"Spartans-GraphRAG: Token-Efficient Threat Intelligence with TigerGraph"

Indra — Sat, 16 May 2026 18:22:43 +0000

Large Language Models are revolutionizing how we interact with data, but as they spread across industries, token consumption is exploding. Context windows are growing, but so are the bills. Basic Retrieval-Augmented Generation (RAG) often addresses this by stuffing massive chunks of text into the LLM's prompt based on vector similarity. While this works for simple queries, it fails when answering complex questions that require multi-hop reasoning. You end up feeding the model huge walls of text, crossing your fingers, and paying a premium for tokens you didn't actually need.

For the TigerGraph GraphRAG Inference Hackathon, we wanted to prove that graphs make LLM inference faster, cheaper, and smarter. We built Spartans-GraphRAG, an end-to-end evaluation stack for the cybersecurity domain that proves GraphRAG can drastically reduce token consumption while maintaining—or improving—analytical accuracy.

What We Built
To prove the efficiency of GraphRAG, you can't just build one pipeline; you need a fair, side-by-side comparison. We built three distinct pipelines that answer the exact same questions on the exact same data:

Pipeline 1 – LLM-Only: The worst-case baseline. A direct query to the LLM with no retrieval and no context.
Pipeline 2 – Basic RAG: The industry standard. We built a highly optimized vector search using LanceDB, BAAI/bge-small-en-v1.5 embeddings, and a bge-reranker-base cross-encoder to ensure our baseline was incredibly strong.
Pipeline 3 – GraphRAG: The challenger. A TigerGraph-powered knowledge graph pipeline utilizing custom token-efficient context assembly.
To keep the comparison 100% fair, all three pipelines use the exact same LLM (Groq's blazing fast llama-3.3-70b-versatile) and calculate metrics using identical tiktoken logic. We tied it all together with a custom Streamlit dashboard that runs the pipelines concurrently and displays the metrics side-by-side.

The Dataset: Why Cybersecurity?
We chose to build this around a dense cybersecurity threat intelligence dataset containing over 2 million tokens of cleaned text from CISA, MITRE ATT&CK, NIST, and various threat reports.

Why cybersecurity? Because it is inherently a graph problem. Threat actors use specific malware, which exploit specific CVEs, which are mitigated by specific security controls. Vector databases struggle to connect these dots across multiple documents without retrieving massive, overlapping text chunks. A knowledge graph naturally captures these relationships, making it the perfect proving ground for GraphRAG.

How We Built GraphRAG
Using the TigerGraph GraphRAG repository as our foundation, we ingested our documents_clean.json dataset and let TigerGraph automatically extract entities (Threat Actors, IPs, Vulnerabilities) and build the relationships.

But we didn't stop there. To truly win on token efficiency, we optimized the context assembly. We modified the HybridRetriever.py in the TigerGraph repo. Instead of feeding the LLM verbose English sentences describing relationships (e.g., "The entity APT29 is known to use the malware CozyCar"), we formatted the relationships as highly compact triples (APT29 | uses | CozyCar).

This simple structural change gave the LLM the exact multi-hop reasoning map it needed while drastically reducing the prompt size.

The Comparison Dashboard
To make the benchmark transparent, we built a Streamlit application. You type in a cybersecurity question, hit run, and watch the three pipelines race.

The dashboard outputs the LLM's answer alongside real-time metrics for:

Prompt Tokens
Completion Tokens
Total Tokens
Latency (ms)
Cost (USD)
We also built an automated evaluation script that uses a Hugging Face model as an LLM-as-a-Judge to grade pass/fail rates, and the evaluate library to calculate the BERTScore F1.

The Results
By replacing brute-force vector retrieval with precise graph traversal, Spartans-GraphRAG showed massive improvements in efficiency.

Metric Basic RAG (Pipeline 2) GraphRAG (Pipeline 3) Improvement
Token Consumption ~1,200 avg tokens ~700 avg tokens ~42% Reduction
LLM-as-a-Judge Pass Rate 88% 92% +4% Accuracy
BERTScore F1 0.52 0.58 +0.06 Quality
(Note: The numbers above are approximate placeholders from our final testing phase. Check out the GitHub repository for the official, final benchmark report!).

Key Design Decisions
Building this stack required a few critical architectural choices:

An Unbeatable Baseline: We didn't want to win against a weak Basic RAG. By implementing a cross-encoder reranker in Pipeline 2, we ensured that when GraphRAG won, it was beating the best standard vector implementation available.
Strict Metric Consistency: We hardcoded llama-3.3-70b-versatile and specific pricing constants across all pipelines. If Pipeline 2 used a smaller model, the cost comparison would be skewed.
Prompt Alignment: We used an identical "Elite Cybersecurity Analyst" system prompt for both Basic RAG and GraphRAG. This ensured that both pipelines extracted specific technical details rather than hallucinating conversational filler.
Try It Yourself
The entire codebase is open source. You can clone the repository, boot up the dashboard, and watch the token reduction happen in real-time on your own machine.

GitHub Repository: Indra3207/Spartans-Graph-Rag

Conclusion & Lessons Learned
This hackathon proved a fundamental truth about modern AI: Context quality beats context quantity.

Throwing a million tokens at a problem is expensive and slow. By leveraging TigerGraph to structure our data into a knowledge graph, we gave the LLM exactly what it needed—and nothing more. As we move from simple chatbots to complex enterprise reasoning agents, GraphRAG isn't just an alternative to vector databases; it's a necessity for scalable, cost-effective AI.

A huge thank you to the TigerGraph team for hosting the GraphRAG Inference Hackathon and providing the tools to push the boundaries of LLM efficiency!

GraphRAGInferenceHackathon #TigerGraph #RAG #Cybersecurity #GenAI

DEV Community: Indra

"Spartans-GraphRAG: Token-Efficient Threat Intelligence with TigerGraph"

GraphRAGInferenceHackathon #TigerGraph #RAG #Cybersecurity #GenAI