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LangChain vs LlamaIndex - Choosing the Right Framework

April 28, 2026•11 min read•Michael Ridland

If you're picking between LangChain and LlamaIndex right now, you're probably six weeks into a proof of concept and starting to feel the framework choice creak. That's the right moment to read this. Most teams don't think hard enough about the framework decision early, and then have to refactor when the cracks show up in production.

I've built and shipped systems on both. So have our engineers. We've seen what scales, what breaks, what gets refactored out within a year, and which framework dependencies become liabilities. This piece is for technical leads, founders, and engineering managers trying to choose without spending another month evaluating.

Short version: if you're building a general-purpose AI application with agents, multi-step workflows, and lots of tool integrations, LangChain is usually the right call. If your application is primarily about retrieving information from a large document corpus and answering questions over it (a real RAG system), LlamaIndex is often the cleaner choice. The two overlap, and you can mix them, but the centre of gravity is different.

What each framework actually is

This sounds obvious but it's where most teams go wrong. They pick LangChain because they heard about it, then try to do RAG in it, and find themselves wrestling with chains and retrievers when they could have just used LlamaIndex's query engines. Or they pick LlamaIndex for a chatbot and find themselves trying to build agent orchestration on top of a retrieval-focused library.

LangChain is a general-purpose framework for building applications powered by large language models. The core abstractions are around chains (sequences of calls), agents (LLMs that pick tools), prompts, output parsers, and memory. It treats RAG as one pattern among many. LangGraph (their newer orchestration library) has become the production-grade way to build complex agent workflows on top of LangChain.

LlamaIndex started as GPT Index and has always been about connecting LLMs to external data. The core abstractions are around documents, nodes, indexes, retrievers, query engines, and (more recently) agents and workflows. RAG is the central use case, and everything is designed to make retrieval over your data work well.

Both frameworks now do agents. Both do tools. Both do RAG. The differences are about which patterns are first-class and which are bolted on.

When LangChain is the right answer

We pick LangChain (often with LangGraph) when the project looks like this:

  • The application orchestrates multiple LLM calls, tools, and external services
  • We need agent behaviour - the LLM decides what to do next, in what order
  • The use case involves integrations with many external APIs (CRM, email, calendars, internal systems)
  • The team is comfortable with Python or JavaScript framework idioms
  • Production reliability and observability are critical (LangSmith helps here)
  • The application will need human-in-the-loop checkpoints

Example: a few months ago we built a system for an Australian professional services firm that processes incoming client documents, classifies them, extracts key data, runs validations against internal policy, and routes them to the right team member with a draft response. That system has 12 different tools, 4 conditional branches, and 2 human review checkpoints. We built it on LangGraph because we needed proper state management and the ability to pause, resume, and replay.

LangChain is also generally easier to hire for. The community is larger, the documentation is denser, and the ecosystem of integrations is bigger. If you want a vector store, document loader, or LLM adapter, LangChain probably has it. Our LangChain consultants work with this framework day-to-day for production builds.

When LlamaIndex is the right answer

LlamaIndex is the better fit when the project looks like this:

  • The core function is "answer questions over a big pile of documents"
  • The retrieval quality is the #1 success metric
  • You need advanced indexing strategies (hierarchical, recursive, hybrid)
  • The team values clean abstractions for ingestion, parsing, and chunking
  • You're not doing heavy agent orchestration with many tools

Example: an Australian legal services client needed to answer complex questions across 40,000 PDF documents (contracts, case files, regulatory submissions). LlamaIndex was the right pick. The PDF parsing (with LlamaParse), the recursive node retrieval, and the query engine abstractions gave us a head start of weeks. Trying to build the same retrieval quality in pure LangChain would have meant writing a lot more glue code.

LlamaIndex also tends to be the better choice when you're indexing structured and semi-structured data alongside unstructured documents. SQL-aware query engines, knowledge graph index, and structured data extractors are first-class in LlamaIndex.

A practical comparison table

Concern LangChain LlamaIndex
Agent orchestration Strong (especially LangGraph) Decent (Workflows) but less mature
RAG retrieval quality Good with effort Excellent out of the box
Document ingestion and parsing Functional via loaders Best in class (especially LlamaParse)
Tool integrations Vast ecosystem Growing but smaller
Observability tooling LangSmith is mature OpenTelemetry-based, less polished
Production readiness LangGraph is production-grade Workflows are improving
Learning curve Steep, lots of concepts Gentler if you stay in the RAG path
Community size Larger Smaller but active
Multi-modal support Good Good
Stability of API Has stabilised significantly Has stabilised, fewer breaking changes recently
Best language Python (JavaScript is solid) Python (TypeScript exists but lags)

What about Semantic Kernel, Haystack, and the Microsoft AI Agent Framework?

The question we get next is always about alternatives. A quick view:

Semantic Kernel is Microsoft's framework, more enterprise-leaning, with strong .NET support. If you're a .NET shop targeting Azure, it's a serious contender. Often pairs with Azure AI Foundry.

Microsoft AI Agent Framework is the newer Microsoft offering that consolidates Semantic Kernel and AutoGen patterns. We use this with Microsoft-heavy clients where governance and Azure integration matter more than ecosystem breadth.

Haystack by deepset is good for search-focused applications, especially when you want strong control over the retrieval pipeline. Smaller community than LangChain.

CrewAI and AutoGen are agent-specific frameworks. Worth looking at if your project is fundamentally multi-agent. But they're more specialised than LangChain.

For Australian businesses that want to stay close to Microsoft's stack, Semantic Kernel or the new agent framework is often the practical choice. For everyone else, LangChain vs LlamaIndex is the real decision.

The cost angle - what does this actually mean for your build budget?

Framework choice affects build cost, not in the obvious way (the frameworks are free), but in how much glue code you have to write.

For a typical mid-sized Australian build (a single AI application with RAG and some agent behaviour), our rough estimates:

  • Simple RAG chatbot on LlamaIndex: AUD $35,000 - $70,000
  • Same chatbot built in LangChain: AUD $45,000 - $85,000 (because you write more retrieval code)
  • Multi-tool agent on LangChain with LangGraph: AUD $80,000 - $180,000
  • Same agent on LlamaIndex: AUD $90,000 - $200,000 (because you write more orchestration code)
  • Production deployment, monitoring, evals: add AUD $20,000 - $50,000 either way

These are ballpark figures. Real costs depend on data complexity, integration count, security requirements, and how mature your internal team is.

The point is this: the framework choice can swing the build cost by 15-25%, but the data, integrations, and evaluation work cost more than the framework choice. Don't fixate on framework selection at the expense of the harder problems.

What gets refactored out

Here's what we see most often when we inherit a project from another team.

When projects start in LangChain and get refactored, it's usually because the team wrote everything as chains and now needs more flexible control flow. The fix is usually to rebuild with LangGraph, which is a moderate effort but worth it.

When projects start in LlamaIndex and get refactored, it's usually because the team needed to add many tools and external integrations and found themselves writing too much custom code around the framework. The fix is sometimes to migrate to LangChain, but more often to keep LlamaIndex for retrieval and add LangGraph around it for orchestration.

The hybrid pattern (LlamaIndex for retrieval, LangGraph for orchestration) is increasingly common in production systems. We've shipped several builds on this combination and it tends to be stable. The conceptual cost is that engineers have to understand both frameworks, but the engineering payoff is worth it.

How the choice plays out in production

This is the part most evaluations miss. Both frameworks look fine in a notebook. The differences appear when you put them under load.

Observability. LangSmith (the paid LangChain observability product) is mature. You can trace agent runs, see token usage, debug prompt regressions, and run evaluations. It's a real production tool. LlamaIndex's observability is OpenTelemetry-based and works, but you'll do more setup work.

Deployment. Both frameworks deploy fine on Azure Container Apps, AWS ECS, or Kubernetes. Neither has strong opinions about deployment. LangGraph offers LangGraph Cloud (a managed deployment) which is convenient but adds vendor lock-in.

Versioning and breaking changes. Both frameworks have stabilised in 2026 compared to the chaotic 2023-2024 period. Breaking changes are now rare and well-communicated. But both still move fast, and you should pin versions and test upgrades carefully.

Cost monitoring. Both expose token usage well. Neither does cost forecasting natively - you'll layer that on with your own dashboards or use a tool like Helicone or Langfuse.

Common mistakes we see

Picking the framework before the architecture. Teams pick LangChain because of community hype, then realise their actual problem is RAG over 100,000 documents. Match the framework to the problem, not the fashion.

Treating frameworks as exclusive. You can use LlamaIndex for document parsing and indexing, then expose those indexes as tools to a LangGraph agent. That's a perfectly valid architecture and we've shipped it more than once.

Skipping the eval framework. Both LangChain and LlamaIndex have eval tooling. Use it from day one. The number of AI projects we've inherited where there's no evaluation harness at all is depressing.

Custom vector store glue. Both frameworks integrate with every serious vector database (Pinecone, Weaviate, Qdrant, pgvector, Azure AI Search, etc.). If you're writing custom code to talk to a vector store, you've probably gone wrong somewhere.

Overusing agents. Just because LangGraph makes it easy to build agents doesn't mean every workflow needs to be an agent. A deterministic pipeline with one LLM call at the end is often more reliable and cheaper than a fully agentic system.

Decision checklist

If you're trying to pick right now, run through these questions honestly:

  1. What's the dominant pattern? Retrieval over many documents, or orchestration of many tools?

  2. How much agent behaviour does it really need? Sometimes "the LLM decides" is a fancy way of avoiding hard product decisions.

  3. What's your team's Python depth? Both frameworks need real Python skill in production. LlamaIndex is gentler if you stay in the RAG path. LangChain has more concepts to learn.

  4. What's the Microsoft alignment? If your stack is Azure AI Foundry and Microsoft 365, Semantic Kernel or the Microsoft AI Agent Framework might fit better than either of these.

  5. What's the production timeline? LangGraph is more proven at production scale today. LlamaIndex Workflows are catching up but have less battle-testing.

  6. What's the support story? LangChain has paid offerings (LangSmith, LangGraph Cloud) that smooth production operations. LlamaIndex has LlamaCloud for managed parsing and ingestion. Both have free open-source paths.

What we recommend for Australian businesses

For most Australian businesses building their first serious AI application in 2026:

  • Document-heavy use case (legal, research, healthcare records, knowledge base): start with LlamaIndex
  • Agent or workflow use case (customer service automation, internal operations agents): start with LangChain plus LangGraph
  • Microsoft-aligned with Azure AI Foundry: consider Microsoft AI Agent Framework instead
  • Multi-modal or specialised: pick based on which framework better supports the specific modality

Don't agonise about reversibility. Both frameworks have similar underlying patterns. If you build cleanly, with a thin service layer in front of the framework, swapping is painful but doable. We've migrated production systems between frameworks before. It's a project but not a catastrophe.

Where we sit on this

We build AI applications for Australian businesses. We've shipped on both frameworks. We don't have a religious view on which is better, because the question doesn't have a single answer. The right framework depends on what you're building, who's building it, and where it has to run.

If you want a straight read on which framework fits your project, we can help. Our AI agent developers build production systems on LangChain, LlamaIndex, and the Microsoft AI Agent Framework, and we can give you an honest view on which is the right pick for your specific situation.

If you're earlier in the process and not yet sure what you're building, our AI strategy consultants help shape the use case before the framework decision matters.

Get in touch via the contact page for a no-cost initial conversation. We'll ask the questions that actually matter and tell you straight whether you should be picking between these two frameworks at all, or whether the right answer is something else entirely.