# Post 2: What the Graph Already Knows

**Series:** The Graph-of-Agents Reasoning Model (2 of 7)
**Visual:** `stories/query-journey/story-traversal-1-query.png`
**Platforms:** LinkedIn, Twitter, Instagram carousel

---

## LinkedIn / Facebook Caption

```
Before GraQle asks an LLM anything, it checks what
the graph already knows.

Here's what the MEMORY layer sees — without spending
a single token:

📊 EDGES (structural)
→ auth.py is imported by: routes.py, api.py, middleware.py
→ auth.py imports: jwt, bcrypt, config
→ auth.py is tested by: test_auth.py

👥 CONSUMERS (who depends on this?)
→ 3 direct consumers
→ 8 transitive consumers (2nd + 3rd hop)
→ Total impact radius: 11 modules

📅 TEMPORAL
→ Last modified: 2 days ago
→ Modified 14 times this month
→ Hit frequency: queried 14 times (hot module)

⚠️ LESSONS (institutional memory)
→ "Never skip refresh token rotation — caused outage 2026-01-15"
→ "auth.verify_token() returns None on expired, not an exception"

📏 DEPTH
→ Dependency depth: 3 (auth → routes → api → client)
→ Centrality score: 0.84 (hub module)

All of this is FREE. Zero LLM cost.
Pure graph traversal over NetworkX.

This is why we check memory FIRST.
Most of the answer is already in the graph.

The LLM's job is just to synthesize — not to discover.

#GraQle #KnowledgeGraph #DevTools #AI
```

## Twitter / X Thread (continuation)

```
4/ MEMORY: What the graph already knows about auth.py

Without asking any LLM:

EDGES:
→ 3 importers
→ 2 imports
→ 1 test file

CONSUMERS:
→ 3 direct, 8 transitive = 11 total impact

LESSONS:
→ "never skip refresh token rotation"

STATS:
→ Modified 14x this month
→ Centrality: 0.84 (hub)

Cost: $0. Time: <1ms.
```

```
5/ Think about that.

Most of the answer to "what depends on auth?"
is already in the graph edges.

The LLM doesn't need to READ auth.py to know
who depends on it. The graph TELLS it.

This is what makes graph-first reasoning
fundamentally different from RAG.
```

## Instagram Carousel (Slide 2)

```
WHAT THE GRAPH ALREADY KNOWS 🧠

Before any AI model is called:

EDGES
→ 3 modules import auth.py
→ auth.py imports jwt, bcrypt, config

CONSUMERS
→ 11 modules affected if auth changes

LESSONS
→ "never skip refresh token rotation"
→ "verify_token returns None, not exception"

COST: $0
TIME: <1ms

The graph remembers.
The LLM just synthesizes.
```
