LlamaIndex and LangChain occupy overlapping but distinct territory in the LLM application framework world. Both can do retrieval-augmented generation. Both can wire up LLMs, tools, and document pipelines. The difference is what they're optimized for — LlamaIndex was built RAG-first, LangChain was built as a broader LLM toolkit.
In 2026 the lines have blurred (LangChain has matured RAG capabilities; LlamaIndex has expanded into agents and chat) but the distinct philosophies still show up in the developer experience. We see both used heavily across Respan's customer base. This article is the side-by-side from running production apps on both.
TL;DR — when to pick each
| Pick LlamaIndex if... | Pick LangChain if... |
|---|---|
| Your application is RAG-first (search-and-summarize over documents) | Your application is broader than RAG (agents, multi-step workflows, integrations) |
| You want best-in-class document indexing, chunking, and retrieval | You want the widest ecosystem of LLM/vectorstore/tool integrations |
| You're processing complex documents (PDFs with tables, structured data, multi-modal) | You're orchestrating LLM workflows that go beyond document Q&A |
| You want a more opinionated framework that "just works" for RAG | You want flexibility to compose custom flows |
| You're working with hierarchical or graph-structured knowledge | You want a broader community / more tutorials |
In production, sophisticated teams often use LlamaIndex for the RAG layer and LangChain (or LangGraph) for the broader application layer. They're not exclusive.
What each framework is
LlamaIndex (originally GPT Index) is the framework built for retrieval-augmented generation. The library specializes in:
- Document parsing — PDFs, structured data, tables, images, multi-modal
- Index types — vector index, summary index, knowledge graph, tree, list
- Query engines — high-level RAG patterns (sub-question, multi-step, recursive retrieval)
- Advanced retrieval — hierarchical retrieval, hybrid search, query routing
- Agent and workflow primitives (added more recently)
- LlamaParse — separately-priced parsing service for complex documents
LangChain is the broad framework for building LLM applications. As covered in our LangChain vs LangGraph article, the library provides chains, retrievers, agents, document loaders, and the largest collection of integration wrappers in the ecosystem. RAG is a use case it supports well, but not the primary identity.
RAG capability comparison
For pure RAG workloads where the application is "ingest documents, answer questions about them with citations":
LlamaIndex strengths:
- More sophisticated index types out of the box — vector, summary, tree, knowledge graph, hybrid combinations
- Advanced query patterns — sub-question decomposition, multi-step retrieval, query routing
- LlamaParse handles complex PDFs (tables, multi-column layouts, scanned documents) better than most parsers
- More retrieval-specific features — re-rankers, response synthesis modes, citation tracking
- More opinionated defaults — less code to get a working RAG pipeline
LangChain strengths:
- Broader ecosystem — more vectorstore wrappers, more LLM clients, more tools
- More flexibility in composition — easier to bolt RAG into a larger workflow
- Larger community / more public examples for any given vectorstore or model
- Mature retriever abstraction that integrates with the rest of the framework
For a RAG-only project, LlamaIndex tends to ship faster and to higher quality with less code. For a project where RAG is one piece of a larger LLM application, LangChain's flexibility wins.
Beyond RAG
LangChain has the broader feature surface:
- Agents (legacy
AgentExecutor, now superseded by LangGraph) - Memory primitives for conversational AI
- Document loaders for arbitrary data sources
- Tools and function-calling wrappers across providers
- Output parsing and structured response handling
- Built-in support for evaluation pipelines
LlamaIndex has expanded these areas in 2025-2026 (added agents, more chat-style patterns) but still trails LangChain on breadth. If your application needs multi-tool agents, complex orchestration, or non-RAG workflows, LangChain has more available.
Document handling
This is where LlamaIndex's RAG-first identity shows most clearly:
- LlamaParse is LlamaIndex's parsing service. It handles complex PDFs, tables, multi-column layouts, scanned documents better than open-source parsers. It's a paid service (separate from the open-source library) but the quality on hard documents is meaningfully better than alternatives.
- Index variety is broader than LangChain — vector index, summary index, tree, knowledge graph, hybrid. For non-trivial document corpora, the right index type matters.
- Citation tracking and response synthesis modes are more developed.
If your application's hardest problem is "how do we accurately extract information from messy enterprise documents," LlamaIndex (especially with LlamaParse) is the better starting point.
Ecosystem and community
LangChain has the larger community in absolute terms — more GitHub stars, more public tutorials, more StackOverflow questions, more SDK wrappers contributed by external teams. For any given vectorstore or LLM provider, LangChain tends to have a wrapper that already exists.
LlamaIndex has a focused, growing community. Documentation is high quality. Public examples are RAG-heavy.
For learning curve and "first 30 minutes," LangChain has more material; for depth on RAG specifically, LlamaIndex has more focused material.
Performance
Both frameworks perform similarly at runtime — LLM latency dominates everything. The framework overhead is small in either case.
LlamaIndex's index types can affect performance materially — a tree index may be much faster than a flat vector search for hierarchical queries; a summary index may be slower for the first call but cheaper for repeated queries. LangChain offers similar capabilities through composition but you build them yourself more often.
Observability
Both frameworks integrate with major observability platforms via callbacks and OpenTelemetry. Respan supports both via the standard OTel GenAI conventions.
Anecdotally, LangChain's older callback system is more battle-tested across observability platforms; LlamaIndex's instrumentation has caught up but still occasionally has gaps for niche flows.
Frank's take — when I actually pick which
For a pure RAG product, default to LlamaIndex. Question-answering over documents, knowledge bases, support docs, contract analysis — these are LlamaIndex's home turf. The advanced index types and LlamaParse are real productivity wins.
For broader LLM applications, default to LangChain (with LangGraph for agents). Multi-step workflows, tool-using agents, complex orchestration — LangChain's breadth and integrations matter more than LlamaIndex's RAG depth.
Use both in the same codebase. LlamaIndex for the RAG layer, LangChain (and LangGraph) for the agent layer. They're not exclusive — most production stacks I see do this.
Use LlamaParse if your documents are complex. Multi-column PDFs, financial filings, scanned contracts, tables — LlamaParse's quality on these is meaningfully better than open-source parsers.
Don't over-engineer. If your use case is "ask LLM about 10 small text docs," neither framework is needed. Embed → store in any vectorstore → retrieve top-k → call LLM. 50 lines of Python. Add a framework when complexity demands it.
How to decide for your project
- Is your application primarily RAG? → LlamaIndex
- Do you have complex documents (PDFs, tables, scanned)? → LlamaIndex (with LlamaParse)
- Are you orchestrating multi-step LLM workflows beyond RAG? → LangChain
- Are you building a tool-using agent? → LangChain + LangGraph
- Are you integrating with many vectorstores / LLM providers? → LangChain (broader ecosystem)
- Do you want best-in-class hierarchical retrieval? → LlamaIndex
FAQ
Is LlamaIndex better than LangChain? For RAG specifically, LlamaIndex is more opinionated and ships better defaults. For broader LLM applications, LangChain has the wider ecosystem and breadth. They're not direct competitors — they overlap on RAG and diverge on everything else.
Can I use LlamaIndex with LangChain? Yes. They integrate cleanly. Common pattern: LlamaIndex for the RAG layer, LangChain (and LangGraph) for the application/agent layer.
Does LlamaIndex support agents? Yes — LlamaIndex has expanded into agents and chat workflows in 2025-2026. The agent surface is smaller than LangChain's but functional. For production agent work, LangGraph is still the more mature choice.
Is LlamaParse free? LlamaParse is a paid service separate from the open-source LlamaIndex library. It has a free tier with quotas. The pricing is competitive for the parsing quality on complex documents.
Which is faster to learn? LangChain has more public material (tutorials, examples, StackOverflow). LlamaIndex has more focused material specifically for RAG. For first-30-minutes-to-working-demo, LangChain has slight edge.
Do they work with the same LLM providers? Yes — both integrate with the major providers (OpenAI, Anthropic, Google, etc.). LangChain has more integrations overall; LlamaIndex covers the major ones cleanly.
Should I use LlamaIndex if I'm using Pinecone / Weaviate / Qdrant? Either framework works with these vectorstores. LangChain has more vectorstore wrappers; LlamaIndex's wrappers are well-tested for the major ones.
Can I use both LlamaIndex and LangChain in the same app? Yes — and many production apps do. They're not exclusive choices.