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.
Key takeaways
- Ask what your hard problem is: orchestrating an agent (LangChain) or getting messy data to a model well (LlamaIndex). The frameworks' centers of gravity never really overlapped.
- LangChain 1.0 (Oct 2025) is a different product than its reputation: legacy chains exiled to langchain-classic, the core now create_agent + middleware on the LangGraph runtime.
- LlamaIndex's depth is the data path — connectors, chunking, indexes, query engines — and its 2026 commercial story is documents: LlamaParse OCR and LlamaCloud pipelines.
- Both do both (LangChain has retrievers; LlamaIndex has agents and workflows) — the edges blur; the depth doesn't.
- Using both is normal, not failure: LlamaIndex as the retrieval/query engine inside a LangChain/LangGraph agent is a standard production pattern.
"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 (with LangGraph underneath when control matters).
- Data/document-shaped problem — ingestion, indexing, retrieval quality, messy PDFs → 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 covers its rivals. If they're data — parsing hostile PDFs, chunking, retrieval quality — that's LlamaIndex's lane, and How RAG Actually 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.
Frequently asked questions
- LangChain or LlamaIndex for RAG?
- LlamaIndex gets you a quality pipeline fastest — its abstractions (readers, node parsers, indexes, query engines) map directly onto RAG's stages, and document handling is its specialty. LangChain can absolutely build RAG, but you assemble more yourself. If RAG is the product, start LlamaIndex; if RAG is one tool inside a larger agent, either works and your agent framework choice should lead.
- LangChain or LlamaIndex for agents?
- LangChain — post-1.0 it IS an agent framework first: create_agent, middleware (human-in-the-loop, redaction), and the LangGraph runtime underneath for durable, controllable execution. LlamaIndex's agents and Workflows are credible, especially document agents over your own corpus, but orchestration depth and ecosystem favor LangChain.
- Can I use them together?
- Yes, and it's a standard pattern: LlamaIndex owns the data layer (ingestion, indexing, a query engine) exposed as a tool; LangChain/LangGraph owns the agent calling it. The integration is officially supported in both directions — composition beats forcing either framework outside its depth.
Related
- LangchainThe provider-agnostic agent framework, post-1.0: a standard create_agent loop on the LangGraph runtime, middleware hooks, and the largest integration ecosystem.
- LlamaindexThe data framework for LLM apps — ingestion, indexing, query engines, and document agents — now centered on document processing with LlamaParse and LlamaCloud.
- LangGraphA low-level library for building stateful, controllable agents as graphs, with checkpointing and human-in-the-loop.
- Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDKA decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.
- How RAG Actually Works: Ingestion, Chunking, Retrieval & RerankingA clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
- LangGraph vs CrewAI: Agent Frameworks Compared (2026)LangGraph vs CrewAI — explicit state-machine control vs role-based crew abstractions. Which agent framework fits your reliability bar and team.
- GraphRAG Explained: When Knowledge Graphs Beat Vector SearchWhat GraphRAG is, how graph-based retrieval differs from vector RAG, the query shapes where it wins, and the honest costs before you build one.