What are the differences between LangChain and other LLM frameworks like LlamaIndex or Haystack?

LangChain, LlamaIndex, and Haystack are frameworks designed to help developers build applications with large language models (LLMs), but they differ in scope, design priorities, and use cases. LangChain focuses on creating modular, chain-based workflows that integrate LLMs with external tools (APIs, databases, etc.) and memory systems. LlamaIndex specializes in structuring and querying private or domain-specific data for LLMs, making it ideal for retrieval-augmented generation (RAG). Haystack, developed by deepset, emphasizes production-ready search pipelines, particularly for document-based question answering and information retrieval. While all three frameworks overlap in enabling LLM-powered apps, their core architectures target distinct stages of development.

LangChain’s strength lies in its flexibility for building multi-step LLM workflows. For example, a developer could create a chatbot that first calls a weather API, processes the data with an LLM, and then saves the conversation history to a database—all using LangChain’s pre-built components like Agents, Tools, and Memory. In contrast, LlamaIndex optimizes data ingestion and indexing for LLM queries. If you have a collection of internal documents, LlamaIndex can automatically chunk, embed, and index them for efficient semantic search, then feed relevant passages to an LLM for answers. Haystack, meanwhile, provides a pipeline-centric approach for document processing: a typical Haystack pipeline might connect a document store (like Elasticsearch), a retriever model, and an LLM, with built-in tools for preprocessing PDFs or web pages.

The choice between frameworks often depends on the problem stage. LangChain is suited for prototyping complex, tool-augmented LLM apps quickly, thanks to its extensive integrations (100+ tools) and abstraction layers. LlamaIndex is better when the priority is efficient data preparation and retrieval for LLMs, especially with large or domain-specific datasets. Haystack shines in production-grade search systems, offering scalability, monitoring, and REST API support out of the box. For instance, a company building a customer support bot might use LlamaIndex to index help articles, LangChain to handle dynamic user interactions with external APIs, and Haystack to deploy a robust search backend. While overlap exists (all support basic RAG), their specialized features guide developers toward specific use cases.