Building applications on top of large language models requires a framework that handles the plumbing: connecting to models, managing prompts, retrieving context from documents, orchestrating multi-step workflows, and integrating with vector databases. LangChain, LlamaIndex, and Haystack are the three leading open-source frameworks for this purpose, each with a distinct philosophy about how LLM applications should be built.
LangChain is the most widely adopted LLM framework, designed as a general-purpose toolkit for building any kind of LLM-powered application. It provides abstractions for prompts, chains (sequential LLM calls), agents (LLM-driven decision-making), memory (conversation state), and tools (external API integrations). LangChain's strength is breadth — it supports virtually every model provider, vector database, and integration you might need. LangGraph extends LangChain with a graph-based workflow engine for building complex agent systems. LangSmith provides observability and evaluation tooling. The ecosystem is massive, with the largest community and most third-party tutorials of any LLM framework.
LlamaIndex (formerly GPT Index) focuses specifically on connecting LLMs with data. While LangChain tries to be everything, LlamaIndex excels at the retrieval-augmented generation (RAG) pipeline: ingesting documents, chunking and indexing them, embedding into vector stores, and retrieving relevant context for LLM queries. It provides sophisticated data connectors (called Loaders) for PDFs, databases, APIs, Slack, Notion, and dozens of other sources. The indexing abstractions — including vector indexes, tree indexes, keyword indexes, and knowledge graph indexes — give fine-grained control over how your data is structured for retrieval. LlamaIndex Workflows provide a more recent orchestration layer for building multi-step applications.
Haystack, developed by deepset, takes a pipeline-first approach inspired by scikit-learn. Everything in Haystack is a Component that connects through typed Pipelines, creating explicit, inspectable data flows. This design makes Haystack applications easier to understand, test, and debug than the more implicit chain-based approaches of LangChain. Haystack 2.0 (rewritten in 2024) modernized the framework with a cleaner API, better type safety, and native support for modern patterns like function calling and structured output. Haystack excels in production deployment scenarios where pipeline reliability and observability matter more than rapid prototyping speed.
For RAG applications — the most common LLM use case — LlamaIndex provides the most sophisticated and optimized experience. Its data ingestion pipeline handles complex document parsing with metadata extraction, hierarchical chunking strategies, and multiple index types that can be combined for hybrid retrieval. LangChain supports RAG through its retriever abstractions but with less depth and fewer optimization options. Haystack provides solid RAG capabilities with its Retriever and Reader components but sits between the other two in terms of RAG-specific sophistication.