LangChain vs LlamaIndex: Which Framework for Your RAG Pipeline in 2025

Retrieval-Augmented Generation moved from research to production at scale in 2024, and two frameworks dominate the implementation landscape: LangChain for orchestration-heavy pipelines and LlamaIndex for data indexing and retrieval-focused workloads. Choosing the wrong one for your use case adds complexity without benefit — here is how to evaluate them for your specific requirements.

LangChain: Orchestration First

LangChain is an orchestration framework — its core value is chaining LLM calls, tools, memory, and agents into coherent workflows. The LangChain documentation covers integrations with over 50 LLM providers, vector stores, and data connectors. LangGraph, its agent workflow extension, enables stateful multi-agent pipelines with explicit state management.

LangChain is the right choice when your pipeline involves multiple steps, conditional routing, tool use (web search, code execution, API calls), or agent loops. It is over-engineered for a simple single-step RAG retrieval where you index documents and answer questions from them.

LlamaIndex: Retrieval Depth

LlamaIndex (formerly GPT Index) focuses on data ingestion, indexing, and retrieval. It provides more granular control over chunking strategies, embedding models, index types (vector, keyword, knowledge graph), and query transformations than LangChain. The LlamaIndex documentation shows purpose-built abstractions for multi-document reasoning and structured data querying that LangChain does not natively replicate.

For retrieval quality — the primary determinant of RAG output quality — LlamaIndex offers more tuning surface: hybrid search, re-ranking, contextual compression, and recursive retrieval strategies are first-class concepts rather than community integrations.

When to Use Each

• Complex multi-step agent workflows: LangChain with LangGraph
• RAG quality optimisation (chunking, re-ranking, hybrid search): LlamaIndex
• Production RAG with evaluation: LlamaIndex with Arize Phoenix or Ragas for retrieval metrics
• LLM provider flexibility across a workflow: LangChain — broadest provider coverage
• Structured data querying alongside unstructured retrieval: LlamaIndex with NL-to-SQL or Pandas query engines

The 2025 Synthesis

The pragmatic pattern emerging in production teams is: LlamaIndex for the retrieval layer, LangChain or LangGraph for the orchestration layer above it. Both frameworks have published integration guides for this hybrid architecture. The most important investment is in your evaluation harness — retrieval precision and answer faithfulness metrics matter more than framework choice for output quality.

At Cynaris, our AI engineering team has built RAG pipelines across both frameworks at enterprise scale. See how we approach LLM system design for organisations moving from prototype to production.