The Swiss-Army-vs-chef-knife analogy
A Swiss Army knife has a tool for everything — saw, scissors, can opener, file. Useful when you don't know what you'll need. A chef's knife does one thing extremely well. You bring it because that's the job.
LangChain is the Swiss Army — agents, chains, memory, tools, output parsers, evals, callbacks. LlamaIndex is the chef's knife — pulling in data and making it queryable. They overlap; they aren't substitutes; teams often use both.
What each one is built for
LangChain
- General-purpose agent and chain orchestration.
- Strong abstractions for tool use, memory, prompt templates, output parsers.
- Huge integration catalogue — every model provider, vector DB, doc loader.
- Companion: LangGraph for explicit graph-based agent control flow.
- Companion: LangSmith for tracing, evals, prompt management.
LlamaIndex
- Focused on RAG and data ingestion.
- Best-in-class document loaders, chunkers, indexers, retrievers.
- Rich query primitives — sub-question, recursive retrieval, query routing.
- Strong on agent + RAG composition but less broad on general agentic workflows than LangChain.
- Companion: LlamaCloud / LlamaParse for managed parsing and indexing.
Pick by what you're building
| You want… | Reach for… |
|---|---|
| A chatbot that answers from your documents | LlamaIndex |
| A multi-tool agent with custom workflows | LangChain (or LangGraph) |
| Sophisticated parsing of PDFs, tables, forms | LlamaIndex (LlamaParse) |
| A complex graph of agents, branching, looping | LangGraph |
| Embedding pipelines with non-trivial chunking | LlamaIndex |
| Tracing, eval, prompt experimentation | LangSmith (LangChain ecosystem) |
Where they overlap
Both can do RAG + agents. The difference is centre of gravity:
- LangChain treats RAG as one chain among many.
- LlamaIndex treats agents as a wrapper around RAG primitives.
For a vanilla "ask my docs" chatbot, either works fine. For a 10-tool agent that occasionally retrieves, LangChain feels more natural. For a deeply tuned retrieval pipeline (hybrid search, reranking, query rewriting), LlamaIndex shines.
Why people use both
A common production stack:
- LlamaIndex ingests, chunks, embeds, indexes. Owns the data pipeline.
- LangChain / LangGraph orchestrates the agent that uses that index alongside other tools.
Frameworks compose because they target different layers of the stack.
When to skip both
- Tiny app — one model call, one tool. Direct SDK calls beat framework overhead.
- Unusual control flow the framework's abstractions resist — sometimes "just write the loop" is faster than fitting your code into someone else's chains.
- Hot-path latency obsession — frameworks add wrappers; raw SDKs are tighter.
- Team velocity > flexibility — a thin in-house abstraction over the SDK is sometimes the right answer for a focused product.
Plenty of strong production AI teams ship without these frameworks. Plenty ship with them. Don't be religious either way.
What to evaluate
- Active development. Both move fast; check recent commits, breaking changes, release cadence.
- Docs quality for your use case. The breadth is huge; relevant docs matter more than total volume.
- Integration depth for your stack — your vector DB, your model provider, your tracing tool.
- Escape hatches. Can you drop down to raw SDK when you need to? Both let you, but how cleanly differs.
- Community signal. Issues, Discord activity, real-world examples close to your problem.
In one line
LangChain is the agent framework with a RAG library inside. LlamaIndex is the RAG framework with an agent library inside. Pick by gravity; many teams happily use both.