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LangChain vs LlamaIndex - Choosing the Right Framework

April 24, 2026•9 min read•Michael Ridland

LangChain and LlamaIndex are the two most popular Python frameworks for building LLM-powered applications. They're often mentioned in the same breath, but they solve different problems and suit different use cases. Picking the wrong one costs time, money, and team morale.

We've shipped production applications on both frameworks. Here's an honest comparison based on what we've seen work - and not work - in real Australian enterprise deployments.

The Fundamental Difference

LlamaIndex is a data framework. Its core strength is connecting LLMs to your data - indexing, retrieval, and query engines. If your primary problem is "I need an AI that can answer questions about my documents," LlamaIndex was purpose-built for that.

LangChain is an application framework. Its core strength is orchestrating LLM-powered workflows - chaining together prompts, tools, memory, and retrieval into complex applications. If your primary problem is "I need to build an AI application that does multiple things," LangChain provides the orchestration layer.

The overlap between them is RAG (Retrieval Augmented Generation). Both frameworks can build RAG applications. But they approach it from different directions, and that matters.

Side-by-Side Comparison

Feature LangChain LlamaIndex
Primary Focus Application orchestration Data indexing and retrieval
Best For Multi-step agents, complex workflows Document QA, knowledge bases
RAG Support Strong, flexible Excellent, purpose-built
Agent Framework LangGraph (mature) Workflows (newer)
Indexing Strategies Basic, relies on integrations Advanced, many built-in options
Query Engine Manual construction Built-in query engines
Learning Curve Steeper, more abstractions Moderate, more focused
Community Size Larger Large, growing fast
Production Tooling LangSmith (observability) LlamaCloud, LlamaParse
Flexibility Very high High within its domain
Enterprise Adoption Widespread Growing

When LlamaIndex Is the Better Choice

Your primary use case is document question-answering

If you're building a system where users ask questions and get answers from a document collection - and that's the core of what you need - LlamaIndex will get you there faster and with better out-of-the-box retrieval quality.

LlamaIndex was designed specifically for this problem. Its indexing strategies (tree index, keyword table index, vector store index, knowledge graph index) give you more options for how to structure and retrieve your data. With LangChain, you're mostly working with vector similarity search and need to build additional retrieval strategies yourself.

One client came to us with a legal document search application. They'd started with LangChain but were struggling with retrieval quality across a corpus of 50,000 contracts. We rebuilt the retrieval layer using LlamaIndex's recursive retrieval with document summaries, and answer accuracy went from 65% to 88% without changing the LLM or the prompts.

You have complex document structures

LlamaIndex handles structured and semi-structured documents better out of the box. If your documents include tables, nested sections, hierarchical information, or mixed content types, LlamaIndex's node parsing and indexing strategies give you more control over how that structure is preserved and used during retrieval.

LlamaParse, their document parsing service, is particularly good at extracting structured data from PDFs - including tables, which are notoriously difficult to handle. For organisations with large volumes of complex documents (financial reports, technical specifications, legal contracts), this is a significant advantage.

You want simpler RAG with less boilerplate

For a straightforward RAG application, LlamaIndex requires less code. The high-level abstractions (VectorStoreIndex, query engine, response synthesiser) handle common patterns with minimal configuration. LangChain requires more explicit chain construction.

# LlamaIndex - basic RAG in ~10 lines
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is our refund policy?")

For proof of concepts and internal tools where speed to first demo matters, this simplicity is valuable.

You need advanced retrieval patterns

LlamaIndex offers retrieval patterns that LangChain doesn't provide out of the box:

  • Recursive retrieval: Start with document summaries, then drill into relevant sections
  • Auto-merging retrieval: Dynamically merge small chunks into larger parent chunks when multiple children are retrieved
  • Knowledge graph retrieval: Build and query knowledge graphs from your documents
  • Multi-document agents: Route queries to document-specific agents that understand each document's structure

If your retrieval requirements are sophisticated, LlamaIndex saves you from building these patterns from scratch.

When LangChain Is the Better Choice

You're building more than a RAG application

If your application involves multi-step workflows, tool use, decision-making, or interactions with external systems - not just document retrieval - LangChain is the stronger choice.

For example, an AI agent that:

  1. Takes a customer enquiry
  2. Searches the knowledge base for relevant information
  3. Checks the CRM for customer history
  4. Determines if a refund is appropriate
  5. Processes the refund via an API
  6. Sends a confirmation email

This requires orchestration across multiple steps and tools. LangGraph (LangChain's agent framework) is purpose-built for this kind of workflow. LlamaIndex can do some of this with its Workflows module, but it's newer and less battle-tested.

You need agent capabilities

LangGraph is currently the most mature framework for building production AI agents in Python. It provides:

  • State management across agent steps
  • Human-in-the-loop patterns
  • Parallel tool execution
  • Conditional branching
  • Persistence and resumability

If you're building AI agents that need to reason, plan, and take actions, LangGraph gives you the most control and the most production-tested patterns.

You want maximum flexibility

LangChain's lower-level abstractions give you more control over every part of the pipeline. You can customise prompts, chain logic, retrieval strategies, and output parsing to a degree that LlamaIndex's higher-level abstractions don't always support.

For production applications where you need fine-grained control over behaviour, this flexibility matters. You might need custom retry logic, conditional prompting based on retrieval results, or application-specific output formatting. LangChain makes this straightforward.

You're integrating with many external tools

LangChain has a larger ecosystem of tool integrations - databases, APIs, SaaS platforms, communication tools. If your application needs to connect to multiple external systems, LangChain's integration library saves you from building those connections yourself.

Your team is already using LangChain

If your team has LangChain experience and existing LangChain code in production, stick with it unless there's a strong reason to switch. The migration cost and learning curve of switching frameworks mid-project is almost never worth it.

Can You Use Both Together

Yes, and this is more common than people think. The frameworks are not mutually exclusive.

A practical pattern we use:

  • LlamaIndex for document indexing and retrieval (it's genuinely better at this)
  • LangChain/LangGraph for application orchestration and agent logic

LlamaIndex has a LangChain integration that lets you use LlamaIndex query engines as LangChain retrievers. This gives you the best of both worlds: LlamaIndex's superior indexing and retrieval with LangChain's orchestration capabilities.

from llama_index.core import VectorStoreIndex
from langchain.agents import AgentExecutor

# Build index with LlamaIndex
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Use as a tool in a LangChain agent
from llama_index.core.langchain_helpers.agents import (
    IndexToolConfig,
    LlamaIndexTool,
)

tool_config = IndexToolConfig(
    query_engine=query_engine,
    name="knowledge_base",
    description="Search the company knowledge base",
)
tool = LlamaIndexTool.from_tool_config(tool_config)

We've used this pattern in several production deployments. It works well, though it does add complexity to your dependency management and testing.

Decision Framework

Use this flowchart to guide your choice:

1. What's your primary use case?

  • Document QA / knowledge base --> Lean LlamaIndex
  • Multi-step agent / complex workflow --> Lean LangChain
  • Both --> Consider using both, or start with LangChain

2. How complex are your documents?

  • Highly structured (tables, nested sections) --> Lean LlamaIndex
  • Mostly plain text --> Either works

3. How sophisticated is your retrieval?

  • Need advanced patterns (recursive, auto-merge, KG) --> LlamaIndex
  • Standard vector + keyword search --> Either works

4. Do you need agent capabilities?

  • Yes, production-grade agents --> LangChain/LangGraph
  • No, just retrieval and generation --> Either works

5. What does your team know?

  • Already using one framework --> Stick with it unless there's a strong reason to switch

Cost Implications

Both frameworks are open source and free to use. The cost differences come from:

Development time: LlamaIndex gets RAG applications running faster. LangChain requires more setup but gives more flexibility for complex applications. For a simple RAG app, expect 20-30% faster development with LlamaIndex. For a complex agent application, expect 30-50% faster development with LangChain.

Managed services: LlamaIndex offers LlamaCloud (managed indexing and retrieval) and LlamaParse (document parsing). LangChain offers LangSmith (observability). Both are optional paid services. Budget $300-$1,000 AUD/month for either if you choose to use them.

Ongoing maintenance: LangChain's broader scope means more moving parts to maintain. LlamaIndex's focused scope means less maintenance for RAG-specific applications.

For a typical enterprise RAG application in Australia, total build cost ranges from $80,000-$250,000 AUD regardless of framework choice. The framework decision affects development speed and team productivity more than direct cost.

What We Recommend for Most Australian Businesses

For most of our Australian clients, we recommend starting with LangChain for these reasons:

  1. Broader applicability: Most projects grow beyond pure document QA. LangChain gives you room to expand without switching frameworks.
  2. Agent capability: Nearly every client eventually wants agent-like features - workflow automation, tool use, multi-step reasoning. LangGraph is ready for that.
  3. Ecosystem: The LangChain community is larger, which means more documentation, examples, and developers available for hire.

However, we use LlamaIndex when the project is specifically and primarily about document retrieval - particularly when document complexity is high and retrieval quality is the primary concern.

And we use both together when a project needs best-in-class retrieval (LlamaIndex) within a broader application framework (LangChain).

How Team 400 Can Help

We're LangChain consultants and LlamaIndex practitioners based in Brisbane, working with businesses across Australia. We've built production applications on both frameworks and can advise on which fits your specific situation.

Our typical approach is a two-week discovery engagement where we assess your use case, data, and team capabilities, then recommend the right framework and architecture. If it makes sense, we build a proof of concept using your actual data so you can see the difference before committing.

If you're weighing up LangChain vs LlamaIndex for your project, reach out to our team. We'll give you an honest recommendation based on what we've seen work. Learn more about our AI consulting services or explore our full service offerings.