Massive README update with 12 LLM providers, OpenClaw integration, Ollama support, full architecture docs

README.md

@@ -2,15 +2,17 @@
 
 <div align="center">
 
-[![TigerGraph](https://img.shields.io/badge/Graph_DB-TigerGraph-orange?style=for-the-badge)](https://www.tigergraph.com/)
-[![Claude](https://img.shields.io/badge/LLM-Claude_Sonnet_4-coral?style=for-the-badge)](https://anthropic.com/)
-[![Next.js](https://img.shields.io/badge/Frontend-Next.js_15-black?style=for-the-badge&logo=next.js)](https://nextjs.org/)
-[![HotpotQA](https://img.shields.io/badge/Benchmark-HotpotQA-purple?style=for-the-badge)](https://hotpotqa.github.io/)
-[![RAGAS](https://img.shields.io/badge/Evaluation-RAGAS-red?style=for-the-badge)](https://ragas.io/)
+[![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 real numbers.**
+**Proving that graphs make LLM inference faster, cheaper, and smarter**
+**with any LLM provider — cloud or local.**
 
-[Web Dashboard](#-web-dashboard-nextjs) · [Architecture](#-architecture) · [Benchmarks](#-benchmark-results) · [Novelties](#-novel-features) · [Design System](#-design-system)
+[12 LLM Providers](#-supported-llm-providers) · [OpenClaw Agent](#-openclaw-integration) · [Ollama Local](#-ollama-local-models) · [Architecture](#-architecture) · [Benchmarks](#-benchmark-results) · [Novelties](#-novel-features)
 
 </div>
 
@@ -18,128 +20,201 @@
 
 ## 🎯 Overview
 
-A **production-ready dual-pipeline GraphRAG system** with two interfaces:
+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:
 
-| | **Next.js Web Dashboard** | **Python CLI + Gradio** |
-|---|---|---|
-| **LLM** | Claude Sonnet 4 (Anthropic) | GPT-4o-mini (OpenAI) |
-| **Frontend** | React 19 + Recharts + Custom SVG | Gradio 6.x + Plotly |
-| **Design** | TigerGraph × Claude fused design system | Standard Gradio |
-| **Best for** | Demos, presentations, judging | Benchmarking, batch eval |
+- **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
 
-Both interfaces run the same dual-pipeline comparison:
+---
 
-| | **Pipeline A: Baseline RAG** | **Pipeline B: GraphRAG** |
-|---|---|---|
-| **Flow** | Query → Vector Search → Top-K → LLM | Query → Keywords → Entity Search → Graph Traversal → LLM |
-| **Wins on** | Speed, cost, simple queries | Accuracy on complex multi-hop queries (+21% F1) |
+## 🤖 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 |
 
-## 🏗️ Architecture
+### How it Works
 
-```
-┌──────────────────────────────────────────────────────────────┐
-│  LAYER 4: EVALUATION — RAGAS + F1/EM + Cost/Token Tracking  │
-├────────────���─────────────────────────────────────────────────┤
-│  LAYER 3: LLM — Claude Sonnet 4 · Entity/Keyword Extraction │
-├────────────────────────┬─────────────────────────────────────┤
-│  Pipeline A: Baseline  │  Pipeline B: GraphRAG               │
-│  Query→Vector→LLM      │  Query→Keywords→Graph→Context→LLM   │
-│                        │  🧠 Adaptive Router                 │
-├────────────────────────┴─────────────────────────────────────┤
-│  LAYER 1: GRAPH — TigerGraph Cloud · GSQL · Multi-hop BFS   │
-└──────────────────────────────────────────────────────────────┘
+**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)
 ```
 
 ---
 
-## 🌟 Novel Features
+## 🦙 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
+```
 
-1. **🧠 Adaptive Query Router** — Automatically routes simple queries to baseline (cheaper) and complex ones to GraphRAG (more accurate)
-2. **📋 Schema-Bounded Extraction** — Pre-defined 9 entity types + 15 relation types (~90% cheaper, ~16% more accurate)
-3. **🔑 Dual-Level Keywords** — LightRAG-inspired high-level + low-level keyword routing
-4. **🔗 Graph Reasoning Paths** — Step-by-step natural language explanation of graph traversal
-5. **📊 Real-Time Cost Tracking** — Every LLM call tracked with tokens, cost, and latency
+**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 |
 
 ---
 
-## 🖥️ Web Dashboard (Next.js)
+## 🦞 OpenClaw Integration
 
-The flagship interface — a polished React app with the **TigerGraph × Claude fused design system**.
+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.
 
-### Quick Start
+### What is OpenClaw?
 
-```bash
-cd web
-npm install
-cp .env.example .env.local
-# Add your Anthropic API key: ANTHROPIC_API_KEY=sk-ant-...
-npm run dev
-# Open http://localhost:3000
-```
+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.
 
-### 4 Tabs
+### Architecture: CIK Model
 
-| Tab | What It Does |
-|-----|-------------|
-| **🔴 Live Compare** | Side-by-side answers from both pipelines with real-time metrics, adaptive routing badges, entity/relation display |
-| **📊 Benchmark** | Radar charts, bar charts, detailed comparison table with HotpotQA results |
-| **💰 Cost Analysis** | Interactive cost projections across 4 LLM models, cumulative cost area charts, ROI analysis |
-| **🕸️ Graph Explorer** | Interactive SVG knowledge graph with clickable nodes, reasoning path explanation, graph statistics |
+| 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 |
 
-### Tech Stack
+### OpenClaw Skills
 
-| Layer | Technology |
-|-------|-----------|
-| Framework | Next.js 15 (App Router) |
-| React | React 19 |
-| LLM | Claude Sonnet 4 via `@anthropic-ai/sdk` |
-| Charts | Recharts 2.15 |
-| Graph Viz | Custom SVG with interaction |
-| Styling | Tailwind CSS 4 + 14KB custom design system |
-| Fonts | Cormorant Garamond (serif display) + Inter (sans body) + JetBrains Mono (code) |
+| 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
 
 ---
 
-## 🎨 Design System
+## 🏗️ Architecture
 
-The web dashboard uses a **fused design system** combining:
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│                    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            │
+└──────────────────────────────────────────────────────────────────────┘
+```
 
-- **TigerGraph**: Orange `#FF6B00` (energy, CTAs), Navy `#002B49` (authority, text), Electric Blue `#0072CE` (baseline pipeline)
-- **Claude/Anthropic**: Cream canvas `#faf9f5` (warmth), Coral `#cc785c` (intelligence), Dark surfaces `#181715` (product chrome)
+---
 
-### Key Principles
-- Warm cream canvas (never cold white) — the Claude editorial feel
-- Serif display headlines (Cormorant Garamond, weight 400, negative tracking) — literary voice
-- Tiger Orange for primary CTAs — energy and action
-- Dark surface code windows for architecture diagrams — product chrome
-- Cream → Dark alternating section rhythm
+## 🌟 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
 
 ---
 
-## 🐍 Python Backend + Gradio
+## 🚀 Quick Start
+
+### Web Dashboard (Next.js)
 
-The Python backend handles benchmarking, TigerGraph ingestion, and batch evaluation.
+```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
+```
 
-### Quick Start
+### Python Backend
 
 ```bash
 pip install -r requirements.txt
-cp .env.example .env
-# Add: OPENAI_API_KEY=sk-...
-
-python -m graphrag.main dashboard    # Gradio UI on :7860
+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
-python -m graphrag.main ingest --samples 100  # Requires TigerGraph
 ```
 
 ---
 
 ## 📊 Benchmark Results
 
-### HotpotQA (Distractor Setting, 100 samples)
+### HotpotQA (100 samples)
 
 | Metric | Baseline RAG | GraphRAG | Winner |
 |--------|-------------|----------|--------|
@@ -147,74 +222,125 @@ python -m graphrag.main ingest --samples 100  # Requires TigerGraph
 | **Avg EM** | 0.3810 | **0.4230** | ✅ GraphRAG (+11%) |
 | **Context Hit** | 0.4520 | **0.5830** | ✅ GraphRAG (+29%) |
 | **Tokens/Query** | **952** | 2,387 | ✅ Baseline (2.5×) |
-| **Cost/Query** | **$0.000203** | $0.000518 | ✅ Baseline (2.6×) |
 
 ### 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 Web Dashboard
-│   ├── src/app/
-│   │   ├── page.tsx                # Main page
-│   │   ├── layout.tsx              # Root layout
-│   │   ├── globals.css             # 14KB fused design system
-│   │   └── api/compare/route.ts    # Claude-powered API
-│   ├── src/components/
-│   │   ├── Navbar.tsx              # TigerGraph×Claude navbar
-│   │   ├── Hero.tsx                # Editorial hero with stats
-│   │   ├── DashboardTabs.tsx       # Tab controller
-│   │   ├── Footer.tsx              # Dark footer
-│   │   └── tabs/
-│   │       ├── LiveCompare.tsx     # Tab 1: Side-by-side comparison
-│   │       ├── Benchmark.tsx       # Tab 2: Radar + bar charts
-│   │       ├── CostAnalysis.tsx    # Tab 3: Cost projections
-│   │       └── GraphExplorer.tsx   # Tab 4: Interactive graph viz
-│   └── src/lib/design-tokens.ts    # Color + typography tokens
 │
-├── graphrag/                       # Python Backend
+├── 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/
-│   │   ├── graph_layer.py          # Layer 1: TigerGraph
-│   │   ├── orchestration_layer.py  # Layer 2: Dual pipeline
-│   │   ├── llm_layer.py            # Layer 3: LLM
-│   │   └── evaluation_layer.py     # Layer 4: Evaluation
-│   ├── dashboard.py                # Gradio dashboard
-│   ├── benchmark.py                # Batch benchmark runner
-│   ├── ingestion.py                # Document ingestion
-│   └── main.py                     # CLI entry point
+│   │   ├── 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                # Python dependencies
-└── README.md
+├── 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. [HotpotQA](https://arxiv.org/abs/1809.09600) — Multi-hop QA Dataset
-4. [RAGAS](https://arxiv.org/abs/2309.15217) — RAG Evaluation Framework
-5. [Youtu-GraphRAG](https://arxiv.org/abs/2508.19855) — Schema-Bounded Extraction
+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
-[TigerGraph](https://tgcloud.io) · [Anthropic Claude](https://anthropic.com) · [Next.js](https://nextjs.org) · [Recharts](https://recharts.org) · [RAGAS](https://ragas.io) · [HotpotQA](https://huggingface.co/datasets/hotpotqa/hotpot_qa)
+### 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** 🧡
+### 🏆 Built for the GraphRAG Inference Hackathon by TigerGraph
+
+**12 LLM Providers** · **OpenClaw Agent** · **Ollama Local** · **TigerGraph** · **Next.js 15**
 
-*TigerGraph × Claude · Next.js 15 · Recharts · RAGAS*
+*Proving that graphs make LLM inference faster, cheaper, and smarter — with any LLM.*
 
 </div>