The short answer: Use LlamaIndex for document-heavy RAG applications. Use LangChain for complex multi-step AI workflows and agent systems. For simple LLM integrations, consider using neither.
Here's the full breakdown.
What each framework does
LangChain is a framework for building applications that chain together LLM calls, tools, and data sources. It was the first major LLM framework and has the largest ecosystem. Its strength is in orchestration — connecting LLMs to tools, APIs, and other systems in sequence.
LlamaIndex (formerly GPT Index) is specialised for building retrieval systems over data. Its core use case is indexing and querying documents — PDFs, databases, APIs — using LLMs. It started narrower than LangChain but has expanded significantly.
When to use LlamaIndex
LlamaIndex excels when your primary need is retrieval over documents or structured data:
Building a RAG system over product documentation
Querying multiple PDFs or knowledge bases
Creating search over structured databases using natural language
Building multi-document Q&A systems
Knowledge graph construction from unstructured text
LlamaIndex's data connectors, indexing strategies, and query engines are more mature and flexible than LangChain's for document-centric work. Its VectorStoreIndex, SummaryIndex, and KnowledgeGraphIndex handle complex retrieval scenarios that would require significant custom code in LangChain.
Choose LlamaIndex when: your bottleneck is retrieval quality, document processing, or indexing strategy.
When to use LangChain
LangChain excels when you need orchestration across multiple tools and systems:
Multi-step agent workflows (research → summarise → draft → send)
Connecting LLMs to APIs, databases, and external tools
Building conversational agents with memory and tool use
Complex chains where multiple LLM calls interact
Production applications needing observability (LangSmith)
LangChain's LCEL (LangChain Expression Language) makes composing complex pipelines clean and declarative. Its agent frameworks and tool integrations are the most mature in the ecosystem.
Choose LangChain when: your bottleneck is workflow orchestration, tool integration, or agent behaviour.
The 2026 state of both frameworks
Both frameworks have matured significantly since 2023. The main evolution:
LangChain moved away from its original verbose, sometimes confusing abstractions toward LCEL — a cleaner, more composable API. LangSmith (observability and debugging) has become genuinely excellent. The ecosystem is huge — 500+ integrations.
LlamaIndex has expanded beyond pure retrieval into agents, workflows, and evaluation frameworks. The 0.10+ releases overhauled the architecture for clarity. Their Workflows feature for agentic applications has matured considerably.
In 2026, both frameworks increasingly overlap. LlamaIndex now has solid agent support. LangChain now has better RAG support. The choice matters less than it did in 2023 — pick the one your team knows or the one better suited to your primary use case.
Performance comparison
Neither framework adds significant latency on its own — they're thin wrappers around API calls. Performance differences come from:
Retrieval strategy: LlamaIndex's advanced retrieval modes (HyDE, multi-vector, recursive retrieval) often outperform equivalent LangChain setups for document Q&A tasks.
Streaming support: Both support streaming well in 2026.
Memory: LangChain's conversation memory implementations are more mature.
When to use neither
For straightforward integrations, both frameworks can add unnecessary complexity. If you're:
Making a single LLM API call per user action
Building a simple chatbot without retrieval
Building a feature that doesn't need multi-step orchestration
Just call the OpenAI or Anthropic API directly. Fewer dependencies, easier debugging, less abstraction to fight against when something goes wrong.
Our recommendation at Sapphire Minds
We use both, and we choose based on the project:
LlamaIndex for RAG systems, document intelligence, and knowledge base Q&A
LangChain + LangSmith for complex agent workflows and when production observability is critical
Direct API calls for simple single-step LLM features
Talk to us about your AI architecture →