LangChain vs LlamaIndex

Feature comparison

LangChain and LlamaIndex are the two most widely deployed LLM application frameworks in enterprise generative AI development. Both started as open-source libraries and have grown into platform companies with paid managed services. Their feature surfaces overlap meaningfully but with different centres of gravity.

LangChain's strength is general-purpose orchestration. It provides abstractions for prompts, chains, agents, tool use, memory, and document loading. LangGraph, the stateful workflow runtime built on top, supports cyclic agentic graphs, human-in-the-loop checkpoints, and durable execution for long-running tasks. LangSmith handles tracing, evaluation, and prompt management. The ecosystem includes hundreds of pre-built integrations across model providers, vector databases, document loaders, retrievers, and tools.

LlamaIndex's strength is retrieval and indexing over enterprise documents. The library provides a deep hierarchy of indexes (vector, summary, knowledge graph, document store), retrievers, postprocessors, and query engines. LlamaParse offers managed parsing for complex documents including PDFs with tables, images, charts, and embedded forms. LlamaCloud provides hosted indexing and retrieval services, removing much of the operational overhead of building a production RAG pipeline.

For agent and tool-use workloads, LangChain (and especially LangGraph) tends to have richer primitives. For document understanding, parsing, and retrieval over heterogeneous enterprise content, LlamaIndex tends to be more opinionated and faster to production. Both support all major model providers (OpenAI, Anthropic, Google, Mistral, Cohere, open-source via Hugging Face) and all major vector databases (Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector).

On observability and evaluation, LangSmith is the more mature commercial product, providing trace search, evaluator harnesses, and human feedback workflows. LlamaIndex partners with Arize, Phoenix, Langfuse, and other open-source and commercial observability tools rather than offering a first-party stack at the same depth.

Enterprise adoption patterns frequently combine both libraries. Teams use LlamaIndex for ingestion, parsing, and retrieval; LangChain or LangGraph for orchestration and agent control flow; and a vendor-neutral observability layer for tracing and evaluation. This compositional pattern is supported by both projects through framework-agnostic abstractions and shared interface conventions.

Pricing comparison

Both libraries are free and open-source under MIT licenses. Commercial revenue is concentrated in the managed services. LangSmith offers a free developer tier with limited traces, then paid plans starting at approximately $39 per user per month for Plus and custom enterprise pricing. LangGraph Cloud (deployed agent runtime) is priced on workflow executions and compute. LangChain's enterprise contracts bundle LangSmith, LangGraph Cloud, support, and SLA, typically in the $50,000 to $300,000 annual range for mid-to-large deployments as of May 2026.

LlamaCloud is priced on indexed documents, retrieval queries, and LlamaParse pages processed. LlamaParse lists at approximately $0.003 per page for the standard tier and higher for premium parsing modes that handle tables, charts, and images. Buying-side caveat for both: the framework libraries themselves carry no licence cost, but production deployments typically incur meaningful cost in the surrounding stack (vector database, embeddings, model inference, observability) that often dwarfs the framework's managed service fees. Cost should be modelled end-to-end across that stack, not on framework pricing alone.

When to choose LangChain

Choose LangChain when the use case is agentic: complex tool use, multi-step planning, branching workflows, durable long-running tasks, or human-in-the-loop processes that benefit from LangGraph's stateful runtime. It fits engineering organisations that need maximum flexibility, a wide integration catalogue, and a mature first-party observability stack via LangSmith. LangChain is also typical when the team prioritises agent control flow over ingestion ergonomics, or when LangGraph's checkpointing and replay capabilities are required for production reliability and auditability.

When to choose LlamaIndex

Choose LlamaIndex when retrieval over enterprise documents is the primary workload, when document parsing and structured RAG are the differentiating capability, or when LlamaCloud's managed indexing and LlamaParse's document understanding accelerate delivery. It fits teams whose generative AI value proposition centres on accurate retrieval over heterogeneous content (PDFs, scanned documents, contracts, financial filings, technical documentation). Industries with document-heavy workflows (legal, financial services, life sciences, public sector) frequently default to LlamaIndex when retrieval depth and parsing quality dominate the architecture.

Alternatives to both