# Post 5: Graph-of-Agents vs RAG — Why It's 100x Cheaper

**Series:** The Graph-of-Agents Reasoning Model (5 of 7)
**Visual:** `stories/query-journey/story-traversal-3-consensus.png`
**Platforms:** LinkedIn, Twitter, HackerNews, Reddit

---

## LinkedIn / Facebook Caption

```
"But isn't this just RAG?"

No. And here's the architectural difference.

RAG (Retrieval-Augmented Generation):
1. Embed the query
2. Vector search across all chunks
3. Retrieve top-k chunks (often 10-20)
4. Stuff them all into one LLM prompt
5. One model answers alone

GraQle (Graph-of-Agents):
1. Graph memory — who's connected? ($0)
2. Embed the query — who's relevant? ($0.00001)
3. Activate subgraph — 8 nodes, not 10,000
4. Each node becomes an agent
5. Agents exchange messages and negotiate
6. Consensus with confidence scoring

The differences that matter:

CONTEXT SELECTION
RAG: top-k by vector similarity (misses structural deps)
GraQle: graph traversal + embedding (catches non-obvious connections)

REASONING
RAG: single model, single prompt, no verification
GraQle: multi-agent, multi-round, consensus + confidence

COST
RAG: ~50,000 tokens/query ($0.15)
GraQle: ~500 tokens/query ($0.0003)

RELIABILITY
RAG: "I think auth is used by..." (no confidence score)
GraQle: "auth.py consumed by routes, api, middleware —
         92% confidence, evidence: 3 import edges + 2 call sites"

MEMORY
RAG: stateless (forgets between sessions)
GraQle: persistent graph (remembers lessons, past mistakes,
         team knowledge across sessions)

RAG reads chunks. GraQle reads architecture.

That's not an incremental improvement.
It's a different paradigm.

#RAG #GraphOfAgents #AI #KnowledgeGraph #GraQle
```

## Twitter / X Thread (continuation)

```
12/ "But isn't GraQle just RAG?"

No.

RAG: embed → retrieve chunks → stuff into prompt → one model guesses
GraQle: graph memory → embed → activate subgraph → agents negotiate → consensus

Fundamental difference: structure vs similarity.
```

```
13/ RAG finds what's SIMILAR to your query.
GraQle finds what's CONNECTED + SIMILAR.

"What depends on auth?" with RAG:
→ Retrieves auth.py chunks (high similarity)
→ Misses routes.py (low textual similarity, HIGH structural dependency)

GraQle:
→ Graph knows routes imports auth (structural edge)
→ Embedding confirms relevance (semantic match)
→ Both get activated
```

```
14/ The other big difference: confidence.

RAG answer: "I think auth is used by routes and maybe api"
(no score, no evidence, no verification)

GraQle answer: "auth.py consumed by routes (3 endpoints),
api (2 imports), middleware (1 wrapper)"
→ 92% confidence
→ Evidence: 3 import edges, 2 call sites
→ Scored on: relevance, completeness, evidence, consensus, governance

One is a guess. The other is a governed response.
```

## Reddit (r/MachineLearning, r/LLMDevs)

```
Title: Graph-of-agents vs RAG for code reasoning — architectural comparison

I keep getting asked "isn't GraQle just RAG?" so here's the
actual architectural comparison.

RAG pipeline:
query → embed → vector search (all chunks) → top-k retrieval →
stuff into single prompt → one LLM call → answer

GraQle pipeline:
query → graph memory ($0, structural edges) →
embed query ($0.00001, 1 call against cached chunks) →
activate subgraph (8 of 10K nodes) →
each node = agent → multi-round message passing →
consensus scoring (5 axes) → governed answer

Key differences:

1. CONTEXT SELECTION: RAG uses vector similarity only.
GraQle uses graph structure (edges, imports, calls) PLUS
similarity. This catches dependencies that are textually
dissimilar but structurally critical.

2. REASONING: RAG = one model, one prompt, no verification.
GraQle = N agents, multi-round negotiation, consensus.

3. TOKEN EFFICIENCY: RAG stuffs top-k chunks (~50K tokens).
GraQle sends only the activated subgraph (~500 tokens).

4. PERSISTENCE: RAG is stateless. GraQle's graph persists
lessons, past mistakes, and team knowledge across sessions.

Benchmarks on our codebase (10K nodes):
- RAG: 50K tokens, $0.15, ~2min, no confidence
- GraQle: 500 tokens, $0.0003, 5.2s, 92% confidence

The core insight: for code, structure matters more than
similarity. A knowledge graph captures structure.
Vector search doesn't.

Paper: arXiv:2508.00031 (GCC framework, 48% SWE-Bench-Lite SOTA)
GitHub: github.com/quantamixsol/graqle
```
