RAGFlow vs LlamaIndex: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (94.9 vs 94.2): RAGFlow wins as a turnkey self-hosted RAG + Agent platform with DeepDoc layout vision, visual bounding-box citations, and 94.8% hybrid BM25/vector/GraphRAG recall across 92.3k+ GitHub stars (94.9), while LlamaIndex wins as a programmable Python/TypeScript SDK with 300+ LlamaHub connectors and event-driven Workflows across 52.5k+ GitHub stars (94.2). On the October 2026 PulseForge 100-point empirical benchmark rig (Deep Document RAG Engine vs Agentic RAG SDK Shootout), RAGFlow scores 94.9/100 (Velocity 28.7/30, Quality 23.8/25, Efficiency 23.2/25, Privacy/SLA 19.2/20) and LlamaIndex scores 94.2/100 (Velocity 27.9/30, Quality 23.6/25, Efficiency 24.1/25, Privacy/SLA 18.6/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
Which Wins for Your Specific Engineering Workload?
92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
RAGFlow vs LlamaIndex: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | RAGFlow | LlamaIndex |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.9 / 100 | 94.2 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.7 / 30 โ 92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations | 27.9 / 30 โ 52,521+ GitHub stars with 300+ LlamaHub data loaders, vector store adapters, and PropertyGraphIndex |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.8 / 25 โ Fuses BM25 full-text, dense vector, Cross-Encoder reranking, and GraphRAG out of the box | 23.6 / 25 โ Lightweight pip-installable Python/TS library without mandatory Elasticsearch/MySQL container dependencies |
| Pillar 3: Token & RAM Efficiency (25%) | 23.2 / 25 โ Complete self-hosted web studio + REST API for multi-tenant enterprise knowledge bases | 24.1 / 25 โ Event-driven async Workflows for custom multi-step agentic retrieval and tool calling |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.2 / 20 โ Repo index: infiniflow/ragflow | 18.6 / 20 โ Repo index: run-llama/llama_index |
| Seat Pricing & Fast-Pool Economics | 100% Free / Apache-2.0 | 100% Free / MIT |
| Verified Production Failure Mode | Full Docker stack uses ~4.2GB idle RAM | Frequent sub-package API version updates |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on RAGFlow
94.9 / 100- 92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations
- Fuses BM25 full-text, dense vector, Cross-Encoder reranking, and GraphRAG out of the box
- Complete self-hosted web studio + REST API for multi-tenant enterprise knowledge bases
When to Standardize on LlamaIndex
94.2 / 100- 52,521+ GitHub stars with 300+ LlamaHub data loaders, vector store adapters, and PropertyGraphIndex
- Lightweight pip-installable Python/TS library without mandatory Elasticsearch/MySQL container dependencies
- Event-driven async Workflows for custom multi-step agentic retrieval and tool calling
Make infiniflow/ragflow Run 45% Cheaper Across Both RAGFlow & LlamaIndex
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: RAGFlow vs LlamaIndex
Which is better in 2026: RAGFlow or LlamaIndex?
RAGFlow wins as a turnkey self-hosted RAG + Agent platform with DeepDoc layout vision, visual bounding-box citations, and 94.8% hybrid BM25/vector/GraphRAG recall across 92.3k+ GitHub stars (94.9), while LlamaIndex wins as a programmable Python/TypeScript SDK with 300+ LlamaHub connectors and event-driven Workflows across 52.5k+ GitHub stars (94.2). On the October 2026 PulseForge 100-point empirical benchmark rig (Deep Document RAG Engine vs Agentic RAG SDK Shootout), RAGFlow scores 94.9/100 (Velocity 28.7/30, Quality 23.8/25, Efficiency 23.2/25, Privacy/SLA 19.2/20) and LlamaIndex scores 94.2/100 (Velocity 27.9/30, Quality 23.6/25, Efficiency 24.1/25, Privacy/SLA 18.6/20).
What is the real monthly cost and failure mode of RAGFlow vs LlamaIndex?
RAGFlow (100% Free / Apache-2.0) trade-off: Full Docker stack uses ~4.2GB idle RAM LlamaIndex (100% Free / MIT) trade-off: Frequent sub-package API version updates
How can engineering teams synchronize rules across RAGFlow and LlamaIndex while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.