# Knowledge Series: The Graph-of-Agents Reasoning Model

> A 7-post educational series explaining WHY GraQle's Memory → Embedding → LLM architecture is 100x cheaper than RAG.

---

## Series Overview

| # | Title | Key Insight | Visual Asset |
|---|-------|-------------|-------------|
| 1 | [Why Memory First?](01-why-memory-first.md) | Cheapest layer first, most expensive last | `node-anatomy` (three rings) |
| 2 | [What the Graph Already Knows](02-what-graph-memory-knows.md) | Edges, consumers, lessons, depth — all free | `query-journey/1-query` (node activation) |
| 3 | [The Embedding Layer](03-embedding-layer.md) | 1 vector call, not 10,000. Cached .npz | `node-square` (middle purple ring) |
| 4 | [LLM Reasoning Comes Last](04-llm-reasoning-last.md) | Agents negotiate, not one model guessing | `query-journey/2-messages` (agent chat) |
| 5 | [Graph-of-Agents vs RAG](05-vs-rag.md) | Structure + similarity > similarity alone | `query-journey/3-consensus` (green graph) |
| 6 | [Why Confidence Scores Matter](06-confidence-scoring.md) | 5-axis scoring turns guesses into decisions | `query-journey/3-consensus` (scoring bars) |
| 7 | [The Full Picture](07-full-picture.md) | Complete architecture in one ASCII diagram | `node-anatomy` (full view) |

---

## Visual-to-Post Mapping

```
stories/node-anatomy/
├── story-node-anatomy.png     ← Used by: Post 1, Post 7
└── story-node-anatomy.svg

stories/query-journey/
├── story-traversal-1-query.png      ← Used by: Post 2
├── story-traversal-2-messages.png   ← Used by: Post 4
└── story-traversal-3-consensus.png  ← Used by: Post 5, Post 6

posts/
└── story-node-square.png     ← Used by: Post 3
```

---

## Posting Strategy

### LinkedIn (primary — professional devs)

Post all 7 over 2 weeks. Each post stands alone but builds on the previous.

| Day | Post | Hook |
|-----|------|------|
| Mon Week 1 | #1 Why Memory First | "Most AI tools: Question → LLM. We do: Question → Memory → Embedding → LLM" |
| Wed Week 1 | #2 Graph Memory | "Before GraQle asks an LLM anything, it checks what the graph already knows" |
| Fri Week 1 | #3 Embedding Layer | "We DON'T re-embed the codebase every query" |
| Mon Week 2 | #4 LLM Last | "Each activated node becomes an AGENT. They TALK to each other." |
| Wed Week 2 | #5 vs RAG | "'But isn't this just RAG?' No. Here's why." |
| Fri Week 2 | #6 Confidence | "'92% confidence' is not marketing. It's 5 independent measurements." |
| Mon Week 3 | #7 Full Picture | "7 posts. 1 architecture. 100x cheaper." |

### Twitter/X (thread format)

Combine all 7 posts into one 20-tweet thread. Post the thread once, then quote-tweet individual posts over the following days with the corresponding image.

### Reddit

Post #1 (Why Memory First) to r/Python and r/programming.
Post #5 (vs RAG) to r/MachineLearning and r/LLMDevs.
Post #7 (Full Picture) to r/ExperiencedDevs.

### Instagram

Create a 7-slide carousel using the square format images + text overlays.

---

## The Core Narrative Arc

```
Post 1: THE INSIGHT
  "Memory first. LLM last."

Post 2-3: THE CHEAP LAYERS
  "The graph already knows. Embeddings narrow."

Post 4: THE EXPENSIVE LAYER (used sparingly)
  "Agents negotiate, not one model guessing."

Post 5: THE COMPARISON
  "This isn't RAG. Structure > similarity."

Post 6: THE TRUST MECHANISM
  "Confidence scores turn guesses into decisions."

Post 7: THE PAYOFF
  "100x cheaper. Same question. Better answer."
```

---

## Key Numbers to Repeat

Use these consistently across all posts:

| Metric | Value | Context |
|--------|-------|---------|
| Token reduction | **100x** | 50,000 → 500 |
| Cost reduction | **500x** | $0.15 → $0.0003 |
| Speed improvement | **24x** | 2 min → 5 sec |
| Confidence | **92%** | Scored, not claimed |
| Memory cost | **$0** | Pure graph traversal |
| Embedding cost | **$0.00001** | 1 vector call |
| LLM cost | **$0.0003** | 500 tokens |
| Agents | **8** | Multi-agent consensus |
| Nodes filtered | **10,127 → 8** | Memory + embedding |
| Scoring axes | **5** | Relevance, completeness, evidence, consensus, governance |
