# Post 7: The Full Picture — Why This Architecture Works

**Series:** The Graph-of-Agents Reasoning Model (7 of 7)
**Visual:** `stories/node-anatomy/story-node-anatomy.png` (the three rings)
**Platforms:** LinkedIn, Twitter, Reddit, HackerNews

---

## LinkedIn / Facebook Caption

```
7 posts later, here's the full architecture in one view.

QUESTION:
"What breaks if I change auth?"

LAYER 1 — MEMORY ($0, <1ms)
┌─────────────────────────────────┐
│ Graph traversal                 │
│ → 3 direct consumers            │
│ → 11 transitive dependencies    │
│ → Past lesson: "never skip      │
│   refresh token rotation"       │
│ → Hub score: 0.84               │
│ Cost: $0                        │
└─────────────────────────────────┘
         ↓ 10,127 → 11 nodes

LAYER 2 — EMBEDDING ($0.00001, <1s)
┌─────────────────────────────────┐
│ 1 vector call (query only)      │
│ Cosine sim against cached npz   │
│ → auth: 0.94 ✓                  │
│ → routes: 0.87 ✓                │
│ → api: 0.82 ✓                   │
│ → middleware: 0.79 ✓             │
│ → config: 0.31 ✗                │
│ Cost: ~$0.00001                 │
└─────────────────────────────────┘
         ↓ 11 → 8 nodes

LAYER 3 — LLM REASONING ($0.0003, 5s)
┌─────────────────────────────────┐
│ 8 agents activated              │
│ 2 rounds of message passing     │
│ Agents negotiate:               │
│  auth→routes: "Do you use JWT?" │
│  routes→auth: "3 endpoints do"  │
│  api→auth: "I import AuthError" │
│ Consensus reached               │
│ Cost: $0.0003 (500 tokens)      │
└─────────────────────────────────┘
         ↓

ANSWER (92% confidence)
┌─────────────────────────────────┐
│ Changing auth.py will break:    │
│ → routes.py (3 endpoints)       │
│ → api.py (2 imports)            │
│ → middleware.py (request wrap)   │
│ Impact: 11 modules              │
│ Confidence: 92%                 │
│ Evidence: 3 edges + 2 call sites│
│ Cost: $0.0003 total             │
│ Time: 5.2s                      │
└─────────────────────────────────┘

That's it. The whole model.

Cheapest layer first. Most expensive last.
Graph structure before semantic similarity.
Semantic similarity before LLM reasoning.
Multi-agent consensus instead of single-model guessing.

This is why GraQle answers cost $0.0003 instead of $0.15.
Not because the model is cheaper.
Because 99% of the work happens before the model is called.

The model's job isn't to discover.
It's to synthesize what the graph already knows.

7 posts. 1 architecture. 100x cheaper.

pip install graqle
https://graqle.com

#GraQle #GraphOfAgents #AI #KnowledgeGraph #Architecture #DevTools
```

## Twitter / X Thread (final)

```
18/ The full picture:

Memory:    10,127 → 11 nodes ($0)
Embedding: 11 → 8 nodes ($0.00001)
LLM:       8 agents, 500 tokens ($0.0003)
Answer:    92% confidence

99% of the work happens before the model.

That's the entire insight behind GraQle.
Cheapest first. Expensive last.
```

```
19/ The model's job isn't to discover your architecture.
It's to synthesize what the graph already knows.

That's why 500 tokens is enough.
That's why it costs $0.0003.
That's why it takes 5 seconds.

Memory first. Embeddings second. LLM last.

pip install graqle
graqle.com
```

```
20/ If you made it this far:

GraQle is free to use (500 nodes, 3 queries/month).
Works with Claude Code, Cursor, VS Code Copilot.
14 LLM backends. Fully offline with Ollama.

pip install graqle && graq scan repo . && graq run "explain the architecture"

GitHub: github.com/quantamixsol/graqle
Docs: graqle.com

End of thread 🧵
```
