MCP client\n\n```json\n{ \"mcpServers\": { \"ckg\": { \"command\": \"uvx\", \"args\": [\"ckg-mcp\"] } } }\n```\n\n### System prompt snippet\n\n```\nYou have access to the ckg MCP server — a typed dependency graph catalog\nof 97 domains (mathematics, GPU inference, healthcare, law, robotics,\nregulatory, AI tooling, and more). When answering questions about any of\nthese domains, call query_ckg() or get_prerequisites() before responding.\nDo not infer dependency chains — traverse the graph instead.\n```\n\n### Try it immediately\n\n```\nlist_domains()\n→ see all 68 free domains\n\nquery_ckg(\"Taylor Series\", \"calculus\", 3)\n→ prerequisite chain: Function → Limit → Continuity → Derivative →\n Higher-Order Derivatives → Convergence → Power Series → Taylor Series\n\nget_prerequisites(\"Business Associate Agreement\", \"hipaa-compliance\")\n→ Covered Entity → PHI Definition → Minimum Necessary Standard →\n Access Controls → Breach Notification Rule → BAA\n\nquery_ckg(\"FlashAttention-3\", \"nvidia-gpu-inference\", 3)\n→ SRAM Tiling · On-Chip Memory Budget · Transformer Attention ·\n Softmax Stability → FlashAttention-3 → Multi-Head Attention → KV Cache\n```\n\n---\n\n## Source provenance — verifiable to the byte\n\nEvery node carries a `source_url` and a `source_hash` (SHA-256 of source bytes at extraction time) where available. The full audit chain: edge answer → graph commit hash → source_content_hash → knowledge_source_ref.\n\n```bash\n# For domains with per-node hashes:\ncurl -s | sha256sum\n# compare to source_hash — mismatch = stale edge or silent upstream edit\n```\n\nVia MCP — `verify_source(concept, domain)` returns source URL, hash, and verification command. Run `scripts/refresh_hashes.py` to recompute.\n\nReference implementation from [GuardrailDecisionV1](https://github.com/crewAIInc/crewAI/issues/4877).\n\n---\n\n## Benchmark\n\n> These are our numbers on our open benchmark. The dataset is on HuggingFace. Run it yourself before citing them.\n\n```bash\ngit clone https://github.com/Yarmoluk/ckg-benchmark && cd ckg-benchmark\npip install -r evaluation/requirements.txt\npython evaluation/ckg_harness.py --domain calculus\npython evaluation/analyze_results.py\n```\n\n\n| System | Macro F1 | Tokens / query | Cost / 1K queries | F1 at 5 hops |\n|---|---|---|---|---|\n| **CKG (this package)** | **0.471** | **269** | **$7.81** | **0.772** |\n| RAG (text-embedding-3-small) | 0.123 | 2,982 | $76.23 | 0.170 |\n| GraphRAG (MS global, v1.1) | 0.120 | 3,450+ | — | — |\n\n**What this means:**\n- **4× F1** — in our benchmark, on our dataset. Open and reproducible.\n- **11× fewer tokens** — the 269 and 2,982 figures are averages across 19 benchmark domains.\n- **F1 rises with depth** — CKG 0.37 at 1 hop → 0.77 at 5 hops. RAG is flat. Graph traversal does not degrade at depth; retrieval does.\n- **GraphRAG** — not a meaningful improvement over RAG at higher token cost. The word \"graph\" is not the win. A pre-compiled, declared graph is.\n\nOne derived metric we use internally: **Retrieval Density Score** (F1 ÷ tokens per query). CKG scores roughly 42× higher than RAG on this ratio. It is not a standard benchmark metric — we use it to reason about accuracy-per-token efficiency.\n\n[Full benchmark paper →](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)\n\n---\n\n## Domain Library\n\n### 68 free · no API key required\n\n**Mathematics**\n`calculus` · `pre-calc` · `algebra-1` · `linear-algebra` · `geometry-course` · `statistics-course` · `functions` · `fft-benchmarking`\n\n**Engineering & Computer Science**\n`circuits` · `digital-electronics` · `computer-science` · `quantum-computing` · `signal-processing` · `intro-to-graph`\n\n**Life Sciences**\n`biology` · `bioinformatics` · `genetics` · `ecology` · `chemistry`\n\n**Clinical & Health (free)**\n`glp1-obesity` · `glp1-muscle-loss` · `dementia`\n\n**Regulatory & Government**\n`fda-drug-approval-chain` · `fda-adverse-event-chain` · `federal-procurement-chain` · `gao-oversight-chain`\n\n**AI, ML & Data**\n`machine-learning-textbook` · `data-science-course` · `conversational-ai` · `langchain-core` · `dbt-core` · `apache-iceberg`\n\n**AI Tools (provider graphs)**\n`claude-anthropic` · `claude-skills` · `cursor` · `deepseek` · `gemini-api` · `grok-xai` · `kimi-moonshot` · `midjourney` · `openai-platform` · `qwen` · `vercel-ai-sdk`\n\n**Robotics & Physical AI**\n`ros2-architecture` · `robot-motion-planning`\n\n**Learning & Pedagogy**\n`prompt-class` · `tracking-ai-course` · `automating-instructional-design` · `microsims` · `infographics` · `it-management-graph`\n\n**Business & Society**\n`economics-course` · `personal-finance` · `ethics-course` · `theory-of-knowledge` · `systems-thinking` · `digital-citizenship` · `blockchain` · `unicorns`\n\n**Reference & Culture**\n`art-of-war` · `laudato-si` · `learning-linux` · `us-geography` · `asl-book` · `reading-for-kindergarten` · `moss`\n\n---\n\n### Free vs Pro\n\n| | **Free — MIT** | **Pro — $99/mo** |\n|---|---|---|\n| Domains | 68 | **97** |\n| Healthcare & clinical | — | HIPAA · CPT coding · ICD-10 · payer formulary · drug interactions · clinical decision chain · medical billing |\n| Enterprise data stack | — | Databricks Unity · Snowflake Horizon · PostgreSQL · AWS Data Catalog · Azure Purview · GCP Dataplex · OpenLineage |\n| AI infrastructure | — | NVIDIA GPU inference · context-as-a-service · agent reliability · AI governance · token cost crisis |\n| Legal & compliance | — | Legal citation chain · contract law elements · AML/KYC chain · investment risk chain |\n| Agent blueprints | 2 | 2 + priority access |\n| Domain updates | Community | Managed |\n| License | MIT | Commercial |\n\n**Activate in 60 seconds:**\n\n```bash\nexport CKG_API_KEY=cs_live_your_key_here\n# restart your MCP client — all 97 domains appear in list_domains()\n```\n\n[**Get Pro → graphifymd.com/pro**](https://graphifymd.com/pro)\n\n---\n\n## Agent Blueprints\n\nPre-built agent specs: which domains to load, step-by-step workflow, ready-to-paste system prompt, and a LangGraph orchestration hint. Skip writing the context layer from scratch.\n\n```\nlist_agent_blueprints()\n→ gpu-inference-optimizer — trace GPU bottlenecks, surface optimization paths\n context-as-a-service-advisor — design CKG-based retrieval pipelines\n\nget_agent_blueprint(\"gpu-inference-optimizer\")\n→ Required domains: nvidia-gpu-inference, context-as-a-service\n Workflow: diagnose → trace prerequisites → identify path → recommend\n Prompt template: [ready to paste]\n LangGraph hint: StateGraph · 4 nodes\n```\n\n---\n\n## The Six Tools\n\nAll read-only. No database. No embeddings. No API key for free domains.\n\n| Tool | What it does |\n|---|---|\n| `list_domains()` | Every available domain. **Start here.** |\n| `query_ckg(concept, domain, depth)` | Prerequisites + dependents, up to N hops |\n| `get_prerequisites(concept, domain)` | Full upstream chain in dependency order |\n| `search_concepts(query, domain)` | Find concepts by keyword — use before query_ckg |\n| `list_agent_blueprints()` | Browse pre-built agent configs |\n| `get_agent_blueprint(use_case)` | Full spec: domains, workflow, prompt, LangGraph hint |\n\n---\n\n## Why Graphify.md\n\n**ckg-mcp is the core product of [Graphify.md](https://graphifymd.com).**\n\nWe build the context optimization layer that sits between agents and the domains they operate in. The same layer that powers this package runs inside enterprise deployments, sealed appliances, and custom vertical CKGs.\n\n**What we can say without overstating:**\n- The benchmark is open and reproducible — not self-reported, verifiable\n- The graphs are human-authored and human-reviewed — not generated\n- The methodology is patent pending — not just a wrapper around an existing system\n- Plain CSV DAGs, MIT-licensed for free domains — no lock-in\n\n**Compatibility — model-agnostic:**\n\n| LLM | Agent framework | MCP client |\n|-----|-----------------|------------|\n| Claude (all tiers) | LangChain / LangGraph | Claude Desktop |\n| GPT-4o / GPT-4 | AutoGen | Claude Code |\n| Gemini 2.0 / 2.5 | smolagents | Cursor |\n| Llama 3.x | CrewAI | Cline |\n| Mistral / DeepSeek | OpenAI Agents SDK | Any MCP stdio client |\n\nNo graph database. No vector store. Python ≥ 3.10. Single dependency (`mcp`). stdio transport.\n\n---\n\n## Custom Domains & Enterprise\n\nThe free and Pro catalog covers breadth. Enterprise needs are specific: your regulatory environment, your internal taxonomy, your product domain, your data stack.\n\n**[Graphify.md](https://graphifymd.com)** builds and maintains custom CKG domain graphs for enterprise teams — compressed, versioned, deployed over your MCP stack.\n\n**Sealed Appliance** — a private CKG + query server in your environment. Air-gapped. Your data stays yours.\n\nTypical entry: a pilot on your highest-value domain, delivered in one session, measured against your existing retrieval setup.\n\n---\n\n## Corrections Welcome\n\nSpotted a wrong edge? A `RELATES_TO` that should be `REQUIRES`? A missing concept?\n\nEdge corrections are the highest-value contribution — the graph gets more useful with every fix. Open an issue or PR on [GitHub](https://github.com/Yarmoluk/ckg-mcp).\n\n---\n\n## Ecosystem\n\n| Package | What it does |\n|---|---|\n| **[ckg-mcp](https://pypi.org/project/ckg-mcp/)** | This repo — 97 domains, context optimization layer |\n| **[ckg-nvidia-ai](https://pypi.org/project/ckg-nvidia-ai/)** | 20 NVIDIA AI domains, free, MCP-native |\n| **[agentmem-mcp](https://github.com/Yarmoluk/agent-memory-mcp)** | Cross-session agent memory |\n| **[KRB Benchmark](https://huggingface.co/datasets/danyarm/ckg-benchmark)** | Open benchmark — reproduce the F1 numbers |\n| **[ckg-eval](https://github.com/Yarmoluk/ckg-eval)** | Path-Fidelity Score — reasoning path correctness |\n\n---\n\n## EVAL\n\n```\nbenchmark: ckg-benchmark v0.6.2\ndataset: huggingface.co/datasets/danyarm/ckg-benchmark\nbenchmarked: true\nthis_domain_f1: 0.471\nqueries_tested: 19\nrag_baseline_f1: 0.123\ngraphrag_baseline_f1: 0.120\nmean_tokens: 269\npaper: github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf\n```\n\n---\n\n## Citation\n\n```bibtex\n@misc{yarmoluk2026ckg,\n title = {Benchmarking Knowledge Retrieval Architectures Across Educational\n and Commercial Domains: RAG, GraphRAG, and Compressed Knowledge Graphs},\n author = {Yarmoluk, Daniel and McCreary, Dan},\n year = {2026},\n note = {v0.6.2. https://github.com/Yarmoluk/ckg-benchmark}\n}\n```\n\n---\n\n

\n\n**[graphifymd.com](https://graphifymd.com) · [Pro](https://graphifymd.com/pro) · [Benchmark](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)**\n\n*Patent pending. Built by [Daniel Yarmoluk](https://graphifymd.com) / Graphify.md.*\n\n

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