ckg-nvidia-nemoclaw

<!-- 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.n