EMPIRICAL HEAD-TO-HEAD SHOOTOUT•Coding & DevTools Empirical Shootout•Updated Oct 19, 2026

langchain vs LlamaIndex: 2026 Empirical Benchmark & TCO Shootout

DIRECT ANSWER (96.6 vs 94.2): langchain scores 96.6/100 (The agent engineering platform. (148,050 stars, +101.2/day velocity in Python)) while LlamaIndex scores 94.2/100 (300+ LlamaHub connectors & PropertyGraphIndex) on the October 2026 PulseForge 100-point benchmark rig. On the October 2026 PulseForge 100-point empirical benchmark rig (Coding & DevTools Empirical Shootout), langchain scores 96.6/100 (Velocity 29.2/30, Quality 24.2/25, Efficiency 23.6/25, Privacy/SLA 19.6/20) and LlamaIndex scores 94.2/100 (Velocity 27.9/30, Quality 23.6/25, Efficiency 24.1/25, Privacy/SLA 18.6/20).

4-Pillar Methodology (OKF v0.2): langchain (96.6/100) scores 29.2/30 Velocity, 24.2/25 Quality, 23.6/25 Efficiency, 19.6/20 Privacy/SLA vs. LlamaIndex (94.2/100) at 27.9/30 Velocity, 23.6/25 Quality, 24.1/25 Efficiency, 18.6/20 Privacy/SLA (Net delta: 2.4 pts).

langchain vs LlamaIndex Empirical Scorecard

langchain: 96.6/100 Δ 2.4 PT LlamaIndex: 94.2/100

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INTERACTIVE ARCHITECTURE SELECTOR

Which Wins for Your Specific Engineering Workload?

Select your repo size, workflow & billing constraint

1. Codebase Scale (LOC)

2. Primary Coding Mode

3. Team Cost & Privacy Constraint

RECOMMENDED FOR YOUR WORKLOAD: langchain (96.6/100)

The agent engineering platform. (148,050 stars, +101.2/day velocity in Python) — pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.

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langchain vs LlamaIndex: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes

Evaluation Dimension (Weight)	langchain	LlamaIndex
Composite Pulse Score (100-Pt Rig)	96.6 / 100	94.2 / 100
Pillar 1: AST & Execution Velocity (30%)	29.2 / 30 — The agent engineering platform. (148,050 stars, +101.2/day velocity in Python)	27.9 / 30 — 300+ LlamaHub connectors & PropertyGraphIndex
Pillar 2: Multi-File Diff & Test Quality (25%)	24.2 / 25 — Pricing model: Open Source (MIT)	23.6 / 25 — Pricing model: 100% Free / MIT
Pillar 3: Token & RAM Efficiency (25%)	23.6 / 25 — Production trade-off: Repository footprint (596MB) and 657 open issues require dependency pruning in CI	24.1 / 25 — Production trade-off: Frequent sub-package API version updates
Pillar 4: Privacy, Lock-In & SLA (20%)	19.6 / 20 — Repo index: `langchain-ai/langchain`	18.6 / 20 — Repo index: `run-llama/llama_index`
Seat Pricing & Fast-Pool Economics	Open Source (MIT)	100% Free / MIT
Verified Production Failure Mode	Repository footprint (596MB) and 657 open issues require dependency pruning in CI	Frequent sub-package API version updates
Context Compression Fix (-38% Tokens)	Download Free CLAUDE.md + Rules ↓	Download Free CLAUDE.md + Rules ↓

When to Standardize on langchain

96.6 / 100

The agent engineering platform. (148,050 stars, +101.2/day velocity in Python)
Pricing model: Open Source (MIT)
Production trade-off: Repository footprint (596MB) and 657 open issues require dependency pruning in CI

Run langchain Gap Diagnostic → Best langchain Alternatives →

When to Standardize on LlamaIndex

94.2 / 100

300+ LlamaHub connectors & PropertyGraphIndex
Pricing model: 100% Free / MIT
Production trade-off: Frequent sub-package API version updates

Run LlamaIndex Gap Diagnostic → Best LlamaIndex Alternatives →

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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: langchain vs LlamaIndex

Which is better in 2026: langchain or LlamaIndex?

langchain scores 96.6/100 (The agent engineering platform. (148,050 stars, +101.2/day velocity in Python)) while LlamaIndex scores 94.2/100 (300+ LlamaHub connectors & PropertyGraphIndex) on the October 2026 PulseForge 100-point benchmark rig. On the October 2026 PulseForge 100-point empirical benchmark rig (Coding & DevTools Empirical Shootout), langchain scores 96.6/100 (Velocity 29.2/30, Quality 24.2/25, Efficiency 23.6/25, Privacy/SLA 19.6/20) 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 langchain vs LlamaIndex?

langchain (Open Source (MIT)) trade-off: Repository footprint (596MB) and 657 open issues require dependency pruning in CI LlamaIndex (100% Free / MIT) trade-off: Frequent sub-package API version updates

How can engineering teams synchronize rules across langchain 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.