LlamaIndex vs LangChain: Which RAG Framework Should You Build On

Choosing between LlamaIndex and LangChain is a commitment, not a library import. The framework you pick will shape how your team models retrieval, how it debugs failures, and how much of the codebase has to move when the next major version lands. You will live inside the winner’s abstractions for years.

The choice is also harder to research than it should be, because most comparison articles still describe the 2023 versions of both projects. LangChain went through a ground-up 1.0 rearchitecture in late 2025. LlamaIndex pivoted from a retrieval library into a multi-agent framework. An article that talks about chains and conversation memory on one side and “just a search tool” on the other is describing software that no longer exists.

The stakes justify getting this right. Enterprise AI spend hit $37 billion in 2025, up 3.2x from the year before, and RAG remains the second-most-common way enterprises customize models (Menlo Ventures, December 2025). The framework decision sits under all of that.

This guide walks through five questions: what LangChain actually is today, what LlamaIndex actually is today, how they compare on verified numbers, the features that genuinely separate them, and the decision rules that map your use case to a framework. Everything version-specific carries a date, because this space does not sit still.

What Is LangChain in 2026?

LangChain is an open-source framework for building LLM applications, and since late 2025 it describes itself as an agent engineering platform. That self-description is accurate, and it marks how far the project has traveled from the chains-and-prompts library most engineers remember.

The 1.0 reset: from chains to agents

LangChain 1.0 shipped on October 22, 2025, and it was a repositioning, not a point release. The legacy surface that defined the framework’s first three years, chains, the old memory classes, the half-dozen coexisting agent patterns, moved out of the core package into langchain-classic. What remained was rebuilt around a single primitive:

from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)

That simplification matters for a reason beyond ergonomics. The loudest criticism of pre-1.0 LangChain, on Reddit and everywhere else, was abstraction churn: five different ways to build the same agent, with outdated tutorials ranking in search forever. The 1.0 release is the project’s answer, and practitioner sentiment has noticeably softened since. The advice showing up in r/LangChain threads now is to ignore pre-v1 material entirely and start from the current docs.

LangGraph is the runtime, not a competitor

One clarifier before anything else, because search data shows this confuses more engineers than the LlamaIndex comparison does: LangGraph is not a LangChain alternative. It is the graph-based runtime that LangChain agents now run on. Every create_agent call executes on LangGraph underneath.

The layering is deliberate. create_agent covers the standard shapes: a model, some tools, a loop. You drop down to LangGraph directly when the standard shape stops fitting, when you need a custom state machine with branching and cycles, durable checkpoints that survive a crash mid-run, or a human-approval gate that pauses execution until someone signs off. Multi-agent systems, where several agents hand work to each other under an orchestrator, are built at this layer too.

The commercial layer: LangSmith

Around the open-source core sits LangSmith, the company’s commercial platform for tracing, evaluation, and deployment. When an agent misbehaves in production, LangSmith is where you replay the run step by step and see which tool call or model response went sideways.

The company behind the framework is well capitalized: a $125M Series B at a $1.25B valuation, announced October 2025. The same announcement reported 90M combined monthly downloads, 35% of the Fortune 500 using LangChain products, and 12x year-over-year growth in LangSmith trace volume; treat those three as company-reported rather than independently audited. Current open-source release as of August 2026: langchain 1.3.x, MIT-licensed, in Python and JavaScript/TypeScript.

What Is LlamaIndex in 2026?

LlamaIndex is an open-source framework that started life in November 2022 as GPT Index, with one job: connect LLMs to your data. That data-first origin still defines it, even as the project has grown into something considerably larger.

Retrieval-first, and it shows in the defaults

The core pipeline is ingestion, indexing, and querying. Documents come in through readers, get chunked and embedded into an index, and a query engine answers questions over them. The whole loop fits in five lines:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

What the five lines hide is the depth underneath them. Hybrid search combining BM25 keyword matching with dense vectors, reranking pipelines, and a catalog of chunking strategies all arrive as configurable defaults rather than components you assemble by hand. This is the practical meaning of “retrieval-first”: the things a document Q&A system needs to answer well are the things LlamaIndex optimized before anything else.

The pivot: from RAG library to document agents

LlamaIndex has moved well past static question answering. “We’ve fully made the pivot to a multi-agent framework,” co-founder Jerry Liu said in mid-2025, describing classic RAG as “a very fixed process” next to agents that load files dynamically, analyze functions, and search specific pages as a task demands.

Orchestration happens through event-driven Workflows rather than graphs: steps subscribe to events and emit new ones, which composes lightweight pipelines without a state-machine formalism. It is a genuinely different orchestration philosophy from LangGraph’s, lighter and less prescriptive, and llama-deploy exists to take those Workflows to production as services.

The commercial layer: LlamaCloud and LlamaParse

The company’s commercial bet is the document layer. LlamaParse handles the documents that break naive pipelines: scanned PDFs, financial tables, slide decks, embedded charts. LlamaCloud wraps parsing, indexing, and retrieval into a managed service, and both went GA alongside a $19M Series A in March 2025. Today the company describes itself as a document-agent and OCR platform, which tells you where it believes its edge lives.

Current release as of August 2026: llama-index 0.14.x, MIT-licensed, Python and TypeScript. Note the version number honestly: LlamaIndex has not declared a 1.0, which in practice means somewhat more API movement between releases than LangChain’s post-1.0 stability contract.

LlamaIndex vs LangChain: Head-to-Head Comparison

The verified numbers first, then the dimensions that matter. Every figure below was pulled from its primary source on August 4, 2026, because numbers in this space rot fast and most comparison articles never say when theirs were true.

Two honest caveats on that table. Download counts include CI and mirror traffic, so read them as relative scale, not user counts. And llama-index is a meta-package over a modular core, so summing its package downloads undercounts real usage somewhat. The order-of-magnitude gap survives both caveats.

And now the number that should reframe how you read both tables. The most careful independent measurement available says the frameworks do not differ on answer quality at all.

Read that carefully and the decision reframes itself. You are not choosing which framework answers better. You are choosing which one gets your team to a maintainable system faster, at acceptable token cost, with the orchestration and debugging story your production environment needs. Those token deltas compound, too: at scale, a ~40% difference in tokens per query is a real line item on the inference bill.

The Key Features That Actually Decide It

Feature catalogs don’t settle this choice; both ecosystems check most boxes on paper. Four differences carry real decision weight.

1. Retrieval depth and document parsing

LlamaIndex ships hybrid search (BM25 plus dense vectors), reranking pipelines, and a menu of chunking strategies as defaults rather than assembly work. LangChain can reach the same retrieval quality, but you assemble it from components, and the assembly is where prototypes stall. LlamaParse then extends the lead into the documents that break naive pipelines: scanned PDFs, financial tables, slide decks.

Practitioner sentiment on Reddit is blunt about this edge. In one widely-discussed r/LangChain thread from an engineer who had shipped production systems on both: “LlamaIndex wins on retrieval quality. It’s not close… LlamaIndex was built retrieval-first and it shows.” Their decision rule is worth quoting whole: “If your app is mostly ‘search my documents and answer questions,’ use LlamaIndex. If your app is ‘search my documents, then do 5 other things with the results,’ use LangChain/LangGraph.”

2. Agent orchestration and durable state

LangGraph is why serious multi-step agents land on LangChain. Checkpointers persist agent state across failures, so a crashed run resumes instead of restarting. Human-in-the-loop gates pause execution for approval before an agent does something consequential. Time-travel debugging rewinds a run to inspect the exact state at each step. LlamaIndex Workflows compose multi-step logic cleanly, but the durable-execution and audit story is thinner, and complex agent topologies push its event model harder than LangGraph’s explicit state machines.

Harrison Chase’s framing explains why LangChain invested here: with agents, “the logic for how your application works is not all in the code. A large part of it comes from the model.” When the model owns part of your control flow, you need infrastructure to observe, checkpoint, and override it. That is the entire LangGraph thesis.

3. Observability and evals

LangSmith is the most integrated first-party observability option in either ecosystem: tracing, eval suites, and deployment in one place, wired into the framework. LangChain reports its trace volume grew 12x year over year (company-reported). LlamaIndex takes the opposite approach, integrating with the open observability ecosystem instead of building its own. If your team already standardizes on a third-party tracing stack, you lose little; if you want one vendor and one pane of glass, LangSmith is the argument.

4. The managed platforms mirror the strengths

Look at what each company charges money for and the comparison writes itself. LlamaCloud monetizes the document layer: parsing, indexing, and retrieval as a managed service. LangGraph Platform monetizes the agent layer: deploying and operating long-running, stateful agents. Each company built its business on the thing its framework does best, which is as honest a signal as this market produces.

Which One Should You Build On? Use Cases and Decision Rules

Match the framework to the shape of your system, not to star counts. The use cases sort cleanly.

The pattern experienced teams converge on uses both. As one r/LangChain practitioner put it: “LangGraph handles orchestration well but LlamaIndex still wins on pure retrieval quality… The combo that’s worked best in practice: LangGraph for the outer loop, LlamaIndex retrievers as tool calls within the agent.” The integration is thin, a LlamaIndex query engine wraps naturally as a LangChain tool, and the split respects each framework’s center of gravity. It is the architecture worth evaluating first for production.

Worth naming the alternatives, briefly. Haystack shows up as the third option in most bake-offs and held its own in the AIMultiple benchmark; teams already deep in its pipeline model have little reason to migrate. And agent-native frameworks like CrewAI and AutoGen answer a different question, multi-agent collaboration, rather than the retrieval-framework question this article covers.

The verdict in one line: build document-centric systems on LlamaIndex, build stateful multi-step agents on LangChain with LangGraph, and combine them when you need both depths. Whichever you pick, no framework saves a RAG system from bad data; that failure mode lives upstream, in why RAG pipelines fail in production and in the storage layer under your embeddings. Still weighing whether RAG is the right strategy at all? Start with fine-tuning vs RAG.

Frequently Asked Questions

What is the difference between LangChain and LlamaIndex?

Both are MIT-licensed frameworks for LLM applications, and both now build agents. LangChain centers on orchestration: the LangGraph runtime manages agent state, branching, and durable execution. LlamaIndex centers on documents: retrieval defaults, LlamaParse parsing, and event-driven Workflows. The practical difference is where each gets you to production quality with the least custom code, and that follows each project’s origin.

Is LlamaIndex better than LangChain for RAG?

For retrieval itself, usually yes: hybrid search, rerankers, and chunking arrive as defaults instead of assembly work. But an independent June 2026 benchmark found identical answer accuracy across frameworks with the same components, so “better for RAG” in practice means faster to good retrieval at lower token overhead, not better answers. If your RAG system feeds a complex agent, LangGraph’s orchestration may matter more than the retrieval delta.

Are LangChain and LlamaIndex free and open source?

Yes, both core frameworks are MIT-licensed, including LangGraph and Workflows. The companies monetize managed layers: LangSmith and LangGraph Platform on one side, LlamaCloud and LlamaParse on the other. You can run either in production without paying anything, at the cost of assembling your own observability, evaluation, and deployment infrastructure from open components.

Can you use LangChain and LlamaIndex together?

Yes, and for serious systems this is the pattern practitioners increasingly recommend: LlamaIndex handles ingestion, indexing, and retrieval, exposed as tools a LangGraph agent calls during orchestration. The integration is thin, since a LlamaIndex query engine maps naturally onto a LangChain tool interface, and each framework ends up doing the job it was built for.

Is LangChain still relevant in 2026?

More than before, by the numbers: ~299M monthly PyPI downloads as of August 2026, a $1.25B valuation, and a 1.0 release that resolved much of the abstraction churn it was criticized for. The “LangChain is bloated” criticism you’ll find in older Reddit threads largely predates create_agent and the v1 documentation; current practitioner advice is to evaluate the 1.x framework on its own terms.

Related Articles

• What Is Retrieval-Augmented Generation (RAG)?: The foundational explainer on how RAG grounds LLMs in your data
• Why RAG Pipelines Fail in Production: The data-quality failure modes no framework can fix
• Data Storage for LLMs and RAGs: Choosing the vector database and storage layer under your framework
• Fine-Tuning vs RAG: Deciding whether RAG is the right customization strategy at all