Can LangChain and LlamaIndex be used together?

Yes. A common pattern uses LlamaIndex for data ingestion, indexing and retrieval, exposing it as a tool or retriever inside a LangChain-based workflow or agent. This combines LlamaIndex's retrieval strengths with LangChain's orchestration, though it adds dependencies that the team must keep updated.

Do I need a framework to build an LLM application?

Not necessarily. Simple applications can call model APIs directly with a vector database and a few hundred lines of code, which keeps behavior transparent. Frameworks help with complex retrieval, many integrations or multi-step agents. Choose based on complexity and how much control and visibility your team needs.

Which framework is better for beginners?

LlamaIndex is often easier for beginners building document question answering, because its defaults produce a working retrieval pipeline quickly. LangChain covers more ground and therefore has more concepts to learn. For learning fundamentals, building a small RAG pipeline without frameworks first is also valuable.

Are these frameworks production ready?

Both are used in production by many teams. Success depends on pinning versions, testing upgrades, adding evaluation and monitoring, and understanding the prompts and calls the framework generates. Treat them like any dependency: valuable when well understood, risky when used as a black box.

LangChain vs LlamaIndex: LLM Framework Comparison

Quick verdict

LangChain is a broad framework for building LLM applications, with components for prompts, tools, agents and many integrations, plus LangGraph for stateful agent workflows and LangSmith for tracing and evaluation. LlamaIndex focuses on connecting language models to your data, with strong ingestion, indexing and retrieval for RAG. Choose LangChain for varied workflows and agents; LlamaIndex for data-heavy retrieval applications.

LangChain aims to be a general toolkit for LLM applications: prompt templates, tool calling, memory and agents, with LangGraph handling stateful orchestration and a large catalog of integrations, including tools exposed through the Model Context Protocol (MCP). LlamaIndex concentrates on the data side: loading documents from many sources, chunking, indexing and retrieving the right context. Many teams use one, some use both, and some use neither once requirements become clear.

LangChain vs LlamaIndex, side by side

Criterion	LangChain	LlamaIndex
Primary focus	General LLM application and agent building	Data ingestion, indexing and retrieval for RAG
RAG support	Good, built from loaders, retrievers and vector store integrations	Excellent, with many indexing and query strategies
Agents	Strong, with LangGraph for graph-based, stateful agent orchestration	Supported through agents and event-driven workflows, often centered on data and retrieval tasks
Integrations	Very large catalog of models, tools and stores	Large catalog of data connectors and stores
Abstraction level	Many abstractions; flexible but can feel heavy	Focused abstractions around data and queries
Observability	LangSmith tracing and evaluation, plus third-party tools	Integrations with tracing and evaluation tools
Learning curve	Broad API surface to learn	Easier for retrieval-focused use cases
Best fit	Multi-step workflows, agents, tool-heavy applications	Document Q&A, knowledge assistants, complex retrieval

Choose LangChain when

Your application orchestrates several steps, tools and model calls.
You are building agents that need structured, stateful workflows.
You want access to a wide range of model and tool integrations.
Your team values flexibility across many kinds of LLM features.

Choose LlamaIndex when

Your main challenge is retrieving the right information from many documents.
You need advanced indexing, chunking and query strategies.
Data comes from varied sources such as PDFs, wikis, databases and APIs.
You want a focused framework for knowledge assistants and search.

Strengths for RAG and agents

For retrieval-augmented generation, LlamaIndex offers many ready-made strategies: hierarchical indexes, recursive retrieval, query routing across sources and response synthesis options. Teams with large, varied document collections often reach good retrieval quality faster with these building blocks than by assembling them manually.

LangChain's strength lies in orchestration. Its tool calling, agent abstractions and LangGraph's graph-based workflows help teams build AI agents that plan steps, call APIs and MCP tools, pause for human approval and maintain state across longer tasks. Combined with tracing and evaluation tools, it supports debugging complex multi-step behavior. Both frameworks can now consume tools exposed through the Model Context Protocol, which reduces lock-in at the integration layer.

Frameworks versus writing your own code

Frameworks speed up prototypes, but abstractions can hide what is sent to models and make debugging harder. Rapid changes in the LLM ecosystem also mean framework APIs evolve frequently. Some production teams use frameworks for ingestion or orchestration while writing core prompts, retrieval logic and model calls directly for clarity and control.

Whichever path you choose, invest in evaluation sets, tracing and clear interfaces between components, so you can swap models, retrievers or frameworks without rewriting the application. Our LLM development teams choose tools per project, sometimes combining LlamaIndex retrieval with LangChain-based orchestration.

Final verdict

Choose LlamaIndex when your application is primarily about getting the right information from large, varied data sources, such as knowledge assistants and document search. Choose LangChain when you need flexible orchestration of tools, workflows and agents across many integrations. For production systems, keep your architecture modular and evaluation-driven, so framework choices remain replaceable as the fast-moving ecosystem continues to evolve.

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LangChain vs LlamaIndex: Which LLM Framework?