# LangChain vs LlamaIndex in 2026: Agents or Data?

> The classic framework confusion resolved — LangChain's agent loop and ecosystem vs LlamaIndex's data-and-documents depth — and when you'd genuinely use both.

They're complements that compete at the edges. LangChain 1.0 narrowed to the agent loop — create_agent on the LangGraph runtime, middleware, the biggest integration ecosystem. LlamaIndex stayed data-first — ingestion, indexing, query engines, document agents. Agent-shaped problems lean LangChain; document-shaped problems lean LlamaIndex; plenty of stacks use both.

"LangChain vs LlamaIndex" endures because both touch LLM apps everywhere — but it's mostly a **category error**: one framework's center is *orchestrating agents*, the other's is *getting your data to a model well*. Sharpen that and the decision usually makes itself.

## The short answer

- **Agent-shaped problem** — tools, multi-step orchestration, provider-agnostic loops → **[LangChain](/tools/langchain)** (with [LangGraph](/tools/langgraph) underneath when control matters).
- **Data/document-shaped problem** — ingestion, indexing, retrieval quality, messy PDFs → **[LlamaIndex](/tools/llamaindex)**.
- **Both shapes in one system** → use both; the seam (a query engine as an agent tool) is well-trodden.

## What each became by 2026

**LangChain** answered its bloat discourse with the 1.0 reset (October 2025): the sprawling chain zoo moved to `langchain-classic`, leaving a focused core — `create_agent`, a production tool-calling loop on the LangGraph runtime, with middleware (human-in-the-loop approval, summarization, PII redaction) and normalized content blocks across providers. Its durable advantages: the largest integration ecosystem in AI, true provider-agnosticism, and the graduated stack (LangChain → LangGraph → LangSmith).

**LlamaIndex** stayed loyal to its founding question — *how does my data reach the model well?* — with connectors, chunking and node parsing, index types, and query engines that compose retrieval with synthesis, plus document agents and event-driven Workflows. Its company, meanwhile, found the commercial center in **documents**: LlamaParse's agentic OCR (complex tables, layouts, handwriting) and LlamaCloud's managed parse/extract/index pipelines. The framework remains MIT and active (deliberately 0.x — pin versions); the headline product is document intelligence.

## Dimension by dimension

| | LangChain | LlamaIndex |
| --- | --- | --- |
| Center of gravity | Agent orchestration | Data → model pipeline |
| Signature API | create_agent + middleware | Indexes + query engines |
| RAG | Capable, assembled | Native depth |
| Agents | Core identity (LangGraph runtime) | Credible, document-focused |
| Document parsing | Via integrations | LlamaParse (the specialist) |
| Commercial layer | LangSmith (observability) | LlamaCloud (documents) |
| Languages | Python + JS | Python (+ TS sibling) |

## How to actually choose

Name the hard problem. If your sleepless nights are *orchestration* — tool reliability, human gates, durable runs — that's LangChain's lane, and [the agent-framework field guide](/guides/concepts/agent-frameworks-2026) covers its rivals. If they're *data* — parsing hostile PDFs, chunking, retrieval quality — that's LlamaIndex's lane, and [How RAG Actually Works](/guides/concepts/how-rag-works) is the map it implements. If you genuinely have both, compose: LlamaIndex query engine as a tool inside a LangChain agent is boring, supported, and correct — the best kind of architecture decision.

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_Source: https://agentscamp.com/guides/comparisons/langchain-vs-llamaindex — Guide on AgentsCamp._