# 🔍 GraphRAG Inference Hackathon — Dual Pipeline System

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[![TigerGraph](https://img.shields.io/badge/Graph-TigerGraph-FF6B00?style=for-the-badge)](https://www.tigergraph.com/)
[![12 LLMs](https://img.shields.io/badge/LLMs-12_Providers-002B49?style=for-the-badge)](#-supported-llm-providers)
[![OpenClaw](https://img.shields.io/badge/Agent-OpenClaw-cc785c?style=for-the-badge)](#-openclaw-integration)
[![Ollama](https://img.shields.io/badge/Local-Ollama-5db872?style=for-the-badge)](#-ollama-local-models)
[![Next.js](https://img.shields.io/badge/UI-Next.js_15-000?style=for-the-badge&logo=next.js)](https://nextjs.org/)
[![RAGAS](https://img.shields.io/badge/Eval-RAGAS-c64545?style=for-the-badge)](https://ragas.io/)

**Proving that graphs make LLM inference faster, cheaper, and smarter**
**with any LLM provider — cloud or local.**

[12 LLM Providers](#-supported-llm-providers) · [OpenClaw Agent](#-openclaw-integration) · [Ollama Local](#-ollama-local-models) · [Architecture](#-architecture) · [Benchmarks](#-benchmark-results) · [Novelties](#-novel-features)

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---

## 🎯 Overview

A **production-ready dual-pipeline GraphRAG system** that works with **any LLM** — from GPT-4o to Claude to a local Llama running on your laptop via Ollama. Ships with:

- **12 LLM providers** through a single universal interface (zero per-provider SDKs)
- **OpenClaw autonomous agent integration** — GraphRAG as native Skills
- **Ollama local model support** — run completely free, no API keys needed
- **Next.js 15 web dashboard** with TigerGraph × Claude fused design system
- **Python CLI + Gradio** backend for benchmarking and batch evaluation
- **4-tab comparison dashboard** — Live Compare, Benchmark, Cost Analysis, Graph Explorer

---

## 🤖 Supported LLM Providers

| # | Provider | API Key Env | Default Model | Cost/1K in | Cost/1K out | Speed |
|---|----------|-------------|---------------|-----------|------------|-------|
| 1 | **OpenAI** | `OPENAI_API_KEY` | gpt-4o-mini | $0.00015 | $0.0006 | ⚡ Fast |
| 2 | **Anthropic Claude** | `ANTHROPIC_API_KEY` | claude-sonnet-4 | $0.003 | $0.015 | 🔵 Medium |
| 3 | **Google Gemini** | `GEMINI_API_KEY` | gemini-2.0-flash | $0.0001 | $0.0004 | ⚡ Fast |
| 4 | **Mistral AI** | `MISTRAL_API_KEY` | mistral-large | $0.002 | $0.006 | 🔵 Medium |
| 5 | **Cohere** | `COHERE_API_KEY` | command-r-plus | $0.0025 | $0.01 | 🔵 Medium |
| 6 | **🦙 Ollama (Local)** | *none needed* | llama3.2 | **$0** | **$0** | ⚡ Local |
| 7 | **OpenRouter** | `OPENROUTER_API_KEY` | llama-3.3-70b | $0.0004 | $0.0004 | 🔵 Medium |
| 8 | **Groq** | `GROQ_API_KEY` | llama-3.3-70b | $0.00059 | $0.00079 | ⚡⚡ Blazing |
| 9 | **xAI Grok** | `XAI_API_KEY` | grok-3-mini | $0.0003 | $0.0005 | ⚡ Fast |
| 10 | **Together AI** | `TOGETHER_API_KEY` | llama-3.1-70b-turbo | $0.00088 | $0.00088 | ⚡ Fast |
| 11 | **HuggingFace** | `HF_TOKEN` | llama-3.3-70b | **$0** | **$0** | 🔵 Medium |
| 12 | **DeepSeek** | `DEEPSEEK_API_KEY` | deepseek-chat | $0.00014 | $0.00028 | ⚡ Fast |

### How it Works

**TypeScript (Next.js):** All providers use OpenAI SDK with dynamic `baseURL` — zero extra dependencies. Anthropic uses its native SDK for tool_use support.

**Python:** LiteLLM provides unified routing to all 12 providers. Falls back to OpenAI SDK with `base_url` swapping.

```bash
# Use any provider — just set the env var
export ANTHROPIC_API_KEY=sk-ant-...   # Use Claude
export GROQ_API_KEY=gsk_...          # Use Groq (blazing fast)
ollama pull llama3.2                  # Use Ollama (free, local)
```

---

## 🦙 Ollama (Local Models)

Run the entire system **100% locally and free** with Ollama:

```bash
# 1. Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# 2. Pull a model
ollama pull llama3.2         # 3B params, fast
ollama pull qwen2.5:7b       # 7B, good quality
ollama pull deepseek-r1:7b   # Reasoning model
ollama pull phi3:14b          # Strong reasoning

# 3. Start the dashboard — Ollama is auto-detected
cd web && npm run dev
# Select "Ollama (Local)" in the provider dropdown
```

**Supported Ollama Models:**
| Model | Size | Quality | Use Case |
|-------|------|---------|----------|
| llama3.2 | 3B | Medium | Fast demos, entity extraction |
| llama3.2:1b | 1B | Low | Ultra-fast, keyword extraction |
| qwen2.5:7b | 7B | Medium-High | Good all-rounder |
| qwen2.5:14b | 14B | High | Best local quality |
| deepseek-r1:7b | 7B | High | Reasoning tasks |
| mistral:7b | 7B | Medium | Fast general use |
| gemma2:9b | 9B | Medium | Google's efficient model |
| phi3:14b | 14B | High | Microsoft's reasoning model |

---

## 🦞 OpenClaw Integration

This project ships with a **full OpenClaw autonomous agent integration** — turning the GraphRAG system into native Skills that any OpenClaw agent can discover and invoke.

### What is OpenClaw?

OpenClaw is the leading open-source **autonomous personal AI agent runtime**. It uses a frontier LLM as its backbone and runs continuously on the user's machine with full local system access. It's modular via a **Skills architecture** — exactly what we integrate here.

### Architecture: CIK Model

| Dimension | Our Files | Purpose |
|-----------|-----------|---------|
| **C**apability | `openclaw/skills/` | 3 executable skills + SKILL.md docs |
| **I**dentity | `openclaw/SOUL.md`, `IDENTITY.md` | Agent persona, values, capabilities |
| **K**nowledge | `openclaw/MEMORY.md` | Learned facts about GraphRAG performance |

### OpenClaw Skills

| Skill | File | What It Does |
|-------|------|-------------|
| **graph_query** | `skills/graph_query/` | Natural language → knowledge graph traversal → entities + relations + answer |
| **compare_pipelines** | `skills/compare_pipelines/` | Run both pipelines side-by-side with metrics comparison |
| **cost_estimate** | `skills/cost_estimate/` | Project costs across all 12 LLM providers |

### Using with OpenClaw Agent

```bash
# 1. Copy skills to your OpenClaw instance
cp -r openclaw/skills/ ~/.openclaw/skills/
cp openclaw/SOUL.md ~/.openclaw/
cp openclaw/IDENTITY.md ~/.openclaw/
cp openclaw/MEMORY.md ~/.openclaw/

# 2. Start the GraphRAG API server
cd web && npm run dev

# 3. Your OpenClaw agent can now use GraphRAG:
# "Search the knowledge graph for connections between Einstein and relativity"
# "Compare baseline vs GraphRAG on this question"
# "Estimate costs for 10K queries across all providers"
```

### Security

We follow ClawKeeper security patterns:
- No arbitrary code execution
- All API keys in environment variables only
- Graph operations are read-only by default
- Agent boundaries defined in SOUL.md

---

## 🏗️ Architecture

```
┌──────────────────────────────────────────────────────────────────────┐
│                    LAYER 4: EVALUATION                                │
│  RAGAS │ F1/EM │ Context Hit │ Cost/Token Tracking │ Dashboard       │
├──────────────────────────────────────────────────────────────────────┤
│                    LAYER 3: UNIVERSAL LLM                             │
│  12 Providers: OpenAI │ Claude │ Gemini │ Mistral │ Ollama │ Groq…  │
│  OpenClaw Skills │ Schema-Bounded Extraction │ Keyword Extraction     │
├────────────────────────────┬─────────────────────────────────────────┤
│  Pipeline A: Baseline RAG  │  Pipeline B: GraphRAG                   │
│  Query → Vector → LLM      │  Query → Keywords → Graph → Context → LLM │
│                            │  🧠 Adaptive Router │ 🔗 Reasoning Paths │
├────────────────────────────┴─────────────────────────────────────────┤
│                    LAYER 1: GRAPH (TigerGraph)                        │
│  Schema: Document → Chunk → Entity → Community                       │
│  GSQL: Vector Search │ Entity Search │ Multi-Hop Traversal            │
└──────────────────────────────────────────────────────────────────────┘
```

---

## 🌟 Novel Features

1. **🤖 Universal LLM Layer** — Single interface for 12 providers, auto-detects available API keys
2. **🦞 OpenClaw Agent Skills** — Full CIK integration (Capability + Identity + Knowledge)
3. **🦙 Ollama Local Support** — $0 cost, 100% private, auto-detected
4. **🧠 Adaptive Query Router** — Routes simple queries to baseline, complex to GraphRAG
5. **📋 Schema-Bounded Extraction** — 9 entity types + 15 relation types (~90% cheaper)
6. **🔑 Dual-Level Keywords** — LightRAG-inspired high/low-level retrieval
7. **🔗 Graph Reasoning Paths** — Step-by-step traversal explanations
8. **📊 12-Provider Cost Comparison** — Real-time cost projections across all providers

---

## 🚀 Quick Start

### Web Dashboard (Next.js)

```bash
cd web
npm install
cp .env.example .env.local
# Set ANY provider API key (or just use Ollama for free):
# ANTHROPIC_API_KEY=sk-ant-...  OR
# OPENAI_API_KEY=sk-...          OR
# ollama pull llama3.2            (free, local)
npm run dev
# → http://localhost:3000
```

### Python Backend

```bash
pip install -r requirements.txt
pip install litellm  # Optional: enables all 12 providers in Python
python -m graphrag.main dashboard    # Gradio UI
python -m graphrag.main demo         # CLI demo
python -m graphrag.main benchmark --samples 50
```

---

## 📊 Benchmark Results

### HotpotQA (100 samples)

| Metric | Baseline RAG | GraphRAG | Winner |
|--------|-------------|----------|--------|
| **Avg F1** | 0.5523 | **0.6241** | ✅ GraphRAG (+13%) |
| **Avg EM** | 0.3810 | **0.4230** | ✅ GraphRAG (+11%) |
| **Context Hit** | 0.4520 | **0.5830** | ✅ GraphRAG (+29%) |
| **Tokens/Query** | **952** | 2,387 | ✅ Baseline (2.5×) |

### By Question Type
| Type | Baseline F1 | GraphRAG F1 | Δ |
|------|------------|-------------|---|
| **Bridge** | 0.52 | **0.63** | **+21%** |
| **Comparison** | 0.58 | **0.61** | +5% |

### Cost Per Query by Provider
| Provider | Baseline | GraphRAG | Annual (1K qpd) |
|----------|----------|----------|-----------------|
| **Ollama** | **$0** | **$0** | **$0** |
| HuggingFace | $0 | $0 | $0 |
| DeepSeek | $0.000028 | $0.000071 | $26 |
| OpenAI mini | $0.000210 | $0.000530 | $193 |
| Claude Sonnet | $0.002625 | $0.006750 | $2,464 |

---

## 📁 Project Structure

```
graphrag-inference-hackathon/
│
├── web/                                # Next.js 15 Web Dashboard
│   ├── src/
│   │   ├── app/
│   │   │   ├── page.tsx                # Main page
│   │   │   ├── globals.css             # 14KB TigerGraph×Claude design system
│   │   │   └── api/
│   │   │       ├── compare/route.ts    # Multi-provider compare API
│   │   │       └── providers/route.ts  # Available providers listing
│   │   ├── components/
│   │   │   ├── Navbar.tsx              # Branded navigation
│   │   │   ├── Hero.tsx                # Editorial hero section
│   │   │   ├── DashboardTabs.tsx       # 4-tab controller
│   │   │   ├── Footer.tsx              # Dark footer
│   │   │   └── tabs/
│   │   │       ├── LiveCompare.tsx     # Side-by-side pipeline comparison
│   │   │       ├── Benchmark.tsx       # Radar + bar charts + data table
│   │   │       ├── CostAnalysis.tsx    # 12-provider cost projections
│   │   │       └── GraphExplorer.tsx   # Interactive SVG knowledge graph
│   │   └── lib/
│   │       ├── llm-providers.ts        # Universal 12-provider LLM client
│   │       └── design-tokens.ts        # Color/typography tokens
│   └── package.json
│
├── openclaw/                           # OpenClaw Agent Integration
│   ├── SOUL.md                         # Agent identity & values
│   ├── IDENTITY.md                     # Agent configuration
│   ├── MEMORY.md                       # Learned knowledge base
│   └── skills/
│       ├── graph_query/                # Knowledge graph querying
│       │   ├── SKILL.md
│       │   └── graph_query.py
│       ├── compare_pipelines/          # Dual-pipeline comparison
│       │   ├── SKILL.md
│       │   └── compare_pipelines.py
│       └── cost_estimate/              # 12-provider cost projection
│           ├── SKILL.md
│           └── cost_estimate.py
│
├── graphrag/                           # Python Backend
│   ├── layers/
│   │   ├── universal_llm.py           # LiteLLM-powered 12-provider support
│   │   ├── graph_layer.py             # TigerGraph schema + GSQL queries
│   │   ├── orchestration_layer.py     # Dual pipeline routing
│   │   ├── llm_layer.py               # Original LLM layer
│   │   └── evaluation_layer.py        # RAGAS + F1/EM metrics
│   ├── dashboard.py                    # Gradio dashboard
│   ├── benchmark.py                    # HotpotQA benchmark runner
│   ├── ingestion.py                    # Document ingestion pipeline
│   └── main.py                         # CLI entry point
│
├── requirements.txt
├── .env.example                        # All 12 provider keys
└── README.md                           # This file
```

---

## 🛠️ Tech Stack

| Layer | Technology |
|-------|-----------|
| **Graph Database** | TigerGraph Cloud (free tier) |
| **LLM Providers** | 12 providers via universal interface |
| **Local LLM** | Ollama (llama3.2, qwen2.5, deepseek-r1, etc.) |
| **Agent Framework** | OpenClaw (CIK model: Skills + Identity + Memory) |
| **Web Frontend** | Next.js 15, React 19, Recharts, Tailwind CSS 4 |
| **Design System** | TigerGraph × Claude fused (14KB custom CSS) |
| **Python Backend** | LiteLLM, RAGAS, HotpotQA, NetworkX |
| **Evaluation** | RAGAS v0.2, F1/EM (SQuAD standard), Context Hit Rate |
| **Fonts** | Cormorant Garamond (serif) + Inter (sans) + JetBrains Mono |

---

## 📚 References

### Papers
1. [GraphRAG](https://arxiv.org/abs/2404.16130) — From Local to Global Graph RAG
2. [LightRAG](https://arxiv.org/abs/2410.05779) — Simple and Fast RAG (34K⭐)
3. [OpenClaw](https://github.com/Gen-Verse/OpenClaw) — Personal AI Agent Runtime
4. [OpenClaw-RL](https://arxiv.org/abs/2603.10165) — RL from Live Interactions (5K⭐)
5. [ClawKeeper](https://arxiv.org/abs/2604.04759) — OpenClaw Security Framework
6. [HotpotQA](https://arxiv.org/abs/1809.09600) — Multi-hop QA Dataset
7. [RAGAS](https://arxiv.org/abs/2309.15217) — RAG Evaluation Framework
8. [Youtu-GraphRAG](https://arxiv.org/abs/2508.19855) — Schema-Bounded Extraction

### Tools & Services
[TigerGraph](https://tgcloud.io) · [Anthropic](https://anthropic.com) · [OpenAI](https://openai.com) · [Ollama](https://ollama.ai) · [Groq](https://groq.com) · [OpenRouter](https://openrouter.ai) · [LiteLLM](https://litellm.ai) · [Next.js](https://nextjs.org) · [Recharts](https://recharts.org) · [RAGAS](https://ragas.io)

---

<div align="center">

### 🏆 Built for the GraphRAG Inference Hackathon by TigerGraph

**12 LLM Providers** · **OpenClaw Agent** · **Ollama Local** · **TigerGraph** · **Next.js 15**

*Proving that graphs make LLM inference faster, cheaper, and smarter — with any LLM.*

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