Content
<!-- mcp-name: io.github.Yarmoluk/ckg-nvidia-nemoclaw -->
# ckg-nvidia-nemoclaw
<p align="center">
<a href="https://yarmoluk.github.io/ckg-nvidia-nemoclaw">
<img src="https://raw.githubusercontent.com/Yarmoluk/ckg-nvidia-nemoclaw/main/assets/og.png" alt="ckg-nvidia-nemoclaw — NemoClaw as a traversable knowledge graph" width="100%"/>
</a>
</p>
<p align="center">
<a href="https://pypi.org/project/ckg-nvidia-nemoclaw/"><img src="https://img.shields.io/pypi/v/ckg-nvidia-nemoclaw?color=0f6e56&label=PyPI" alt="PyPI version"/></a>
<a href="https://pypi.org/project/ckg-nvidia-nemoclaw/"><img src="https://img.shields.io/pypi/pyversions/ckg-nvidia-nemoclaw?color=0f6e56" alt="Python"/></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/data-ELv2-0f6e56" alt="Data: ELv2"/></a>
<a href="LICENSE-CODE"><img src="https://img.shields.io/badge/code-MIT-13201c" alt="Code: MIT"/></a>
<a href="https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf"><img src="https://img.shields.io/badge/F1-0.471_%C2%B74%C3%97_RAG-0f6e56" alt="F1: 0.471 · 4× RAG"/></a>
<a href="https://glama.ai/mcp/servers/Yarmoluk/ckg-nvidia-nemoclaw"><img src="https://glama.ai/mcp/servers/Yarmoluk/ckg-nvidia-nemoclaw/badges/score.svg" alt="ckg-nvidia-nemoclaw MCP server"/></a>
</p>
**An auditable knowledge graph for NVIDIA NemoClaw — deterministic agent answers with cryptographic source traceability.**
Every edge traces to a declared relationship and a SHA-256-pinned source document. Built for platform engineers, agent developers, and docs teams who need verifiable answers about NemoClaw dependencies, runtimes, policy, and deployment paths — not model inference.
> Not a general-purpose semantic search layer. If it's not a declared edge, the graph doesn't return it.
```bash
pip install ckg-nvidia-nemoclaw
# or: uvx ckg-nvidia-nemoclaw
```
[PyPI](https://pypi.org/project/ckg-nvidia-nemoclaw/) · [GitHub](https://github.com/Yarmoluk/ckg-nvidia-nemoclaw) · [Benchmark paper](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf) · [Interactive graph →](https://yarmoluk.github.io/ckg-nvidia-nemoclaw) · [graphifymd.com](https://graphifymd.com)
---
## What it is
55 nodes · 74 edges · the full NemoClaw stack as a typed dependency graph. Pre-structured, traversable, deterministic. Served over MCP. No inference at query time.
```
get_prerequisites("ManagedMCPServer")
→ ManagedMCPServer
├─ [ENABLES] NemoClaw ← platform root
├─ [REQUIRES] NetworkPolicy ← root concept, no dependencies
└─ [REQUIRES] L7Proxy
├─ [IMPLEMENTS] OpenShell
└─ [REQUIRES] SharedGateway
└─ [IMPLEMENTS] InferenceProvider
269 tokens · declared edges only · no inference
RAG equivalent: ~2,982 tokens · probabilistic
```
```
query_ckg("ProgressiveToolDisclosure")
← [IMPLEMENTS] OpenClaw
← [IMPLEMENTS] Hermes
← [IMPLEMENTS] LangChain_Deep_Agents
All three runtimes share this mechanism.
RAG returns three separate docs. The graph knows — it's a declared edge.
```
---
## Source provenance — verifiable to the byte
Every node carries a `source_url` and a `source_hash` (SHA-256 of the source document's bytes at extraction time). An edge isn't just *asserted* from a source — it's pinned to a specific version of it.
```bash
# Verify any node's source hasn't changed since extraction
curl -s https://docs.nvidia.com/nemoclaw/latest/ | sha256sum
# expected: 3d5bc97645f1ea274497ee6b931d9649990504daa9fa9ecc56411c324de0beb8
```
**The full audit chain:**
```
edge answer
→ graph commit hash (git log -- nemoclaw.csv)
→ source_content_hash (sha256 of page bytes at extraction time)
→ knowledge_source_ref (URL — fetch hint, not trust anchor)
```
A hash mismatch means either the source changed (stale edge → re-extract) or the graph was patched without re-fetching (silent edit → investigate). No judgment required. Run `scripts/refresh_hashes.py` to recompute.
Via MCP — `verify_source("CorporateCA")`:
```
source_url: https://docs.nvidia.com/nemoclaw/latest/
source_hash: sha256:3d5bc97645f1ea274497ee6b931d9649990504daa9fa9ecc56411c324de0beb8
verify: curl -s '<url>' | sha256sum
```
Reference implementation of `knowledge_source_ref` + `source_content_hash` from [GuardrailDecisionV1](https://github.com/crewAIInc/crewAI/issues/4877).
---
## What developers are actually hitting
Signal from 123 GitHub issues, [HN 47427027](https://news.ycombinator.com/item?id=47427027), [HN 47435066](https://news.ycombinator.com/item?id=47435066), and hands-on walkthroughs.
**01 — Context bloat in tool loops.** Agents forget tool schemas after loop iterations. The model re-infers NemoClaw's architecture on every query instead of reading declared structure.
**02 — "Which agent is burning my budget?"** OpenShell makes token spend visible per agent for the first time. The next question is how to reduce it. CKG is that answer.
**03 — The Policy Source Gap.** NVIDIA's own OpenShell knowledge graph names this explicitly: the missing layer between the runtime policy engine and the structured knowledge agents need. We filled it.
---
## Declared relationships, not confidence scores
Every edge was extracted from a source document and given a type. No probabilistic weights, no cosine similarity scores, no confidence intervals. An edge either exists — declared, typed, sourced — or it doesn't. When the answer isn't in the graph, the traversal returns nothing rather than a hallucinated approximation.
**Edge types:**
| Type | Meaning | Example |
|------|---------|---------|
| `REQUIRES` | Hard prerequisite — A cannot function without B | `OpenShell REQUIRES L7Proxy` |
| `ENABLES` | Capability unlock — A makes B possible | `ManagedMCPServer ENABLES NetworkPolicy` |
| `IMPLEMENTS` | Concrete instantiation of an abstract concept | `OpenClaw IMPLEMENTS ProgressiveToolDisclosure` |
| `RELATES_TO` | Conceptual proximity, no dependency direction | `SecurityHardening RELATES_TO Sandbox` |
**Why no confidence levels?** The edge type *is* the confidence signal. `REQUIRES` means load-bearing and sourced; `RELATES_TO` means real but weaker. A missing edge is silence from a source-grounded system — not a soft no, not a low-confidence guess.
```
✗ RAG: "CorporateCA is probably used for identity management... (similarity: 0.81)"
Score is on the chunk, not the claim. The claim itself is unverified.
✓ CKG: "CorporateCA is anchored at image build for TLS interception proxy traversal."
No score. Declared edge. Traces to security hardening source doc.
```
---
## A/B test — NemoClaw domain, local models, no GPU
30 questions from real GitHub issues · CPU only · Ollama · temperature 0 · seed 42
| Category | Bare model | + CKG | Lift |
|----------|-----------|-------|------|
| Lookup F1 | 0.100 | 0.171 | **+71%** |
| Multi-hop F1 | 0.058 | 0.100 | **+73%** |
| Prereq-chain F1 | 0.077 | 0.156 | **+103%** |
| Key-fact accuracy | 9.3% | 22.3% | **+13pp** |
> phi4-mini and nemotron-mini truncate at ~2,050 tokens. The CKG is 6,837 tokens — only 30% loads. Prereq-chain F1 still doubles on that fraction. Full-context models widen the gap further.
**L01 — lookup:**
```
Q: What are the three agent runtimes in NemoClaw?
✗ Bare: "NemoClaw supports TensorFlow, PyTorch, and ONNX Runtime..." [invented]
✓ CKG: "OpenClaw (default), Hermes (NEMOCLAW_AGENT=hermes),
LangChain Deep Agents (NEMOCLAW_AGENT=dcode)" [declared edges, correct]
```
**P08 — prereq-chain (best Δ +0.261):**
```
Q: How does CorporateCA integrate into NemoClaw's security chain?
✗ Bare: "CorporateCA, a cloud-native IAM solution from NVIDIA..." [hallucinated]
✓ CKG: "CorporateCA is anchored at the image build stage for TLS
interception proxy traversal." [exact mechanism, correct]
```
**L08 — lookup:**
```
Q: What enterprise manufacturing deployment uses NemoClaw via the FOX Blueprint?
✗ Bare: "FOX (Flexible Open-Source Object Tracking)..." [invented acronym]
✓ CKG: "Foxconn's MoMClaw is a production deployment of the FOX Blueprint." [correct]
```
---
## Install
**Add to claude.ai (no install required):**
```
https://ckg-nvidia-nemoclaw.onrender.com/mcp
```
Settings → Connectors → Add connector → paste URL.
**Local — Claude Desktop / Claude Code:**
```bash
pip install ckg-nvidia-nemoclaw
# or
uvx ckg-nvidia-nemoclaw
```
```json
{
"mcpServers": {
"nemoclaw": {
"command": "uvx",
"args": ["ckg-nvidia-nemoclaw"]
}
}
}
```
---
## Tools
| Tool | Description |
|------|-------------|
| `ask_nemoclaw(question)` | Natural language query — auto-detects concept, traverses the relevant subgraph |
| `query_ckg(concept, depth)` | Typed subgraph around a specific concept (1–5 hops) |
| `get_prerequisites(concept)` | Full upstream prerequisite chain — every dependency in order |
| `search_concepts(query)` | Fuzzy search across all 55 concepts |
| `list_domains()` | Available domains and node/edge counts |
| `verify_source(concept)` | Source URL + SHA-256 hash for any concept — full audit chain back to source bytes |
---
## What's in the graph
**55 nodes · 74 edges · 4 edge types: `REQUIRES` · `ENABLES` · `IMPLEMENTS` · `RELATES_TO`**
| Layer | Concepts |
|-------|----------|
| Agent runtimes | OpenClaw · Hermes (Nous Research) · LangChain Deep Agents |
| Platform | OpenShell · NVIDIA Agent Toolkit · OpenShell TUI · CLI |
| Inference | SharedGateway · vLLM · Ollama · NIM Local · ModelRouter |
| Policy | NetworkPolicy · PolicyTier (Restricted/Balanced/Open) · PolicyPreset · Telegram · Discord · Slack |
| Security | L7Proxy · Landlock LSM · CONNECT Proxy · CorporateCA · SecurityHardening · Sandbox |
| Agent features | Progressive Tool Disclosure · Context Compaction · Heartbeat · Snapshots · Shields |
| Deployment | DGX Spark · DGX Station · macOS Apple Silicon · WSL2 · Brev |
| Ecosystem | FOX Blueprint · MoMClaw (Foxconn) · Nemotron 3 Ultra · Agent Harness |
Every node traces to a source at `docs.nvidia.com/nemoclaw/latest/`, the FOX Blueprint, or the Nemotron 3 Ultra ecosystem docs. Every source is SHA-256 pinned — run `scripts/refresh_hashes.py` to verify.
---
## The dependency graph
> Rendered interactively at [yarmoluk.github.io/ckg-nvidia-nemoclaw](https://yarmoluk.github.io/ckg-nvidia-nemoclaw). On PyPI and plain markdown viewers, the Mermaid source below is human-readable as-is.
```mermaid
graph TD
NC[NemoClaw] --> OS[OpenShell]
NC --> OC[OpenClaw]
NC --> HM[Hermes]
NC --> LC[LangChain Deep Agents]
NC --> MCP[ManagedMCPServer]
NC --> AH[AgentHeartbeat]
OC --> PTD[ProgressiveToolDisclosure]
HM --> PTD
LC --> PTD
OS --> L7[L7Proxy]
OS --> LL[LandlockLSM]
OS --> CP[CONNECT_Proxy]
L7 --> SG[SharedGateway]
SG --> IP[InferenceProvider]
IP --> vLLM[vLLM]
IP --> OLL[Ollama]
IP --> NIM[NIM_Local]
IP --> MR[ModelRouter]
MCP --> NP[NetworkPolicy]
MCP --> L7
NP --> PT[PolicyTier]
NP --> PP[PolicyPreset]
PP --> TG[Telegram]
PP --> DC[Discord]
PP --> SL[Slack]
SH[SecurityHardening] --> SB[Sandbox]
SH --> LL
SH --> BP[NemoClaw_Blueprint]
NIM --> DGX[DGX_Spark]
style NC fill:#0f6e56,color:#fff
style PTD fill:#1a5c47,color:#fff
style SH fill:#1a5c47,color:#fff
style NP fill:#1a5c47,color:#fff
style IP fill:#2d7a5e,color:#fff
```
---
## Community pulse
123 open GitHub issues · [HN 47427027](https://news.ycombinator.com/item?id=47427027) · [HN 47435066](https://news.ycombinator.com/item?id=47435066)
- **#7084** — Hermes shows ready but tool calls silently fail ("phantom-ready") · async state between OpenShell and agent runtime not synchronized
- **#360** (47↑) — "Can I run local with no API key?" · `inference.local` requires NVIDIA API key even in offline mode
- **#1832** — Multi-sandbox SharedGateway conflicts · two sandbox containers claim the same InferenceProvider slot
- **#2991** — Context window fills after ~12 tool calls in OpenClaw · no auto-compaction
- **#5133** — PolicyPreset Telegram/Discord integration underdocumented
---
## Sources
Every node and edge traces to one of these. No probabilistic inference — declared relationships only.
| Type | Source | Coverage |
|------|--------|----------|
| Official | `docs.nvidia.com/nemoclaw/latest/` | Core platform — agent runtimes, OpenShell, security, inference routing, policy |
| Official | FOX Blueprint docs | MoMClaw (Foxconn) manufacturing deployment, enterprise patterns |
| Official | Nemotron 3 Ultra ecosystem | DGX Spark/Station, ModelRouter, NIM Local integration |
| Official | Security hardening guide | LandlockLSM, L7Proxy, CorporateCA, CONNECT Proxy, Sandbox chain |
| Official | Managed MCP Server docs | ManagedMCPServer, NetworkPolicy, PolicyTier, PolicyPreset, messaging bindings |
| Official | Agent features reference | ProgressiveToolDisclosure, Context Compaction, Heartbeat, Snapshots, Shields |
| Community | [github.com/Yarmoluk/ckg-nvidia-nemoclaw/issues](https://github.com/Yarmoluk/ckg-nvidia-nemoclaw/issues) | 123 issues — phantom-ready, local API key, SharedGateway conflicts |
| Community | [HN 47427027](https://news.ycombinator.com/item?id=47427027) · [HN 47435066](https://news.ycombinator.com/item?id=47435066) | Local inference gap (83↑) · token burn visibility |
| Dataset | [huggingface.co/datasets/danyarm/ckg-benchmark](https://huggingface.co/datasets/danyarm/ckg-benchmark) | KRB v0.6.2 — 7,928 queries, 30 NemoClaw-domain questions |
| Benchmark | [github.com/Yarmoluk/ckg-benchmark/paper/main.pdf](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf) | Full methodology, F1 0.471, RAG/GraphRAG baselines |
---
## Benchmark (KRB v0.6.2 locked)
| System | Macro F1 | Mean tokens | Cost / 1k queries |
|--------|----------|-------------|-------------------|
| **CKG** | **0.471** | 269 | $7.81 |
| RAG | 0.123 | 2,982 | $76.23 |
| GraphRAG | 0.120 | ~3,000 | ~$76 |
7,928 queries · 5-hop F1: 0.772 (CKG) vs 0.170 (RAG) · [dataset](https://huggingface.co/datasets/danyarm/ckg-benchmark) · [full paper](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)
---
## Licensing
Three layers, three licenses, one plain-English answer to each question you actually have:
| Layer | License | Plain English |
|-------|---------|---------------|
| **Server code** — `server.py`, `graph.py`, `serve.py`, `scripts/` | MIT ([LICENSE-CODE](LICENSE-CODE)) | Do anything. Fork it, embed it, sell products built on it. No attribution required. |
| **Graph data** — `domains/nemoclaw.csv` + source hashes | Elastic License 2.0 ([LICENSE](LICENSE)) | Free for all internal and commercial use. The one thing you cannot do: offer this graph as a hosted or managed service that competes with Graphify.md. |
| **Extraction pipeline, benchmark harness, curation methodology** | Proprietary — Graphify.md | Not in this repo. This is the compounding asset — how 97 domains get built and maintained, not the outputs. |
**Can I build an agent or product using this CKG?** Yes. No restrictions, no attribution required.
**Can I run this inside my company's infrastructure?** Yes. ELv2 allows all internal commercial use.
**Can I fork the server code and build my own CKG on a different domain?** Yes. Server code is MIT.
**Can I offer "NemoClaw CKG as a Service" commercially?** No. That's the one thing ELv2 blocks.
**What's actually proprietary?** Not the graph topology — anyone can read the NVIDIA docs. What's proprietary is the extraction process that decides *which* 55 nodes out of thousands matter, *why* L7Proxy `REQUIRES` SharedGateway and not `RELATES_TO`, and the benchmark framework that proves the result is correct.
---
## EVAL
```
benchmark: ckg-benchmark v0.6.2
dataset: huggingface.co/datasets/danyarm/ckg-benchmark
benchmarked: true
this_domain_f1: 0.576
queries_tested: 28
baseline_f1: 0.156
lift_vs_baseline: +269%
model: phi4-mini (Ollama local)
rag_baseline_f1: 0.123
graphrag_baseline_f1: 0.120
mean_tokens: 269
paper: github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf
```
---
Built by [Graphify.md](https://graphifymd.com) · [97 domains](https://graphifymd.com/pro/) · [PyPI](https://pypi.org/project/ckg-nvidia-nemoclaw/) · patent pending
*Community-built. Not affiliated with, endorsed by, or sponsored by NVIDIA Corporation. NemoClaw is a trademark of NVIDIA Corporation. All referenced trademarks belong to their respective owners.*
