LangChain, LlamaIndex, or Custom Code: Choosing an LLM Development Approach
Soham Halder
Which LLM Development Approach Should You Choose: Building an LLM application starts with choosing the right development approach: LangChain, LlamaIndex or custom code.
What Is LangChain: LangChain helps developers build LLM applications with reusable components, model integrations, tool calling and multi-step AI workflows.
When Should You Choose LangChain: Choose LangChain when your application needs AI agents, multiple tools, conditional workflows or complex interactions across different systems.
What Is LlamaIndex: LlamaIndex connects large language models with external data, helping developers organise documents, build indexes and retrieve relevant information.
When Does LlamaIndex Work Best: LlamaIndex is a strong choice for document-based chatbots, knowledge assistants and retrieval-augmented generation (RAG) applications.
What Does Custom Code Offer: Custom code gives developers greater control over model calls, data flow, application logic and performance without relying on a large framework.
The Trade-Off of Building From Scratch: Custom development can reduce unnecessary dependencies, but teams must build and maintain integrations, retrieval pipelines, error handling and other components themselves.
Can You Combine These Approaches: Yes, developers can use LlamaIndex for data retrieval, LangChain for workflow orchestration and custom code for specialised application requirements.
How Do You Make the Final Choice: Choose LangChain for complex workflows, LlamaIndex for data-heavy applications, and custom code for focused projects that need greater control and minimal overhead.