LangChain vs LlamaIndex (2026): Which Framework for RAG and LLM Apps?

If retrieval over your own documents is the core of what you’re building, LlamaIndex is the more direct fit — it’s a data/RAG-first framework where indexing, querying, and document agents are the main event. If you’re orchestrating complex, multi-step, stateful agent workflows that happen to include retrieval, LangChain (and its agent runtime, LangGraph) gives you more general-purpose machinery. Both are MIT-licensed, both are Python-first with TypeScript ports, and both can build a perfectly good RAG pipeline — so the choice is about which shape of problem you’re solving, not about which one “does RAG.”

This page compares them through the lens that matters on Aquila: building private, self-hosted RAG you actually own. For the broader picture of assembling that stack, see our self-hosted RAG complete guide.

Side-by-side comparison

	LangChain	LlamaIndex
Repository	langchain-ai/langchain	run-llama/llama_index
License	MIT	MIT
Primary language	Python (+ LangChain.js for TS)	Python (+ TypeScript version)
GitHub stars (June 2026)	~140k	~50k
Core positioning	General LLM/agent orchestration	Data-framework / RAG-first
Agent layer	LangGraph (stateful, durable agents)	Workflows / Agent Workflows (event-driven)
Retrieval focus	One integration among many	The central abstraction
Document parsing	Via integrations	First-party (LlamaParse / LlamaCloud)
Observability/eval	LangSmith (first-party)	Workflow debugger + integrations
Learning curve	Steeper (broad surface area)	Gentler for pure RAG

Star counts are GitHub’s rounded figures as of June 2026 (LangChain ~140k, LlamaIndex ~50k) and drift over time; the license and language facts are the stable ones to weight. Both projects ship a separate first-party TypeScript implementation if your app isn’t in Python.

Philosophy: orchestration vs data

The cleanest way to understand the difference is the problem each was born to solve.

LangChain started as a way to chain LLM calls and components together — prompts, models, tools, memory, retrievers — into pipelines, and has since repositioned around agents. Its own framing in 2026 is an “agent engineering platform”: LangChain 1.0 (GA October 2025) centres on a create_agent abstraction running on the LangGraph runtime. Retrieval is one capability among dozens of integrations; the framework’s centre of gravity is general orchestration of multi-step, tool-using, stateful workflows.

LlamaIndex started as a data framework — the original name was GPT Index — built specifically to connect your data to an LLM. Its centre of gravity is RAG: load documents, index them, retrieve the relevant chunks, synthesize an answer. In 2026 it leans hard into document intelligence, describing itself around document agents and OCR (via LlamaParse and LlamaCloud). When the task is “answer questions over my corpus,” LlamaIndex’s defaults point you straight at it.

Neither is locked into its origin — LangChain can do clean RAG, LlamaIndex has agents — but the defaults, the documentation, and the path of least resistance reflect these roots. Choosing well mostly means matching the framework’s grain to your problem.

Retrieval and indexing

This is the dimension where LlamaIndex’s RAG-first DNA shows most clearly.

LlamaIndex treats the index as a first-class object. VectorStoreIndex builds embeddings over your nodes and exposes top-k semantic retrieval; querying flows through a retrieval → postprocessing → response-synthesis pipeline you can tune at each stage. It ships a rich set of indexing strategies (vector, summary, tree, knowledge-graph), node parsers and chunkers, and first-party document parsing through LlamaParse — useful when your sources are messy PDFs, tables, or scanned documents rather than clean text. If your retrieval quality hinges on parsing and chunking real-world documents well, that first-party parsing layer is a genuine advantage.

LangChain provides retrievers, vector-store integrations, and document loaders too — and a great many of them, since breadth of integrations is part of its pitch. But retrieval is one module in a large library rather than the organizing principle. You assemble a RAG chain from components; it works well, but you’re doing more of the wiring yourself, and parsing/chunking is typically handled by separate integrations rather than a first-party product.

Both connect to the same vector databases you’d self-host — Qdrant, Chroma, pgvector, Milvus, Weaviate. The framework doesn’t lock your data layer; see best self-hosted vector databases for choosing one. And whichever framework you use, retrieval quality is dominated by your embedding model, chunking, and reranking — not the framework itself. Our reranking guide and best local embedding models cover the parts that actually move accuracy.

Agents

Both frameworks have invested heavily in agents, and this is where LangChain’s general-orchestration heritage pays off.

LangChain’s agent story runs on LangGraph, a lower-level framework for building stateful, durable agents — with persistence, human-in-the-loop checkpoints, and memory built in. The 2026 create_agent abstraction sits on top of it, with a middleware system for the agent loop (summarization, PII redaction, human approval). If you’re building something that loops, branches, calls many tools, pauses for human input, and needs to survive restarts, LangGraph is purpose-built for that graph-of-state model, and it’s the more mature option for complex agent control flow.

LlamaIndex offers agents through event-driven Workflows (and “Agent Workflows”), which orchestrate steps, tool calls, and LLM calls — increasingly with MCP server integration, filesystem tools, and persistent memory in 2026. These are well-suited to document-centric agents: read, extract, reason over your corpus, act. For the “agent that works over my data” shape, LlamaIndex Workflows are clean; for arbitrary, sprawling, multi-tool control flow, LangGraph is the deeper toolkit.

Rule of thumb: if the agent’s job is mostly retrieving and reasoning over your documents, LlamaIndex’s workflows fit naturally. If the agent’s job is orchestrating many tools and long-running stateful processes, LangGraph is built for exactly that.

Ecosystem

Both have grown well beyond a single library.

• LangChain ships LangGraph (agent runtime), LangSmith (first-party observability, evaluation, and deployment), and historically LangServe for serving chains as APIs (its deployment role has largely folded into the LangSmith/LangGraph platform). LangChain’s integration count is its headline strength — it connects to an enormous range of models, vector stores, and tools.
• LlamaIndex ships LlamaParse (document parsing/OCR), LlamaCloud (a managed umbrella over parsing, extraction, and indexing, including an EU residency option), LlamaExtract (schema-based extraction), and LlamaHub (community connectors). The ecosystem is oriented around getting messy real-world documents into a usable, queryable state.

For self-hosters, one nuance matters: the frameworks are open-source and run entirely on your infrastructure, but some of the managed cloud add-ons (LangSmith, LlamaCloud, LlamaParse’s hosted tiers) are paid SaaS. You can build and run private RAG with the open-source core of either and never touch the cloud products — LlamaIndex even ships a local open-source parser (LiteParse) — but if you adopt the hosted observability or parsing services, you’ve reintroduced a third party. Keeping the stack fully self-hosted means leaning on the open-source pieces and your own tooling.

Learning curve

LlamaIndex is generally faster to a working RAG pipeline. Because retrieval is the central abstraction, the “load → index → query” path is short and the defaults are sensible; a handful of lines gets you a working question-answering system over a folder of documents. If RAG is your whole project, you’ll be productive sooner.

LangChain has a larger surface area and a faster-moving API, which historically gave it a reputation for churn and abstraction overhead. The 1.0 release (and the consolidation around LangGraph) has made the core more stable and coherent, but there’s still simply more to learn because the framework spans far more than retrieval. The payoff is that once you’ve learned it, you can build a much wider range of LLM applications without reaching for a second framework.

If you’re new to building LLM apps and your first project is “chat with my documents,” start with LlamaIndex. If you already know you’re heading toward complex agents and tool orchestration, invest in LangChain/LangGraph up front.

When to pick which (and when to use neither)

Pick LlamaIndex if:

• Retrieval over your own data is the core of the product, not a side feature.
• You have messy, real-world documents (PDFs, tables, scans) and want strong first-party parsing.
• You want the shortest path from “folder of documents” to “working RAG.”
• Your agents are document-centric: read, extract, reason over a corpus.

Pick LangChain (with LangGraph) if:

• You’re orchestrating complex, multi-step, stateful agents with many tools.
• You need durable execution, human-in-the-loop, and rich agent control flow.
• You want one framework spanning far more than RAG.
• Breadth of integrations across models and tools is a priority.

Consider neither if:

• Your RAG pipeline is genuinely simple — embed, store, retrieve top-k, stuff into a prompt. At that point a framework can be more abstraction than you need; calling your vector DB and LLM directly (with a few helper functions) is often clearer, easier to debug, and avoids dependency churn. Plenty of solid production RAG runs on a thin layer of your own code over a self-hosted vector database and a local LLM served by Ollama or vLLM. Reach for a framework when the orchestration genuinely earns its complexity.

The honest answer for many teams is that the frameworks aren’t mutually exclusive: it’s common to use LlamaIndex for the data/retrieval layer and LangGraph for agent orchestration on top, picking the best tool for each job.

Verdict

For RAG over your own data, LlamaIndex is the more direct, lower-friction choice — it’s built for exactly that, with first-class indexing and strong document parsing. For complex agent orchestration, LangChain with LangGraph is the deeper, more general toolkit — built for stateful, multi-tool, durable workflows where retrieval is just one capability. Both are MIT-licensed, run fully on your own infrastructure, and are safe to build on; the decision is about the shape of your problem. And don’t overlook the third option: if your pipeline is simple, the leanest, most controllable thing is often a little of your own code over a self-hosted vector DB and a local model — which is very much the Aquila way of doing things.

FAQ

Is LlamaIndex better than LangChain for RAG? For pure RAG over your own documents, LlamaIndex is usually the more direct fit — retrieval and indexing are its central abstractions and its document parsing (LlamaParse) is first-party. LangChain can build excellent RAG too, but retrieval is one module among many. If RAG is the project, start with LlamaIndex; if RAG is one part of a larger agent system, LangChain may serve the whole better.

Can I use LangChain and LlamaIndex together? Yes, and many production stacks do. A common pattern is LlamaIndex for the data/retrieval layer and LangChain’s LangGraph for agent orchestration on top. They’re both Python-first and interoperate fine; pick the best tool for each layer rather than forcing one framework to do everything.

Are LangChain and LlamaIndex open source and free? Both core frameworks are MIT-licensed, free, and run entirely on your own infrastructure. Note that some add-on cloud services (LangChain’s LangSmith, LlamaIndex’s LlamaCloud/LlamaParse hosted tiers) are paid SaaS. You can build fully self-hosted, private RAG with the open-source core of either and never use the cloud products.

Which has a gentler learning curve? LlamaIndex is generally faster to a working RAG pipeline because retrieval is the central abstraction and the defaults are sensible. LangChain has a larger surface area (it spans far more than retrieval), so there’s more to learn — though the 1.0 consolidation around LangGraph has made its core more stable than its earlier reputation suggested.

Do I even need a framework for RAG? Not always. If your pipeline is simple — embed, store, retrieve top-k, prompt — calling your vector database and a local LLM directly with a thin layer of your own code is often clearer and easier to maintain than pulling in a framework. Reach for LangChain or LlamaIndex when the orchestration or document handling genuinely earns the dependency.

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