[A] RAGFlow (94.9)
VS
[B] LlamaIndex (94.2)
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).
1. Velocity (30)
28.6 vs 27.1
2. Quality (25)
23.9 vs 23.8
3. Efficiency (25)
23.3 vs 24.4
4. Privacy/SLA (20)
19.1 vs 18.9
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
RAGFlow94.9
infiniflow/ragflow
100% Free / Apache-2.0
LlamaIndex94.2
run-llama/llama_index
100% Free / MIT
Velocity Advantage:RAGFlow (+1.5 pts)
Token/RAM Efficiency:LlamaIndex (+1.1 pts)
RAGFlow vs LlamaIndex: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
RAGFlowLlamaIndex
| Evaluation Dimension (Weight) | [A] RAGFlow | [B] LlamaIndex |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.9 / 100 | 94.2 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.6 / 30 92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations | 27.1 / 30 52,521+ GitHub stars with 300+ LlamaHub data loaders, vector store adapters, and PropertyGraphIndex |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.9 / 25 Fuses BM25 full-text, dense vector, Cross-Encoder reranking, and GraphRAG out of the box | 23.8 / 25 Lightweight pip-installable Python/TS library without mandatory Elasticsearch/MySQL container dependencies |
| Pillar 3: Token & RAM Efficiency (25%) | 23.3 / 25 100% Free / Apache-2.0 • Complete self-hosted web studio + REST API for multi-tenant enterprise knowledge bases | 24.4 / 25 100% Free / MIT • Event-driven async Workflows for custom multi-step agentic retrieval and tool calling |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.1 / 20 Zero-retention configurable; upstream index: infiniflow/ragflow | 18.9 / 20 Open/BYOK telemetry control; upstream index: run-llama/llama_index |
| Seat Pricing & Fast-Pool Economics | 100% Free / Apache-2.0 | 100% Free / MIT |
| Verified Production Trade-Off | Full Docker stack uses ~4.2GB idle RAM | Frequent sub-package API version updates |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + .cursorrules ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity: RAGFlow (28.6/30)
•
Efficiency: LlamaIndex (24.4/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: RAGFlow (94.9/100) (Saves ~$312/mo for 5 Devs with Root CLAUDE.md)
Velocity Winner (28.6/30): 92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations
Specimen #1 Unmasked Preview (`CLAUDE.md` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ SWE-bench (500 tasks) + 50-Probe OWASP Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (RAGFlow (94.9/100) • infiniflow/ragflow • 5 Devs): + [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat) + [Verdict TCO] infiniflow/ragflow | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
.cursor/rules/architecture.mdc
litellm_router.yaml (-45% API Spend)
.github/workflows/ai-ci-gate.yml
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: RAGFlow vs LlamaIndex by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Spend: ~$20 to $38/dev/mo (Seat + Fast Pool)
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: RAGFlow (94.9/100)
92,323+ GitHub stars with built-in DeepDoc OCR & table structure recognition plus visual page citations - pair with a production CLAUDE.md + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.
+ [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat)
HEAD-TO-HEAD MODEL & CODING-AGENT ROUTER CALCULATOR
65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M TokensCompare Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 Cost, P50 Latency & SWE-Bench + 1-Click Fallback Router Preview
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
SPECIMEN #1 UNMASKED PRODUCTION FAILOVER CONFIG (litellm_fallback_router.yaml)
+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing RAGFlow and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for RAGFlow, the case for LlamaIndex, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick 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 Pick 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
When to Skip Both (BYOK OSS)
Aider / Cline- $0/mo Seat Fee: Pay pure upstream Anthropic/DeepSeek/OpenAI API rates or run local Ollama models for $0.
- Zero IDE Lock-In: Keep standard VS Code, Neovim, or JetBrains with deterministic git commits.
- 45% Lower API Burn: Route simple edits to Haiku/DeepSeek and hard diffs to Sonnet/Opus via LiteLLM.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION
Wire the winner into your own repository.
Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.
PRODUCTION .CURSORRULES, CLAUDE.MD & COST-ROUTER PACK
Scan Your Repo →
Wire RAGFlow or LlamaIndex Into Your Repository & Cut Infra / API Spend by 45%
Export your repository's production CLAUDE.md, .cursor/rules/architecture.mdc, and CLAUDE.md in one click.
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • Root CLAUDE.md
Email yourself a starter CLAUDE.md context map to prune node_modules and build bloat.
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs
Complete Pack: scoped .cursor/rules/*.mdc, CLAUDE.md, LiteLLM 45% Router & CI Gate.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾
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 28.6/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.1/20) and LlamaIndex scores 94.2/100 (Velocity 27.1/30, Quality 23.8/25, Efficiency 24.4/25, Privacy/SLA 18.9/20).
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://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.
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