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.

Both frameworks have matured significantly since 2023. LangChain passed 90,000 GitHub stars. LlamaIndex is close behind. Production deployments running on both are not toy demos — they're handling millions of queries a month at companies in legal tech, healthcare, fintech, and SaaS. The libraries are no longer experimental. The question is which one fits the shape of what you're building.

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. A sales qualification agent that reads a CRM, sends a personalized email, and logs the outcome in three different systems is exactly the kind of thing LangChain handles cleanly.

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. The community has contributed integrations for everything from Slack and Notion to custom SQL databases and payment processors.

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. You can express a conditional branch — "if the confidence score is below 0.7, retrieve additional context before answering" — in a few lines.

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. A workflow where a research agent gathers information, passes it to a drafting agent, which sends it to a review agent, which loops back if the draft fails a quality check — that's native LangGraph territory.

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

A concrete example of the debugging friction: we once spent three hours tracking down why an agent was making an extra LLM call. The issue was inside a default summarisation buffer memory that was silently triggering. With a more transparent system you'd catch that immediately. With LangChain you sometimes need to enable verbose logging and read through a lot of output.

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. A 12-person law firm that wants to make their entire case archive searchable — 40,000 PDFs across 15 years — would start with LlamaIndex. The ingestion pipeline handles document parsing, chunking, embedding, and storing with minimal boilerplate.

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. Hybrid search alone — combining dense vector search with BM25 sparse search — typically lifts answer accuracy by 10–20% on domain-specific corpora. LlamaIndex makes that a one-line config change.

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. A research assistant that needs to compare findings across 50 clinical trial reports needs different retrieval logic than a simple FAQ bot. LlamaIndex has built-in patterns for both.

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. When a user asks a question and gets a wrong answer, you need to know whether the problem is in chunking, embedding, retrieval, or generation. LlamaIndex's callback system lets you trace that path with minimal instrumentation code.

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

What to Expect in Practice

The framework choice ripples through to timelines, costs, and maintenance burden. Here is what we actually observe across projects:

Initial setup: Both frameworks take roughly the same time to get a prototype running — typically one to three days for a developer who hasn't used either before. The first 20% of functionality comes quickly. The last 20% is where the frameworks diverge sharply.

Retrieval quality tuning: If you're building a RAG system over a proprietary knowledge base, expect two to four weeks of tuning to reach production-grade answer quality regardless of framework. With LlamaIndex, more of that tuning happens through configuration rather than custom code. With LangChain, you'll often write more custom retrieval logic to match what LlamaIndex provides out of the box.

A realistic scenario — a 50-person accounting firm: They want an internal assistant that answers questions about their tax procedures, client engagement letters, and regulatory guidelines. They have 3,000 Word documents and PDFs. LlamaIndex would get them to a working internal demo in about two weeks, with production-quality retrieval achievable in six to eight weeks. LangChain would take longer for the same outcome because the retrieval primitives need more custom work. But if they later want the assistant to auto-draft emails, update their project management system, and pull data from their accounting software — they'd need to add LangChain-style agent logic, or migrate to a combined stack.

A realistic scenario — a B2B SaaS company: They want an AI agent that monitors their customer's usage data, identifies accounts at risk of churning, and automatically schedules a check-in call with the account manager. That's a pure action-and-decision problem, not a document-retrieval problem. LangChain from day one, no question.

Maintenance cost: Both frameworks release frequently. LangChain has historically had more breaking changes between minor versions, which is a real consideration if you have a small team maintaining the system long-term. LlamaIndex has been more stable in this respect. Budget for framework update work — typically a few days per quarter for an active project on either.

What Can Go Wrong

Common mistakes when starting with LangChain:

• Using the default ConversationBufferMemory for production chatbots. It sends the entire conversation history with every call. A session with 100 turns will blow through your context window and cost 10x what it should. Use ConversationSummaryBufferMemory or a custom memory implementation.
• Building complex agent pipelines without enabling LangSmith tracing from the start. Debugging a multi-step agent without trace visibility is painful. Set up tracing before you build, not after the first production issue.
• Assuming the default agent will handle errors gracefully. It won't. Tool failures need explicit retry and fallback logic or the agent stalls.

Common mistakes when starting with LlamaIndex:

• Using the default chunk size without profiling retrieval quality on your actual data. The default 1,024 token chunks work fine for general text but poorly for technical documents, legal text, or structured tables. Tune chunking strategy early.
• Ignoring the re-ranking step. Vector similarity retrieves plausible chunks; a re-ranker filters to the actually relevant ones. Skipping re-ranking in production is one of the most common reasons RAG systems produce confident but wrong answers.
• Over-indexing everything at ingestion without thinking about update frequency. If your document library changes daily, you need an incremental update strategy from the start, not retrofitted after six months of stale data complaints.

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.

If your primary question is "can users get correct answers from their data?", LlamaIndex should be your first choice. If your primary question is "can the AI take the right action based on what it knows?", LangChain should be your first choice.

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.

One development worth noting in 2026: both frameworks now have better support for streaming responses, structured output, and multi-modal inputs (text, images, documents together). These were pain points in 2024. If previous evaluations shaped your team's opinion of either framework, it's worth revisiting. The production experience of both has meaningfully improved.

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.

Related guides

• How to build a RAG chatbot: a step-by-step guide
• Vector databases explained: why AI agents need them
• How to build an AI chatbot with LangChain and OpenAI
• OpenAI Assistants API vs building a custom AI agent
• Our LLM integration services

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.

Frequently Asked Questions

Can I switch from LangChain to LlamaIndex (or vice versa) later without rebuilding everything?

You can, but it's not trivial. The core logic — prompts, business rules, integration points — transfers cleanly. The retrieval pipeline and agent abstractions do not; they need to be re-implemented in the new framework's patterns. A migration typically takes two to four weeks for a mid-sized project. The better approach is choosing the right framework upfront, or designing a clean interface layer between your application logic and the framework so a future switch is contained.

Is LangChain or LlamaIndex better for OpenAI's GPT-4o models?

Both work well with OpenAI models. LangChain has a slightly tighter integration with OpenAI's function-calling and tool-use APIs because that's central to its agent design. LlamaIndex works equally well for retrieval tasks regardless of which LLM backend you choose. If you're using Claude, Gemini, or a local model instead of OpenAI, both frameworks support multiple providers — your framework choice should be driven by application type, not LLM provider.

What does it cost to build a production RAG system using LlamaIndex?

The framework itself is open source and free. The real costs are infrastructure (vector database hosting runs $50–$500/month depending on data volume), LLM API calls (typically $200–$2,000/month for a moderately active internal tool), and development time. A production-ready RAG system for a team of 50 people — ingesting a few thousand documents, with a clean UI — typically runs $25,000–$60,000 in development cost for a first build, with ongoing maintenance of roughly 10–20 hours per month.

How steep is the learning curve for a developer new to both frameworks?

Expect one to two weeks to get comfortable with the basics of either framework, assuming Python proficiency. LangChain's abstractions are slightly harder to internalize at first because there are more of them, but the documentation and community resources are extensive. LlamaIndex is a bit more intuitive if the developer has a data engineering background because the indexing concepts map to familiar patterns. Neither requires prior ML experience to use productively.

Do LangChain and LlamaIndex work with on-premise or self-hosted LLMs?

Yes. Both frameworks support open-source models hosted locally or on private infrastructure — Llama 3, Mistral, Phi, and others via Ollama or vLLM. This is relevant for organizations with data residency requirements or who can't send document contents to external APIs. The caveat: open-source models generally produce lower-quality outputs than GPT-4o or Claude 3.5 for complex reasoning tasks. Plan for more retrieval tuning and prompt engineering to compensate.

Which framework is better for a non-technical team to maintain?

Neither framework is designed for non-technical maintenance — both require a developer to manage updates, debug retrieval issues, and extend functionality. If your team needs something maintainable by non-developers, look at managed AI platforms (Botpress, Cohere Coral, OpenAI Assistants) rather than building on either open-source framework. If you do have a developer available but they're part-time or junior, LlamaIndex's more declarative retrieval configuration tends to be easier to maintain without deep framework expertise.

Is it worth waiting for the frameworks to mature further before building?

No. Both frameworks are mature enough for production use today. Companies across healthcare, legal, financial services, and e-commerce are running high-volume production systems on both. The frameworks will keep evolving, but the core retrieval and agent abstractions are stable. Waiting means delaying the business value of AI tooling by months for marginal technical risk reduction. Build on what's stable today and plan for framework updates as a normal part of maintenance.