LangChain vs LlamaIndex: Agents and RAG Compared
LangChain and LlamaIndex are the two biggest open-source frameworks for AI agents in 2026. They overlap but they are not direct competitors. LangChain is a general agent toolkit; LlamaIndex is a data-first framework that grew into agents. Most experienced teams use both, each for its strength. This page is the practical comparison: who should pick which, by use case.
Quick verdict
- Pick LangChain if: your problem is tool-heavy (agent calls many APIs, runs multi-step workflows)
- Pick LlamaIndex if: your problem is data-heavy (ingest 100+ documents, answer questions across them)
- Use both if: you want LlamaIndex's data layer plus LangChain's agent orchestration
- For pure observability: LangChain + LangSmith is the most mature stack
Who should pick LangChain
- You are building an agent that calls many tools (search, code execution, internal APIs, MCP servers)
- RAG (retrieval-augmented generation) is one feature among many, not the core problem
- You want LangSmith for tracing, evaluation, and the LangSmith Engine (released May 2026)
- You need JavaScript support, not just Python
- You want the widest community and tutorial library
Who should pick LlamaIndex
- Your problem starts with documents: parsing, chunking, embedding, retrieving
- You want LlamaParse for parsing 50+ unstructured file types with high accuracy
- You are building a knowledge-work agent over a large corpus
- You are okay with Python-only (the OSS framework is Python-first)
The core architectural difference
LangChain's mental model is the agent loop. You build an agent, give it tools, and the framework manages the call loop. Tools can be anything: a vector store, a search API, a custom Python function. RAG is one tool among many.
LlamaIndex's mental model is the index. You define how data is ingested, chunked, embedded, and retrieved. The agent sits on top of that index and consumes it. The data layer is the framework's strongest part. Agent features added later grew out of the data foundation.
Side-by-side comparison
| Axis | LangChain | LlamaIndex |
|---|---|---|
| Primary identity | General agent framework | Data-first RAG framework |
| Languages | Python + JavaScript | Python (TypeScript port exists) |
| Document parsing | Via document loaders | LlamaParse (50+ file types, best in class) |
| Agent maturity | Most mature | Growing |
| Observability product | LangSmith (best in category) | LlamaTrace |
| Community downloads | Largest in agent category | 25M+ packages/month |
| Multi-agent native | Via LangGraph | Limited |
| Recent 2026 launches | LangSmith Engine (May 2026), Managed Deep Agents | LlamaParse, LiteParse, LlamaCloud |
| Best for | Tool-heavy general agents | Data-heavy knowledge agents |
How real teams use them together
A common 2026 production pattern: LlamaIndex handles document ingestion and retrieval. LangChain handles agent orchestration and tool calling. LangSmith provides observability across both. Inside one production agent, a LangChain Tool wraps a LlamaIndex retriever. The agent calls the tool. LlamaIndex queries the index. Results come back as context.
This pattern means "LangChain vs LlamaIndex" is often the wrong framing. The right question is whether your data needs are deep enough to justify the LlamaIndex layer, or whether LangChain's built-in retrievers are sufficient.
Pricing comparison
| Layer | LangChain | LlamaIndex |
|---|---|---|
| OSS framework | Free (MIT) | Free (MIT) |
| Observability free tier | LangSmith Developer (free) | LlamaTrace (free tier) |
| Observability paid | LangSmith Plus, $39/seat/month | LlamaTrace tiers |
| Document parsing | Via free loaders | LlamaParse (paid, tier-based) |
| Managed deployment | Managed Deep Agents | LlamaCloud |
How I'd pick on SellerShorts
On SellerShorts, I run our internal docs-QA agent on LlamaIndex because the corpus is 200+ markdown files and the loaders saved real time. I run our competitor-research agent on LangChain because it calls 6 different APIs and the tool-heavy pattern fits LangChain better. Same team, same week, two frameworks, because the problems are different shapes. Treat "which is better" as the wrong question. Treat "is my problem data-heavy or tool-heavy" as the right one.
Try both
Get started with LangChain and LlamaIndex
Both are free open-source. The fastest way to know which fits is to build the same agent in each.
Built an agent on either? List your AI agent on SellerShorts.
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Frequently asked questions
LangChain vs LlamaIndex: which should I pick?
Different shapes. LangChain is a general agent framework (agents + tools + chains + RAG). LlamaIndex started as RAG-first and grew into agents. Pick LangChain when your problem is tool-heavy across many APIs. Pick LlamaIndex when your problem is data-heavy: parsing documents, building indexes, retrieving from large corpora.
Can I use both together?
Yes. Many production teams use LlamaIndex for the data ingestion and retrieval layer, then LangChain for the agent orchestration and tool calling. The two coexist cleanly because LangChain agents can consume LlamaIndex retrievers as tools.
Which has better community support?
LangChain has the largest agent-framework community by raw count. LlamaIndex has 25M+ monthly package downloads, putting it in the same tier. Both have extensive third-party tutorials. LangChain edges out for general agent learning resources; LlamaIndex edges out for RAG-specific guides.
Pricing comparison?
Both OSS frameworks are free. LangChain's paid product is LangSmith (Plus $39/seat/month). LlamaIndex's paid products are LlamaParse and LlamaCloud, tier-based. For a starter project, you pay only for LLM API spend with either.
Which one is faster to prototype with?
Depends on your problem. For a chat agent with a few tools: LangChain prototypes faster. For a RAG agent over 100 PDFs: LlamaIndex prototypes faster because the document loaders and chunking are more polished.
Migration between them?
Not automatic. Conceptually the building blocks are similar (LLM, retriever, agent, tools), so a rewrite takes hours, not weeks. Most teams who migrate end up using both side-by-side rather than fully switching.