The tooling ecosystem around large language models (LLMs) has expanded faster than most practitioners can track. Two names dominate nearly every conversation about LLM application development: LangChain and LlamaIndex. Both promise to reduce the friction of building with LLMs. Both have large communities and active development. And both get recommended, almost reflexively, the moment someone asks how to get started.
But neither one is the right answer for every team, every project, or every stage of development. A third path is increasingly worth serious consideration: building from scratch, using nothing but the base APIs and your own abstractions.
This guide breaks down what each approach actually offers, where each one creates problems, and how to decide which path fits your situation. The goal isn’t to crown a winner. It’s to help you make a deliberate choice rather than defaulting to whatever has the most GitHub stars.
Understanding What These Tools Actually Do
Before comparing them, it helps to be precise about the problem they solve.
LLMs on their own are stateless text predictors. They take a prompt, return a completion, and remember nothing. Building useful applications on top of them — things like document question-answering systems, multi-step agents, retrieval pipelines, or conversational interfaces — requires orchestration: managing context, routing between tools, retrieving relevant information, handling memory, and chaining operations together.
LangChain and LlamaIndex both provide that orchestration layer. They abstract away the plumbing so developers can focus on application logic rather than infrastructure. Building from scratch means writing that plumbing yourself.
LangChain: Broad, Flexible, Occasionally Overwhelming
LangChain arrived early and built an enormous surface area. Its strength is breadth. It provides abstractions for chains, agents, memory, tools, retrievers, output parsers, prompt templates, and dozens of integrations with external services, databases, and APIs.
Where It Works Well
Teams that need to connect LLMs to a wide variety of external tools and data sources will find LangChain’s integration library genuinely useful. It covers a remarkable range of providers — from vector databases to search engines to custom APIs — and the community has produced extensive documentation and examples for common patterns.
LangChain also handles multi-step agentic workflows reasonably well. If your application needs an LLM to decide between tools, loop on results, or execute a sequence of actions based on intermediate outputs, LangChain’s agent abstractions give you a starting point without requiring you to build the decision loop yourself.
Where It Creates Friction
The same breadth that makes LangChain appealing also makes it hard to reason about. The abstraction layers sit deep, and when something breaks, tracing the failure through multiple layers of wrapped classes is non-trivial. Many developers report that LangChain’s abstractions initially accelerate development and then create drag during debugging and customization.
The framework has also evolved rapidly, which means documentation can lag behind the current API, older tutorials may reference deprecated patterns, and upgrading between versions occasionally introduces breaking changes. For production systems that require stability and predictability, this churn is a real cost.
LangChain works best when you’re prototyping quickly, exploring what an LLM application could do, or building something where broad integration coverage matters more than tight control over implementation details.
LlamaIndex: Focused, Opinionated, Purpose-Built for Retrieval
LlamaIndex (formerly GPT Index) started with a narrower scope: making it easier to build retrieval-augmented generation (RAG) systems. That focus is still its defining characteristic.
Where It Works Well
If your application is primarily about retrieving information from documents and providing it as context to an LLM, LlamaIndex is exceptionally well-suited. Its data connectors, indexing strategies, and query engines are purpose-built for this use case, and the abstractions are generally more coherent as a result.
LlamaIndex also provides solid tooling for more advanced RAG patterns: re-ranking, hybrid search, hierarchical retrieval, and query routing. Teams building production-grade document question-answering systems, knowledge base assistants, or enterprise search applications will find that LlamaIndex handles the edge cases that simpler retrieval pipelines miss.
The framework has also invested meaningfully in evaluation tooling, which matters more than it might seem. Knowing whether your retrieval system is actually returning useful context — and knowing that before it affects users — is one of the harder problems in LLM application development.
Where It Creates Friction
LlamaIndex is less suited to applications that extend beyond retrieval. If you need rich agent behavior, complex tool use, or workflows that don’t center on document retrieval, you may find yourself fighting the framework’s assumptions rather than working with them.
Like LangChain, it also carries abstraction overhead. The more the framework assumes about your architecture, the harder it becomes to deviate from those assumptions when your requirements don’t fit the expected pattern.
Building From Scratch: More Accessible Than It Sounds
“Building from scratch” doesn’t mean writing an LLM from scratch. It means calling the model provider’s API directly, implementing your own retrieval logic, and building only the abstractions your specific application needs.
This approach has become more accessible as the base APIs have matured. OpenAI, Anthropic, Google, and other providers now offer well-documented APIs with solid client libraries. Vector database providers like Pinecone, Weaviate, and Qdrant have their own SDKs. The infrastructure exists. You’re choosing to assemble it yourself rather than through a framework.
Where It Works Well
Building from scratch gives you complete visibility into what your system is doing at every step. There are no hidden prompt injections from framework templates, no unexpected retry logic, no abstraction layers intercepting your requests. When something behaves unexpectedly, you know exactly where to look.
This control becomes especially valuable in production. Teams that have taken LLM applications through the full development lifecycle frequently report that framework constraints become more painful as requirements evolve. Custom retrieval logic, specialized context management, domain-specific prompt engineering, and performance optimization are all easier when you own the implementation.
There’s also a less obvious benefit: building from scratch forces clarity about what your application actually needs. Frameworks can encourage over-engineering by making complex patterns easy to reach for. When you’re implementing retrieval yourself, you’ll naturally implement exactly what your use case requires — no more, no less.
Where It Creates Friction
The upfront investment is real. You’ll write more code initially, make more decisions explicitly, and spend more time on infrastructure that frameworks would handle automatically. For teams exploring whether an LLM application is viable at all, that investment may delay learning.
There’s also a knowledge requirement worth being honest about. Building a good RAG pipeline from scratch requires understanding embedding models, vector similarity, chunking strategies, and context window management. Frameworks can paper over knowledge gaps that scratch-built systems will expose.
How to Choose
The decision comes down to three variables: project stage, team expertise, and application type.
If you’re in early exploration, use a framework. LangChain or LlamaIndex will let you test assumptions faster than building from scratch. The abstraction overhead matters less when the goal is learning, not production. LangChain is the better choice if you’re uncertain what your application will need. LlamaIndex is the better choice if retrieval is clearly central.
If you’re building a retrieval-heavy production system, LlamaIndex is worth serious consideration. Its focused scope means the abstractions are more likely to align with what you need, and its evaluation tooling will serve you well as you move toward production.
If you have clear, specific requirements and a team with strong engineering foundations, building from scratch is often the better long-term investment. The initial overhead pays dividends in debuggability, performance optimization, and flexibility as requirements evolve.
If your application needs deep customization at any layer of the stack, plan for a scratch-built approach — or build with the expectation that you’ll eventually peel back the framework abstractions. Many teams end up there anyway; starting with that intention is more efficient.
One more thing: these aren’t permanent decisions. A common and sensible pattern is to prototype with LangChain, develop a clearer understanding of what the production system needs, and then rebuild the core retrieval and orchestration logic from scratch while retaining framework integrations for peripheral components.
Recommended Learning Resources
To go deeper on each approach:
Final Thoughts
LangChain, LlamaIndex, and scratch-built systems aren’t competing philosophies. They’re tools with different tradeoffs, suited to different contexts. The teams that struggle most with LLM application development are usually the ones that chose a tool before they understood their problem well enough to evaluate the tradeoffs.
Start by understanding what your application needs: Where does complexity live? What do you need to control? How much do you value speed to prototype versus long-term maintainability? Those answers will point you toward the right approach more reliably than any benchmark comparison.
The frameworks will continue to evolve. The underlying principles of good software architecture won’t.
Vinod Chugani is an AI and data science educator who bridges the gap between emerging AI technologies and practical application for working professionals. His focus areas include agentic AI, machine learning applications, and automation workflows. Through his work as a technical mentor and instructor, Vinod has supported data professionals through skill development and career transitions. He brings analytical expertise from quantitative finance to his hands-on teaching approach. His content emphasizes actionable strategies and frameworks that professionals can apply immediately.
