LangChain vs LlamaIndex for RAG: Which Framework Should You Use?

Every team building a RAG system eventually hits the same fork: LangChain or LlamaIndex? The confusion is fair. Both frameworks retrieve documents, call LLMs, and produce answers. Both have grown fast and converged in places. Both have changed enough over the past year that advice from late 2024 can point you at the wrong default.

The hard part is that the two frameworks optimize for different problems. Pick the wrong one and you do not get a broken system. You get a working system that fights you every time you try to tune it. A retrieval-focused team that ends up wiring LangChain's chain abstractions by hand, or an agent team wrestling with LlamaIndex's index-centric mental model, is the usual cost of defaulting to whichever tool showed up first in a search result.

So the choice is hard because both look interchangeable on day one and only diverge once your real workload shows up.

What LlamaIndex is#

LlamaIndex is a data framework built specifically for connecting language models to external data sources. Its primary job is ingestion, indexing, and retrieval: it takes your PDFs, SQL databases, APIs, and internal documents, turns them into indexes, and gives an LLM the right context at query time.

The framework ships with over 300 data connectors, multiple index types (vector, summary, knowledge graph), and higher-level retrieval abstractions like hierarchical chunking, auto-merging retrieval, and sub-question decomposition. Its query engines handle question-answering (RAG flows), while chat engines handle multi-turn conversations over the same indexed data. For complex document parsing, LlamaCloud offers LlamaParse, a vision-language model-based parser designed for nested tables, charts, and structured documents.

LlamaIndex is Python-first, though a TypeScript version exists. The design philosophy is depth over breadth: it would rather give you five different retrieval strategies than five different tool integrations.

What LangChain is#

LangChain is a broader orchestration framework for building applications with language models. Its core library provides standardized interfaces for models, prompts, output parsers, retrievers, and tool calls. These are the building blocks you chain together into pipelines.

The agent story in LangChain has shifted significantly. The real agent runtime is now LangGraph, a separate but tightly coupled library that models agent workflows as directed graphs. LangGraph supports persistent state, long-running execution, human-in-the-loop checkpoints, and streaming. The LangChain core remains for chain composition and model abstraction, while LangGraph handles stateful, multi-step agent loops.

LangChain's ecosystem is wide. It supports over 500 integrations and works with OpenAI, Anthropic, AWS Bedrock, Azure, Ollama, HuggingFace, Fireworks, and more via a standardized model interface. Observability flows through LangSmith, a platform-agnostic tracing, evaluation, and monitoring tool that works with any LLM framework, not just LangChain.

How the two compare#

LlamaIndex leads on retrieval quality#

LlamaIndex wins cleanly on out-of-the-box retrieval quality. It ships with retrieval strategies you would otherwise build manually.

Hierarchical chunking splits documents into smaller chunks for retrieval but returns the parent chunk on match, which prevents answers that get cut off mid-sentence. Auto-merging retrieval combines overlapping retrieved chunks before sending them to the LLM, reducing noise and contradiction. Sub-question decomposition breaks complex questions into simpler sub-queries, retrieves answers to each, then synthesizes a final response. These strategies produce meaningfully better results on multi-hop and multi-document questions without manual tuning.

LangChain can implement all of these, but they require explicit wiring. Nothing prevents you from building a hierarchical retriever in LangChain, since the framework is extensible enough. The difference is that LlamaIndex ships the right default and you opt out, while LangChain requires you to opt in and build.

If retrieval quality is the primary metric for your product, this gap is real.

LangGraph leads on agent orchestration#

LangGraph is the better choice for agent orchestration, and LangChain is its primary delivery vehicle.

LangGraph models agents as directed graphs with nodes (steps) and edges (transitions). This structure makes complex, stateful workflows tractable. It supports recovery from failures via checkpoints, human-in-the-loop approval at any node, persistent memory across sessions, and streaming so you can observe intermediate outputs. According to LangGraph's documentation, the framework is designed for "long-running, stateful agents" where reliability and control matter as much as task completion.

LlamaIndex has agents, but they are lighter-weight. They work well for retrieval-augmented tool use (look up a document, call an API, answer a question), yet they are not designed for durable, multi-step workflows with persistence and rollback. If you need an agent that coordinates across ten tools, retries on failure, and waits for a human approval before an action, LangGraph is the right layer.

How each handles observability#

Both frameworks have first-class observability, but they take different approaches.

LangChain ships LangSmith, which is framework-agnostic by design. It traces requests from any LLM application using Python, TypeScript, Go, or Java SDKs. Dashboards track token usage, latency percentiles, cost, error rates, and user feedback. Evaluation supports metrics like context precision and faithfulness for RAG-specific quality assessment. LangSmith is available as managed cloud, BYOC, or self-hosted, which helps teams with data residency requirements.

LlamaIndex's observability answer is LlamaTrace, built on the open-source Arize Phoenix platform. Phoenix uses OpenTelemetry under the hood, which means any OTel-compatible library plugs in without modification. Built-in evaluators cover faithfulness, relevance, hallucination, toxicity, and custom criteria. For RAG-specific debugging (tracing every chunk retrieved and seeing how it influenced the answer) Phoenix is particularly well-suited.

The practical difference is one of scope. LangSmith is better for team-scale monitoring and cross-pipeline evaluation. Phoenix and LlamaTrace are better for debugging retrieval quality on a specific query.

Integrations and ecosystem#

LangChain has a broader integration surface. Its 500+ integrations cover almost every LLM provider, vector store, retriever, document loader, and tool adapter you will encounter. The standardized model interface means switching from OpenAI to Anthropic to Bedrock is a one-line change. This breadth matters for teams building against many external services or wanting to evaluate multiple model providers quickly.

LlamaIndex's 300+ connectors are focused on data ingestion: PDFs, SQL, REST APIs, Google Docs, Notion, GitHub, Slack, and more. These connectors are deeply integrated with the indexing pipeline, so data flows cleanly from source to chunk to vector store. For teams spending most of their integration work on getting documents in rather than connecting external tools, this distinction matters less.

Real-world scenarios#

Scenario 1: Internal document Q&A over 50,000 PDFs Your team has a large corpus of PDFs that need semantic search and structured Q&A. Retrieval quality is the product. Use LlamaIndex. Its hierarchical chunking and auto-merging retrieval handle long documents better. LlamaParse handles complex PDFs with tables. You will spend less time writing retrieval logic and more time improving answer quality.

Scenario 2: A customer-facing agent that searches your catalog, emails support, and books meetings This is a multi-tool, multi-step workflow with external service calls and optional human approval. Use LangGraph. Its graph-based execution model handles branching, error recovery, and state persistence cleanly. Wire in a LlamaIndex retriever if catalog search quality matters.

Scenario 3: Rapid prototype for a startup demo Either works. LlamaIndex has less boilerplate for pure RAG. LangChain's integrations are broader if you are unsure which tools you will need. Pick the one your team is more familiar with and move.

Scenario 4: Enterprise platform with multiple RAG pipelines and a centralized evaluation team Use LangChain with LangSmith. The framework-agnostic tracing, team-level dashboards, and BYOC/self-hosted options fit an enterprise eval workflow better than LlamaTrace at scale.

The common wrong choice#

The most common mistake is picking LangChain for a straightforward document Q&A product because it appears more often in tutorials and has broader name recognition. For pure retrieval, where the only job is "ask a question, get a good answer from documents," LangChain adds abstraction without adding retrieval quality. You end up building chunking logic and retrieval strategies by hand that LlamaIndex ships by default.

The second common mistake is picking LlamaIndex for a complex multi-tool agent. LlamaIndex's agents handle simple tool use well, but they are not designed for durable stateful workflows. You will hit walls when you need retry logic, state persistence across sessions, or human-in-the-loop checkpointing.

How to choose#

The recommendation comes down to one question asked first: what is the primary job of your system? Sort on that, and the rest of the decision falls into place. The tree below is the shortest path from workload to framework.

Then work through these questions to confirm the call:

1. What is your system's primary job? If it is "retrieve relevant context and answer questions," LlamaIndex. If it is "orchestrate multiple tools, models, and steps in a stateful workflow," LangGraph/LangChain.

2. How much control do you need over each retrieval step? LlamaIndex gives you high-level retrievers you can tune. LangChain gives you lower-level components you compose yourself.

3. What is your observability priority? For team-level monitoring and cross-pipeline evals, LangSmith. For per-query RAG debugging, Arize Phoenix with LlamaTrace.

4. Are you building for a regulated or data-residency-constrained environment? Both have self-hosted options. LangSmith has explicit BYOC support; Arize Phoenix is open-source and self-hostable in one command.

5. Do you need multi-agent or human-in-the-loop flows? If yes, LangGraph is the right runtime regardless of which retrieval layer you choose.

Our recommendation#

Start with LlamaIndex if your product is retrieval-first. It requires less code for a basic RAG pipeline, ships better retrieval defaults, and integrates cleanly with LangSmith or Phoenix for observability. You can always add a LangGraph orchestration layer later without replacing your retrieval logic.

Start with LangChain plus LangGraph if you are building a tool-using agent from the beginning, need stateful execution with recovery, or want a single framework that handles both retrieval and multi-step orchestration. Wire in LlamaIndex's retriever as one node in the graph if retrieval quality matters.

Use both if your system is large enough to separate concerns: LlamaIndex as the retrieval service, LangGraph as the execution layer, LangSmith as the observability layer. This is increasingly how production RAG systems are built, and both frameworks are designed to interoperate. See our guide on building a RAG search pipeline for a practical walkthrough, and our comparison of fine-tuning vs RAG for AI products to understand when retrieval is the right approach at all.

Key Takeaways#

• LlamaIndex is the stronger default for retrieval-heavy systems. Its hierarchical chunking, auto-merging retrieval, and sub-question decomposition produce better results with less manual wiring than LangChain's component-based approach.
• LangChain + LangGraph is the right choice for complex, stateful agent workflows. LangGraph's durable execution, human-in-the-loop support, and checkpoint-based recovery are not available in LlamaIndex's lighter agent layer.
• Both frameworks have first-class observability, but via different tools: LangSmith for team-level monitoring, Arize Phoenix/LlamaTrace for per-query RAG debugging.
• Many production systems use both: LlamaIndex handles retrieval, LangGraph handles orchestration.
• Avoid choosing based on tutorial familiarity or GitHub stars. Evaluate the primary workload (retrieval depth versus agent complexity), then pick the framework that fits the shape of the problem.

FAQ#

Can I use LlamaIndex and LangChain together?

Yes. A common pattern is to use a LlamaIndex query engine as a retriever inside a LangGraph agent. LlamaIndex handles chunking, indexing, and retrieval, while LangGraph handles multi-step execution, state, and tool orchestration. Both frameworks expose standard interfaces that make this straightforward.

Is LangChain being replaced by LangGraph?

Not exactly. LangChain core remains for chain composition and model abstraction. LangGraph is the recommended runtime for stateful, multi-step agents. The LangChain docs describe LangGraph as built on top of LangChain, serving as the underlying orchestration engine for LangChain's higher-level agent patterns (LangGraph overview).

What is LlamaTrace and how does it differ from LangSmith?

LlamaTrace is LlamaIndex's hosted observability platform, built on the open-source Arize Phoenix project. It integrates natively with LlamaIndex workloads and uses OpenTelemetry for tracing. LangSmith is LangChain's observability platform, framework-agnostic, with richer team-level dashboards and evaluation workflows. Both track retrieval quality metrics like faithfulness and context relevance.

Which framework has better TypeScript support?

LangChain has a more mature TypeScript SDK. LlamaIndex has a TypeScript version, but the Python version is more feature-complete and receives updates first. If your team is TypeScript-first, LangChain is the safer choice today.

How do these frameworks handle multi-tenancy?

Both support multi-tenancy but in different ways. LlamaIndex handles it through namespace separation within vector stores. LangChain handles it through LangSmith's project-level isolation and LangGraph's session-scoped state. For production multi-tenant RAG, the vector store's tenant isolation strategy (at the database level) usually matters more than the framework choice. See our notes on how vector search works.