LangChain vs LlamaIndex: Which Framework Fits Your AI Application?

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.

Getting that wrong is expensive in a quiet way. Pick a framework whose strengths don't match your application and you spend weeks writing custom code to recreate what the other one does out of the box, or you migrate halfway through a project. This comparison covers what each framework is for, where each excels and causes friction, a side-by-side table, realistic timelines, the mistakes teams make when starting with each, how to run a short bake-off on your own data, and a decision framework you can apply on your next kickoff call.

LangChain: What It Is and What It Excels At

LangChain started as a framework for chaining LLM calls together — hence the name. (Its GitHub repository and linked docs are the best place to check current APIs, since they change quickly.) 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. The project's GitHub repository tracks its current integrations and release notes. 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

Benefits of Using LangChain or LlamaIndex

A thin layer over a model provider's SDK is sometimes enough, as the FAQ notes. When an application grows past that, either framework brings advantages that would otherwise take weeks to build and much longer to harden. The benefits below apply to both, with differences in emphasis noted where they matter.

Integrations You Do Not Have to Write

Both frameworks ship connectors for document sources, vector databases, model providers, and external tools. LangChain's ecosystem is broadest for tools and services; LlamaIndex's connectors cover a wide range of data sources. Each connector you do not write is code you do not have to test, secure, and maintain, which matters most for small teams supporting a system long after launch.

Proven Retrieval Patterns

Hybrid search, re-ranking, recursive retrieval, and query decomposition are well understood techniques, but implementing them correctly takes time. LlamaIndex in particular offers them as configuration, and LangChain provides the building blocks. Teams start from patterns that have been tested across many projects rather than reinventing retrieval logic and discovering its edge cases in production.

Structured Agent Orchestration

Multi-step agents need state, tool calling, error handling, and sometimes coordination between several agents. LangGraph gives that structure explicitly, as a graph of steps with shared state. Without it, teams tend to grow ad hoc loops that become hard to reason about once conditional branches, retries, and human approval steps are added.

Visibility Into What the System Did

LangSmith tracing and LlamaIndex's callback system show each step a request took: which chunks were retrieved, which tools were called, what the model saw. That visibility shortens debugging dramatically when an answer is wrong, and it gives teams the data they need to improve retrieval and prompts systematically.

A Large Community and Shared Knowledge

Both frameworks have large user bases, so common problems usually have documented answers, examples, and community-built integrations. New team members can often learn the framework from public material, which reduces the knowledge risk of a custom in-house abstraction that only its author understands. Hiring is easier too, since experience with either framework is common among developers building LLM applications.

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.

LangChain and LlamaIndex Use Cases

The two scenarios above generalise. These are the application types where each framework, or the combination, tends to fit best in production.

Internal Knowledge Assistants

Teams want staff to get correct answers from policies, procedures, and past work without searching shared drives. The problem is retrieval quality across many document types. LlamaIndex handles ingestion, chunking, and hybrid retrieval with little custom code, and its query engines support summaries and comparisons across documents. The outcome is an assistant that cites sources, with tuning effort spent on configuration rather than retrieval plumbing.

Customer Support and Sales Agents

A support or sales agent has to look up records, decide what to do, and act: issue a refund, update the CRM, book a meeting. That is an action-and-decision problem. LangChain's agent abstractions and integrations fit it naturally, with LangGraph adding explicit control over steps, retries, and escalation to a human when the agent is unsure or the action is risky.

Multi-Agent Research and Drafting Workflows

Some workflows split naturally into roles: one agent gathers information, another drafts, a third reviews and sends work back if it fails a quality check. LangGraph models these loops as a graph with shared state, which makes the flow inspectable and testable. Teams use it for report drafting, proposal preparation, and similar pipelines where quality checks need to be explicit rather than hoped for.

Document Comparison and Analysis

Comparing findings across many reports, contracts, or filings needs retrieval logic beyond simple top-k similarity. LlamaIndex's query decomposition and recursive retrieval break a comparison question into parts, retrieve for each, and assemble the result. Legal, research, and compliance teams use this pattern where answers depend on several documents at once.

Assistants That Retrieve and Then Act

Many production systems need both: retrieve the right policy or record, then act on it. The common architecture puts LlamaIndex in the retrieval layer and a LangChain or LangGraph agent above it, calling retrieval as one of its tools. Each framework does what it is best at, and the boundary between them stays clear for testing and upgrades over time.

Common LangChain and LlamaIndex Mistakes

Most problems teams hit with either framework come from trusting defaults that were designed for demos. The first three are LangChain mistakes; the next three are LlamaIndex mistakes. All six are easy to fix early and painful to fix late.

Shipping LangChain's Default Buffer Memory

Using the default ConversationBufferMemory for production chatbots is a common trap. 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 Agents Without Tracing

Building complex agent pipelines without enabling LangSmith tracing from the start makes every bug a guessing game. Debugging a multi-step agent without trace visibility is painful. Set up tracing before you build, not after the first production issue.

Assuming Agents Handle Tool Failures

The default agent will not handle errors gracefully. Tool failures need explicit retry and fallback logic or the agent stalls, sometimes silently, leaving the user waiting on a task that will never finish.

Keeping LlamaIndex's Default Chunk Size

Using the default chunk size without profiling retrieval quality on your actual data is the most common LlamaIndex mistake. 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.

Skipping Re-Ranking

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.

Indexing Without an Update Strategy

Over-indexing everything at ingestion without thinking about update frequency creates stale answers later. If your document library changes daily, you need an incremental update strategy from the start, not retrofitted after six months of stale data complaints from users.

LangChain and LlamaIndex Best Practices

Whichever framework you choose, these habits keep a project maintainable after the prototype stage, when the original developer may have moved on and the system has real users depending on it.

• Pick by application shape, not popularity. Write one sentence describing whether the system mainly answers questions from data or mainly takes actions. That sentence usually decides the framework, and it is worth revisiting if the product's direction changes.
• Hold infrastructure constant when comparing. Use the same model, embeddings, and vector store for any comparison, so you are evaluating the framework rather than the stack around it.
• Build an evaluation set before tuning. Thirty to fifty real questions, including unanswerable ones and action requests, graded blind by a domain expert. Every chunking, retrieval, or prompt change gets measured against it.
• Turn on tracing on day one. LangSmith for LangChain, callbacks and instrumentation for LlamaIndex. When an answer is wrong, you need to see whether chunking, retrieval, or generation failed.
• Wrap the framework behind your own interface. Keep prompts, business rules, and integration points in your code, and call the framework through a thin layer. Upgrades and a possible future switch then stay contained.
• Pin versions and schedule upgrades. Both frameworks release frequently. Pin dependencies, read release notes, and budget a few days each quarter for updates rather than upgrading under pressure.
• Treat defaults as starting points. Memory strategy, chunk size, retrieval top-k, and agent error handling all need production settings chosen against your own data and traffic.
• Combine frameworks deliberately. If you use both, give each a clear layer: LlamaIndex for retrieval, LangChain or LangGraph for orchestration, with a defined interface between them.
• Revisit the choice when the product changes. A knowledge assistant that starts taking actions, or an agent that comes to depend mostly on retrieval, may need a different balance of frameworks. Review the architecture at major product milestones rather than letting it drift.

How to Run a One-Week Bake-Off on Your Own Data

If the decision framework below doesn't settle it, a short, structured comparison will. It's cheaper than discovering the mismatch three months in.

1. Write 30–50 real test questions. Pull them from support tickets, internal Slack threads, or the people who will use the system. Include the hard ones: multi-document comparisons, questions with no answer in the corpus, and questions that require an action.
2. Use the same model, embeddings, and vector store for both. Otherwise you're comparing infrastructure, not frameworks.
3. Build the thinnest working version in each. One day per framework is usually enough for a basic retrieval pipeline or a single-tool agent.
4. Score answers blind. Have a domain expert grade correctness and citation quality without knowing which framework produced which answer. Our guide to AI agent evals covers how to structure this.
5. Measure developer experience honestly. Track how long it took to debug the first wrong answer and how readable the code is for whoever will maintain it.
6. Decide on the architecture, not just the library. Often the result is "LlamaIndex for retrieval, LangGraph for orchestration," which is a fine outcome.

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 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-weight models that fit on modest hardware generally trail the leading hosted models on 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 (such as Botpress, or the hosted agent and assistant builders offered by the major model providers) 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.

Do I need LangChain or LlamaIndex at all?

Not always. For a simple application that sends a prompt, calls one or two tools, or retrieves from a single vector store, the model provider's SDK plus a few hundred lines of your own code can be easier to debug and maintain than a framework. Frameworks earn their place when you need many data connectors, advanced retrieval such as hybrid search and re-ranking, stateful multi-step agents, or built-in tracing. Start lean, and adopt a framework when you find yourself rebuilding what it already provides.

Conclusion

The LangChain vs LlamaIndex question is really a question about the shape of your application. Both frameworks are mature, both run real production workloads, and both have narrowed the gap since 2023. Their philosophies still differ, and that difference is what should drive the choice.

LangChain, and LangGraph in particular, is strongest when the application is about agents and actions: calling tools, making decisions, coordinating several agents, and writing to external systems. LlamaIndex is strongest when the application is about answering questions from data: ingesting many source types, tuning chunking, and getting hybrid search and re-ranking right without much custom code. Many production systems use both, with LlamaIndex as the retrieval layer feeding a LangChain-based agent.

The caveats are worth remembering. Both frameworks change quickly, so budget for upgrade work. Defaults such as chunk size and memory strategy are starting points, not production settings. And for simple applications, a thin layer over the model provider's SDK can beat either framework.

If you're still unsure, run a one-week bake-off on your own data and questions. If you'd like an experienced team to help design the architecture, our LLM integration services can get you there faster.