LangChain vs LlamaIndex vs building it yourself

Everyone building AI applications faces the same question: should I use LangChain, LlamaIndex, or just build it myself with OpenAI’s API?

The answer matters more than you think. Pick wrong and you will spend months debugging abstraction layers instead of shipping features.

The stakes have gotten higher. LangChain now has 90M+ monthly downloads and powers production applications at companies like Uber, JP Morgan, and BlackRock. LlamaIndex has expanded into document agents, smart spreadsheet processing, and enterprise document workflows. These are serious tools with serious adoption.

I have watched teams make this decision dozens of times. The pattern is predictable. They start with a framework because it promises to get them moving fast. Six months later, they are fighting the framework more than using it.

The abstraction trap

Frameworks seduce you with convenience. LangChain documentation shows you can build a chatbot in five lines of code. LlamaIndex promises to connect LLMs to your data with minimal setup. Both sound great.

The problem shows up when you need to do something the framework did not anticipate.

I came across this analysis of LangChain’s complexity that nailed it: the framework becomes a source of friction rather than productivity once your requirements get sophisticated. You end up diving into LangChain internals to change behavior that should be simple.

The langchain llamaindex comparison gets interesting when you look at what each framework actually abstracts. LangChain wraps everything - LLM calls, prompts, memory, chains, agents. That is five layers between you and the AI. LlamaIndex focuses on data connection and retrieval. Narrower scope, but still plenty of abstraction.

Research from developers who abandoned frameworks found something surprising: their simpler implementations immediately outperformed the framework versions in both quality and reliability.

Why does this happen? Every abstraction layer adds complexity. You debug the framework, not your application. You learn LangChain’s quirks instead of learning how LLMs actually work.

What each framework actually solves

Let me be fair to both frameworks. They do solve real problems. Just not the ones most teams think.

LlamaIndex excels at one thing: connecting LLMs to your data. If you need to build a system that searches through documents, creates embeddings, and retrieves context for AI responses, LlamaIndex handles this well. The high-level API lets you prototype quickly. The indexing and retrieval modules are solid.

LlamaIndex has expanded significantly. LlamaParse v2 overhauled document parsing with up to 50% cost reduction and automatic handling of skewed or rotated scans. They have added LlamaAgents for one-click document agent deployment, LlamaSheets for messy spreadsheet processing, and enterprise document processing pipelines.

Where LlamaIndex struggles: anything beyond data-focused workflows. Need complex multi-step reasoning with arbitrary logic? Limited options. Want fine-grained control over agent behavior? Fight with opinionated abstractions.

LangChain takes the opposite approach. It gives you maximum flexibility through modular components you can chain together. Agents, tools, memory, custom chains - you can build sophisticated workflows.

The architecture has matured significantly. LangGraph 1.0 provides durable state persistence, production-tested at companies like Uber, LinkedIn, and Klarna. If your server restarts mid-workflow, it picks up exactly where it left off. LangChain agents are now built on top of LangGraph, so you can start with high-level APIs and drop down to lower-level control when needed.

The catch: that flexibility still comes with serious baggage. Dependency bloat remains a complaint. Installing LangChain pulls in dozens of packages. Many teams report spending as much time understanding LangChain as building features.

Performance tells part of the story. Analysis comparing frameworks to direct API calls found that for simple requests, direct implementation shows measurably lower latency and reduced costs. The framework overhead is real.

Interestingly, for complex workflows, frameworks can actually perform better due to built-in optimization capabilities. So the langchain llamaindex comparison depends heavily on your specific use case.

When building yourself makes sense

Most AI applications do not need a framework. They need three things: an API client, prompt management, and error handling.

That is it.

Tutorials on building without frameworks show you can create functional AI agents in under 200 lines of clean code. No abstractions. No magic. Just direct API calls that you fully control and understand.

The benefits compound over time:

• You know exactly what every line does
• Debugging means reading your code, not framework source
• Changes take minutes, not hours of fighting abstractions
• No breaking changes from framework updates
• Your team learns LLM fundamentals instead of framework quirks

Building directly works best when your requirements are clear and relatively simple. Need a chatbot that maintains conversation context? Straightforward with the OpenAI API. Want to add document search? RAG implementations without frameworks use ChromaDB and direct API calls effectively.

The effort difference matters less than you think. Developers switching from LangChain report their custom implementations took about the same development time as learning to use the framework properly. But ongoing maintenance was dramatically simpler.

Simple language here: if you are building something straightforward, skip the framework. Use the API directly. Write clean functions. You will ship faster and maintain easier.

The hidden costs of frameworks

Most teams overlook what happens after you ship. That is where frameworks really hurt you.

Breaking changes are brutal. LangChain had frequent breaking changes throughout its development as it evolved rapidly. Code that worked last month breaks after an update. You are stuck: stay on old versions with security risks, or spend cycles adapting to changes.

LangChain and LangGraph both hit 1.0 in October 2025, coinciding with a $125 million Series B from Sequoia Capital. They now promise no breaking changes until 2.0. But that stability took years. Early adopters paid the price in constant refactoring.

Maintenance complexity multiplies when you consider your full stack. Your application depends on LangChain, which depends on OpenAI’s SDK, which depends on… you get it. Each layer can break. Each library has its own update cadence.

The reliability numbers are sobering. Error rates compound exponentially: 95% reliability per step yields only 36% success over 20 steps. Production demands 99.9%+ reliability, yet even sophisticated agent implementations struggle to achieve this. Every abstraction layer you add introduces more potential failure points.

Microsoft’s analysis of agentic complexity put it well: frameworks need careful consideration in terms of cognitive load, security concerns, latency, and ongoing maintenance. Every layer is another potential point of failure.

The security implications are real. More dependencies mean more attack surface. More code you did not write running in your application. More security advisories to track.

On the positive side, frameworks bring observability tools. 89% of teams have implemented observability for their agents. LangSmith, the LangChain observability platform, provides tracing, evaluation, and cost tracking. If you build from scratch, you need to build or integrate observability yourself - not trivial, but not impossible either with tools like Langfuse that work independently of any framework.

Team knowledge becomes an issue too. When someone new joins, do they need to learn LangChain’s abstractions or just learn to work with LLM APIs? Which skill is more valuable long-term?

How to choose your approach

Here is a practical framework for the langchain llamaindex comparison and deciding on DIY.

Start with complexity assessment:

• Simple chatbot or single-purpose tool? Build directly
• Data-heavy retrieval system? Consider LlamaIndex
• Multi-step reasoning with durable state requirements? LangGraph excels here - LinkedIn, Uber, and 400+ companies use it for complex stateful workflows
• Quick prototype with role-based agents? CrewAI ships fast - but teams report hitting walls 6-12 months in when requirements grow beyond its opinionated design
• Anything requiring heavy customization? Build directly

Team skills matter significantly:

• Team comfortable with frameworks and abstractions? Framework might work
• Team prefers understanding fundamentals? Build directly
• Small team that needs to move fast? Direct implementation is often faster
• Large team with specialized AI engineers? Framework expertise might pay off

Think about long-term costs. Deloitte warns that more than 40% of agentic AI projects could be cancelled by 2027 due to unanticipated complexity and cost. Adding framework dependencies increases that risk. Direct API implementations integrate more easily into existing systems.

Prototyping versus production is another consideration. Frameworks excel at rapid prototyping. You can test ideas quickly. But many teams find that production requirements push them toward simpler, more controlled implementations.

My recommendation for most mid-size companies: start simple. Build your first version with direct API calls. You will learn what you actually need. If you hit complexity that genuinely requires a framework, you will know it. And you will understand LLMs well enough to use the framework effectively.

The framework landscape has consolidated. Beyond LangChain and LlamaIndex, you should know about OpenAI’s Agents SDK - a minimalist approach without graphs or state machines, supporting both Python and TypeScript. Microsoft merged AutoGen and Semantic Kernel into a unified Agent Framework, targeting GA in early 2026 with enterprise features like built-in governance and multi-cloud support.

The langchain llamaindex comparison becomes clearer when you realize most applications do not need any of these. They need clean code that calls AI services effectively.

Frameworks promise to handle complexity for you. But they introduce their own complexity in the process. Often, the best way to handle complexity is to avoid creating it in the first place.

Build what you need, not what a framework wants you to build.