LangChain vs LlamaIndex: Which Framework Should You Use for Your AI Application?

Two Mature Frameworks With Different Philosophies

LangChain and LlamaIndex are the two most widely used Python frameworks for building LLM-powered applications. Both have been through multiple major versions, both are actively maintained, and both have large communities. Neither is going away.

The "which should I use?" question comes up constantly — in hiring decisions, in architecture reviews, on every kick-off call. We're going to answer it based on what each framework is actually good at, not on which one has the better launch tweets.

LangChain: What It Is and What It Excels At

LangChain started as a framework for chaining LLM calls together — hence the name. It has evolved significantly but kept its identity as a framework for building complex, multi-step LLM workflows.

Core strengths:

Agent and tool use. LangChain has the most mature and flexible abstractions for AI agents — systems that use tools, make decisions, and execute multi-step tasks. The ReAct, OpenAI Functions, and LangGraph agent architectures give you fine-grained control over how agents reason and act.

Ecosystem breadth. LangChain integrates with hundreds of tools, databases, APIs, and services out of the box. If you need to connect to a specific tool, LangChain probably has an integration — and if it doesn't, building one fits cleanly into the existing patterns.

Flexibility. LangChain's LCEL (LangChain Expression Language) lets you compose arbitrarily complex chains of LLM calls, retrievers, tools, and custom logic with clean syntax. Once you've got the hang of it, the composition story is genuinely good.

LangGraph for complex workflows. LangGraph, built on LangChain, provides a graph-based framework for stateful multi-agent workflows — systems where multiple agents interact, share state, and coordinate. It's the strongest option we know of for complex agent orchestration today.

Where LangChain works best:

• AI agents that use multiple tools and take actions
• Complex multi-step workflows with conditional logic
• Applications that need to integrate with many different services
• Multi-agent systems with coordination between agents
• Conversational applications with sophisticated memory management

LangChain's friction points:

• The abstraction layer can be opaque — debugging unexpected behaviour sometimes means peeling back several layers to figure out where exactly things went sideways
• The framework changes rapidly; code written six months ago may need updating, and we've felt this on real projects
• For simple RAG use cases, LangChain can feel over-engineered for what you're trying to do

LlamaIndex: What It Is and What It Excels At

LlamaIndex started as GPT Index — a library specifically for indexing documents and enabling LLM queries over them. It's expanded significantly but kept a strong focus on data ingestion, indexing, and retrieval.

Core strengths:

Data ingestion and indexing. LlamaIndex has the most comprehensive set of data connectors and index types of any framework. Loading data from PDFs, databases, APIs, web pages, email, Notion, Google Drive, and dozens of other sources is straightforward. The index types — vector, tree, list, graph — are well-implemented and performant.

RAG quality. For production RAG systems, LlamaIndex provides more sophisticated retrieval strategies out of the box: hybrid search, re-ranking, recursive retrieval, query decomposition. Getting to high retrieval quality without custom engineering is easier here than in LangChain.

Query engines and data structures. LlamaIndex's query engines give you high-level abstractions for different question-answering patterns — simple retrieval, summarisation over large documents, comparison queries across multiple documents.

Observability. LlamaIndex has strong built-in callback and instrumentation systems that make it easier to understand what's happening inside a retrieval pipeline. This matters more than it sounds.

Where LlamaIndex works best:

• Applications where the primary use case is querying over documents or structured data
• RAG systems where retrieval quality is the main concern
• Knowledge bases, document Q&A systems, research assistants
• Applications ingesting data from many different source types
• Systems that need fine-grained control over the retrieval pipeline

LlamaIndex's friction points:

• Agent capabilities are less mature than LangChain, though improving fast
• Fewer integrations with external tools and APIs
• The abstraction can feel heavy for simple chatbot use cases

Direct Comparison

The Decision Framework

Use LangChain if your application is primarily about agents and actions.

If you're building an AI agent that uses tools, makes decisions, calls APIs, writes to databases, and takes actions in external systems — LangChain is the better choice. Its agent abstractions are more mature, more flexible, and better documented.

If you need multiple agents coordinating — an orchestrator dispatching to specialist sub-agents — LangGraph (built on LangChain) is the strongest available option.

Use LlamaIndex if your application is primarily about querying over data.

If you're building a system that answers questions from documents, knowledge bases, databases, or other structured/unstructured data — LlamaIndex is the better choice. Its retrieval pipeline, indexing strategies, and data connectors will get you to high retrieval quality faster.

Use both when your application involves both.

This is more common than people expect. A production AI system often needs sophisticated document retrieval and agent-like tool use. The frameworks combine cleanly: LlamaIndex for the retrieval layer, LangChain for the agent and tool use layer.

This is architecturally sound and we've shipped it in production. It's not even fancy — it's just using the right tool for each layer.

What About LangChain's Built-In RAG?

LangChain has RAG capabilities — vector store integrations, retrieval chains, document loaders. For simple RAG use cases, they work fine.

For production RAG where retrieval quality is the whole game — where you need hybrid search, re-ranking, query decomposition, and fine-grained control over chunking and indexing — LlamaIndex's retrieval stack is more capable and more mature.

The honest view: LangChain's RAG is enough to get started and good enough for simple applications. LlamaIndex's RAG is better for production systems where retrieval quality determines whether the product is actually useful.

Current State in 2026

Both frameworks have converged somewhat since their early versions. LangChain has improved its data loading and indexing. LlamaIndex has improved its agent capabilities. The gap between them is narrower than it was in 2023.

The underlying philosophies remain different:

• LangChain thinks in chains, agents, and tools
• LlamaIndex thinks in indices, query engines, and retrieval

Those philosophies drive design decisions inside each framework and make them genuinely better fits for different application types. They're not interchangeable, even when they overlap on the surface.

Our Stack

We use both frameworks in production:

LangChain / LangGraph for AI agents — sales qualification agents, support agents, and any system where the agent makes decisions and takes actions across multiple tools.

LlamaIndex for knowledge retrieval systems — RAG-powered chatbots, document Q&A systems, and knowledge bases where retrieval quality is the primary concern.

Both together for complex production systems that need high-quality retrieval feeding into an agent that takes actions based on what was retrieved.

If you want the long answer for your specific situation, that's a conversation we'd genuinely enjoy having.

Talk to us about your application architecture — we'll help you choose the right stack, including telling you when neither framework is what you actually need.