LlamaIndex and LangChain look interchangeable on a first read. Both ship retrievers, agents, and first-class Python and TypeScript SDKs. The real question is where your application's hard problem lives: getting the right data into the model, or orchestrating everything around it.
Copy link to headingWhat is LlamaIndex?
LlamaIndex is an open-source framework for connecting LLMs to private, domain-specific data. A model trained on the public web doesn't know about your PDFs, your warehouse, or your internal APIs. LlamaIndex is built to close that gap at inference time. Recent releases stretched the project beyond classic RAG into agent and workflow primitives, but ingestion and retrieval are still where it shines.
Copy link to headingCore components for data ingestion and retrieval
LlamaIndex organizes RAG into five stages: loading, indexing, storing, querying, and evaluation. Data connectors, called Readers, pull from files, databases, and APIs into a unified Document and Node data model that the rest of the framework operates on.
The framework ships multiple index types for different query patterns. Vector store indexes handle similarity search. Keyword table indexes cover term matching. For hierarchical data or knowledge graphs, there are tree and property graph indexes. Query engines wire retrievers, postprocessors, and response synthesizers into one interface, so a few lines of application code return a grounded answer.
Copy link to headingWho LlamaIndex is built for
LlamaIndex works best when ingestion quality and retrieval precision are the hard problems: enterprise search across contracts, compliance docs, and internal knowledge bases. Structured extraction from messy PDFs is another strong fit.
The hosted side of the project leans into the same workload. LlamaParse handles complex layouts, tables, and scanned pages, while LlamaExtract pulls schema-typed fields out of unstructured documents.
Copy link to headingWhat is LangChain?
LangChain is an open-source framework for building LLM-powered applications, with a current focus on agents and orchestration. LlamaIndex starts from the data. LangChain starts from the workflow and wires model calls, tool use, and decision logic into one runtime. The v1.0 release made create_agent the primary primitive and moved the older chains and LangChain Expression Language (LCEL) APIs into a separate langchain-classic package. The project committed to no further breaking changes until 2.0.
Copy link to headingCore components for agents and orchestration
The current LangChain stack runs across two main tiers. The core library handles the agent loop, model integrations, and a middleware system. Middleware hooks let you plug in message summarization, human-in-the-loop checkpoints, PII redaction, and similar custom behavior.
LangGraph sits underneath as the orchestration runtime for stateful, multi-step workflows with cycles, conditional branching, and durable state. Memory covers two ranges: short-term conversation history through LangGraph's checkpointing, and long-term recall through the LangMem SDK. LangSmith adds first-party tracing, evaluation, and production dashboards.
Copy link to headingWho LangChain is built for
LangChain suits projects where orchestration complexity has outgrown retrieval complexity. Multi-step research agents, customer-support workflows with CRM integration and human review, and cost-based model routing all land in LangChain's sweet spot.
It's also a good choice when you expect the workflow to outgrow a single linear chain. Standardized provider interfaces and the LangGraph runtime mean the same application can pick up new tools, new models, or new branches without a rewrite.
Copy link to headingKey differences between LlamaIndex and LangChain
LlamaIndex is data-centric, LangChain is orchestration-centric. Here's how that plays out across the areas developers usually compare.
Copy link to headingData ingestion
Ingestion is where the two frameworks diverge most. LlamaIndex ships distinct index structures and a Reader ecosystem that turn raw files into queryable Nodes in a few lines of code. The same pipeline extends to hosted parsing and extraction.
LangChain provides document loaders and text splitters as primitives, but the rest of the ingestion pipeline is something you wire up yourself. That tradeoff is intentional. The same loaders end up feeding agents that do more than retrieve.
Copy link to headingRetrieval and querying
LlamaIndex supports hybrid dense and sparse retrieval, configurable node postprocessors for reranking, and query fusion across multiple retrievers. Tighter retrieval abstractions help most when answer quality is the metric you're measuring, and they keep application code small as the corpus grows.
LangChain treats retrievers as one tool among many that an agent can call. The agent loop decides when to retrieve, how to reformulate the query, and what to do with the result. That's closer to how production agents work, though it pushes more responsibility into the orchestration layer.
Copy link to headingAgents and workflows
LangChain's agent layer, powered by LangGraph, handles stateful cyclic workflows where an agent reasons, acts, observes, and iterates. Conditional edges, multi-agent handoffs, and human-in-the-loop interrupts are all built into the graph definition.
LlamaIndex's FunctionAgent uses native function-calling for retrieval-grounded tasks, and its event-driven Workflows abstraction supports steps, loops, and branches for more involved orchestration. The framework can express most of what LangGraph does, but its orchestration layer is less opinionated about agent structure.
Copy link to headingObservability and evaluation
LangSmith is LangChain's first-party platform for tracing, evaluation, and production dashboards. It's framework-agnostic and explicitly supports LlamaIndex stacks, the AI SDK, and direct provider SDKs, so picking LangSmith doesn't lock the rest of the application to LangChain.
LlamaIndex leans on a network of integrations: Arize Phoenix, Langfuse, and the hosted LlamaTrace service all hook into the framework's callback system. Both options work in production. Pick LangSmith if you want one vendor for orchestration and observability, or stay with the integration route if you prefer to mix and match.
Copy link to headingTypeScript support
The langchain and llamaindex npm packages both ship full TypeScript paths, and both run on Node and Bun, which are supported Vercel runtimes. Neither SDK has meaningful feature gaps compared to Python, so the framework choice usually comes down to the same retrieval-vs-orchestration question regardless of language.
Deployment stays the same on both sides, too. Both frameworks plug into the AI SDK on Vercel, so the streaming, hooks, and UI primitives don't change when the underlying framework does.
Copy link to headingTradeoffs to consider with each framework
Every framework makes scope decisions, and knowing where each one draws its lines helps you plan ahead.
Copy link to headingWhere LlamaIndex narrows its scope
LlamaIndex keeps its orchestration scope intentionally tight, which pays off when the hard problem is data quality. For agents that need deep branching, multi-agent coordination, or long-running checkpoints, you may reach for LangGraph alongside it or use LlamaIndex's lower-level Workflows API for more control.
Some of the higher-level query engine APIs also abstract away parts of the agent loop that production teams want to inspect. That Workflows surface exposes those internals, with the tradeoff of writing more orchestration code by hand.
Copy link to headingWhere LangChain trades simplicity for flexibility
LangChain's layered abstractions give you many composition options, and those same layers add debugging surface area. Tracing a failed call through middleware, agent state, and graph edges takes more steps than reading a single function, which is exactly the kind of work LangSmith is designed to make easier.
The v1.0 release consolidated the API surface, with legacy chain APIs moved to langchain-classic and a clear migration path for older projects. Going forward, the project commits to no further breaking changes until 2.0.
Copy link to headingWhen to use LlamaIndex
If your biggest challenge is getting ingestion and retrieval right, LlamaIndex is usually the best place to start:
Enterprise search and document Q&A: Internal knowledge bases over PDFs, contracts, and policy documents, where ingestion quality and retrieval precision drive answer quality. The Reader ecosystem, hierarchical indexing, and structured extraction map onto that workload with little glue code.
Large-scale RAG with accuracy as the priority: Semantic search over large corpora where answer quality is the metric you're tracking. Hybrid retrieval, fine-grained chunking control, and query fusion address the failure modes that show up first as the corpus grows.
Structured extraction from PDFs and other unstructured data: Pulling typed fields out of legal contracts or financial statements, with LlamaParse handling the layout and LlamaExtract turning a schema definition into typed output.
If any one of these is the dominant problem, LlamaIndex usually keeps the codebase small.
Copy link to headingWhen to use LangChain
LangChain is usually the better fit when orchestration is the hard part:
Multi-step agentic workflows and tool use: Research agents, customer-support workflows with CRM integration and human escalation, and autonomous coding assistants. LangGraph's cyclic graphs, tool-calling loops, human-in-the-loop interrupts, and branching decision flows are all built in.
Chained pipelines across multiple LLM providers: Applications routing requests to different models based on cost, latency, or capability. Standardized provider interfaces let you swap providers without rewriting application logic, which helps a lot when model-diversification policies are in play.
Apps that need orchestration beyond retrieval: Conversational agents with persistent memory across sessions, workflow automation that touches external systems, and production deployments where tracing and evaluation are part of the operating contract.
If that describes your project, LangChain's primitives and LangGraph's runtime cover the orchestration layer so you can focus on application logic.
Copy link to headingUsing LlamaIndex and LangChain together
Running both frameworks in the same application is common in production. LlamaIndex tends to handle the data layer while LangChain manages orchestration around it.
Copy link to headingLlamaIndex for retrieval, LangChain for orchestration
LangChain's langchain_community package ships LlamaIndexRetriever and LlamaIndexGraphRetriever classes that wrap a LlamaIndex query engine in the retriever interface LangChain agents already understand. The same pattern works the other way for projects that want LangGraph as the runtime: a LlamaIndex query engine becomes a tool, and the graph decides when to call it.
Copy link to headingCommon hybrid architecture patterns
A typical hybrid keeps ingestion, indexing, and retrieval inside LlamaIndex while LangGraph manages the reasoning loop, tool calls, and state transitions. That split keeps the data pipeline focused on accuracy and the agent definition focused on behavior. It's simpler to debug than one framework trying to own both.
Copy link to headingBuilding LlamaIndex and LangChain apps on Vercel
Both frameworks run on Vercel through Vercel Functions and the AI SDK, which provides shared streaming, hooks, and UI primitives in Next.js. The @ai-sdk/langchain adapter was rewritten for AI SDK 6. It connects LangChain and LangGraph event streams to the SDK's UI message stream and preserves human-in-the-loop interrupts. A separate @ai-sdk/llamaindex adapter covers the same ground for LlamaIndex query engines and chat engines.
AI Gateway handles model routing, rate limiting, and observability for both stacks. The LangChain integration is documented end to end, and LlamaIndex on Python plugs in through the llama-index-llms-vercel-ai-gateway package. Long retrieval and agent runs need more headroom than the default function timeout, so maxDuration is worth setting deliberately. Functions with Fluid compute enabled can run up to 1800 seconds on Pro and Enterprise. Billing is based on actual compute usage, not wall-clock idle time.
Copy link to headingPick the framework that fits your hard problem
Most production projects end up running LlamaIndex and LangChain, with LlamaIndex handling ingestion and retrieval while LangChain or LangGraph handles the agent runtime around it.
On Vercel, deployment works the same either way. Both frameworks plug into the AI SDK, AI Gateway, and Vercel Functions running on Fluid compute, so swapping one for the other doesn't force changes elsewhere in the stack.
If you want to move from comparison to a running app, the LangChain starter template is a Next.js project with chat, agents, and retrieval examples ready to deploy. From there, start a new project or browse templates to layer in the data and orchestration patterns the application needs.
Copy link to headingFrequently asked questions about LlamaIndex and LangChain
Copy link to headingIs LlamaIndex better than LangChain for RAG?
LlamaIndex ships more opinionated, purpose-built abstractions for RAG, with defaults that keep configuration short for common retrieval patterns. LangChain supports RAG as one capability inside a broader runtime. For projects whose primary challenge is retrieval accuracy across a document collection, LlamaIndex tends to reduce the amount of glue code in the application.
Copy link to headingCan I use LlamaIndex and LangChain together?
Yes, and it's a common production pattern. LangChain's langchain_community package provides LlamaIndexRetriever and LlamaIndexGraphRetriever classes that wrap LlamaIndex query engines as LangChain-compatible retrievers. The same query engines can be exposed as tools inside a LangGraph workflow.
Copy link to headingWhich has better TypeScript support?
Both langchain and llamaindex on npm offer first-class TypeScript paths and run on Vercel Functions. Pick based on whether the application needs stronger orchestration or stronger retrieval, not the language.
Copy link to headingDo I need a framework at all for RAG?
Not always. A direct provider SDK paired with a vector database client can handle a basic RAG pipeline without framework overhead. Frameworks help once the application needs hybrid retrieval, multi-step agents, durable state, or cross-provider observability.