AI frameworks

LangChain vs LlamaIndex: choose the right framework for RAG and agents

Compare LangChain and LlamaIndex for RAG, agents, document ingestion, retrieval workflows, orchestration, evaluation, observability, and production architecture.

Updated 2026-06-119 min readIntermediate

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Best for

Developers choosing a RAG or agent framework
Teams migrating from prototypes to production LLM apps
Builders comparing document-first and orchestration-first stacks
Readers deciding whether to use LangChain, LlamaIndex, or both

Not for

A live benchmark of every framework release
A replacement for testing your own documents and workflows
Teams that have not defined data ingestion, evals, and deployment boundaries

Comparison

Choose by workflow, not brand

Option	Best for	Strengths	Tradeoffs	Use when
LangChain	Broad LLM application building, agents, model/tool integrations, and orchestration paths	Large integration ecosystem and a path from simple chains to LangGraph and LangSmith.	Can become complex if the app really only needs document retrieval.	Your workflow is agent-heavy, tool-heavy, or spans many model providers and external systems.
LlamaIndex	Document ingestion, indexing, retrieval, query engines, and agentic RAG over private data	Strong document and retrieval abstractions with a clear RAG mental model.	May not be the only orchestration layer you need for complex multi-agent state machines.	Your primary problem is turning messy documents into reliable knowledge workflows.
Both together	Teams with document-heavy retrieval plus broader application orchestration needs	Lets each framework do what it is good at.	Adds integration overhead and unclear ownership if boundaries are loose.	You define one layer for retrieval and one layer for orchestration, with tests around the boundary.

Choose by center of gravity

The question is not which framework is more popular. The question is whether your hardest problem is orchestration or data. Orchestration-heavy apps need tool routing, state, retries, and agent loops. Data-heavy apps need ingestion, chunking, metadata, indexing, and evidence quality.

Start with LlamaIndex if document processing and retrieval are the core risk.
Start with LangChain if the core risk is agent orchestration and integrations.
Keep framework boundaries explicit so a prototype does not become a hard-to-debug knot.

Prototype comparison that actually works

Build the same small RAG app twice: ingest five representative documents, answer twenty real questions, log retrieved evidence, and compare answer faithfulness. You will learn more from one fair test than from many abstract debates.

Use the same embedding model, chunking strategy, and vector database.
Compare developer time, retrieved evidence, latency, and debugging experience.
Keep the winning prototype only if it also has an eval path.

Production architecture warning

Frameworks help, but they do not remove product requirements: data deletion, tenant isolation, prompt versioning, evaluation, cost monitoring, and incident debugging still need first-class design.

Do not hide retrieval quality behind a single framework abstraction.
Log document IDs, chunk IDs, model routes, and prompt versions.
Treat framework upgrades like dependency migrations with regression tests.

Decision Rules

A practical checklist

Pick LlamaIndex first for document ingestion, indexing, and retrieval-heavy products.

Pick LangChain first for agent workflows, tool integrations, and orchestration-heavy products.

Use both only when the boundary is clear and tested.

Evaluate retrieval quality before arguing about framework preference.

Related Guides

Continue the decision path

Read RAG chunk size guide

Tune retrieval inputs before choosing framework abstractions.

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Read RAG evaluation guide

Measure retrieval quality before standardizing on a framework.

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RAG chunk size guide

Choose chunking defaults before comparing frameworks.

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RAG evaluation guide

Build a test set for retrieval and answer quality.

Open

Vector database comparison

Choose retrieval infrastructure after framework selection.

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Chinese Archive

Aligned deeper reading

LangChain zero-to-one archive

Chinese LangChain tutorials and workflow notes.

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Dify and knowledge-base archive

Chinese RAG and knowledge-base workflow materials.

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Topic Hubs

Explore the wider search cluster

Topic hub

Coding agents

Compare AI coding agents, repo-aware developer tools, app builders, agent frameworks, MCP servers, workflow automation, and practical engineering adoption paths.

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FAQ

Common questions

Is LangChain better than LlamaIndex?

Not universally. LangChain often fits broader agent and orchestration work. LlamaIndex often fits document-first RAG and retrieval workflows. The best choice depends on your hardest production problem.

Can I use LangChain and LlamaIndex together?

Yes, but define a clear boundary. For example, use LlamaIndex for document ingestion and retrieval, then use LangChain or LangGraph for orchestration and tool workflows.

What should I test before choosing?

Test ingestion, retrieval evidence, answer faithfulness, latency, debugging, versioning, and how easily the team can add evaluations.

Source Links

Primary references used for this guide

Reference

LangChain overview

Official LangChain documentation overview.

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Reference

LlamaIndex RAG introduction

Official LlamaIndex explanation of RAG concepts.

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Reference

LlamaIndex framework docs

Official LlamaIndex framework documentation.

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Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

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