LangChain vs LlamaIndex: Quick Verdict
LangChain and LlamaIndex are the two most-used Python frameworks for building LLM-powered applications. They overlap significantly but have different design philosophies:
- LangChain — A general-purpose orchestration framework for building agents, chains, and complex multi-step workflows
- LlamaIndex — A data framework optimized for indexing, retrieval, and building RAG (Retrieval-Augmented Generation) pipelines
If you're building a chatbot, agent, or complex pipeline, LangChain. If you're building a search or Q&A system over your documents, LlamaIndex.
But the reality is more nuanced. Let's go deeper.
Framework Philosophy
LangChain
LangChain started as a toolkit for chaining LLM calls together. Today it's a full agent and workflow framework with:
- LCEL (LangChain Expression Language) for composing chains declaratively
- LangGraph for stateful, cyclical agent workflows
- LangSmith for tracing, evaluation, and observability
Its design is modular: chains, tools, retrievers, memory, and agents are all swappable components. This gives you flexibility but also introduces significant abstraction layers.
LlamaIndex
LlamaIndex (formerly GPT Index) was purpose-built for data ingestion and retrieval. Its core primitives are:
- Documents and Nodes (chunks of ingested text)
- Indexes (vector, summary, knowledge graph)
- Query engines and Retrievers
It ships with over 160 data connectors for PDFs, Notion, Slack, Google Drive, databases, and more. Its retrieval pipeline is more configurable out-of-the-box than LangChain's.
Feature Comparison
| Feature | LangChain | LlamaIndex | |---------|-----------|------------| | RAG/Retrieval | Good | Excellent | | Agent support | Excellent (LangGraph) | Good (basic agents) | | Data connectors | ~80 | 160+ | | Observability | LangSmith (paid) | Built-in callbacks | | Multi-modal | Limited | Growing | | Community size | Very large | Large | | Learning curve | Steep | Moderate | | Streaming | ✅ | ✅ | | Async support | ✅ | ✅ |
Developer Experience
LangChain DX
LangChain has famously steep abstractions. Its API has changed dramatically between versions (v0.1 → v0.2 → v0.3), and many tutorials online are outdated. LCEL is powerful once you learn it, but the mental model takes time.
LangGraph, LangChain's agent framework, is genuinely excellent for complex stateful agents — cyclical workflows, parallel execution, human-in-the-loop checkpoints. If you need agent orchestration, nothing matches it today.
LlamaIndex DX
LlamaIndex has cleaner abstractions for its core use case. The VectorStoreIndex → QueryEngine → query() pattern is intuitive. The data connector ecosystem (SimpleDirectoryReader, Notion, Slack, etc.) works out of the box with minimal config.
For retrieval-specific tasks, LlamaIndex is faster to get right. Its sub-question query engine, knowledge graph index, and recursive retrieval patterns are battle-tested RAG techniques with simple APIs.
When to Use LangChain
Choose LangChain when:
- You're building agents — LangGraph is the best stateful agent framework available
- You need complex multi-step chains — Routing, parallel execution, and conditional branching are first-class
- Observability matters — LangSmith's tracing is the industry standard for LLM debugging
- Your team already knows it — Migration costs are real; if you have LangChain in production, stay on it unless retrieval quality is a blocker
When to Use LlamaIndex
Choose LlamaIndex when:
- Your primary use case is RAG — Document Q&A, internal knowledge bases, semantic search
- You need diverse data sources — 160+ connectors vs. LangChain's ~80
- Retrieval quality is critical — Advanced features like reranking, hybrid search, and recursive retrieval are better supported
- You want a gentler learning curve — LlamaIndex's API is more focused and easier to reason about
Can You Use Both?
Yes — and many production teams do. A common pattern:
- LlamaIndex for the retrieval pipeline (ingestion, indexing, query)
- LangChain / LangGraph for the agent orchestration layer that decides when and how to call the retriever
LlamaIndex exports a standard retriever interface that plugs directly into LangChain chains and LangGraph nodes. The two frameworks are composable by design.
Performance Considerations
Neither framework adds meaningful latency to LLM calls — the bottleneck is always the model. However:
- LangChain's abstraction layers add Python overhead in complex chains. For very high throughput, you may want to bypass the framework and call the model SDK directly.
- LlamaIndex's query pipeline can add latency for multi-hop retrieval (sub-questions, recursive retrieval). Tune your retrieval depth carefully.
What Teams Actually Use in Production
From our experience shipping AI MVPs across dozens of projects in 2024–2025:
- LlamaIndex is the default for any product where "answer questions about my documents" is the core feature
- LangGraph (part of LangChain) is the default when the product requires autonomous agent behavior
- No framework (raw SDK calls) is increasingly common for simple, high-volume use cases where abstraction adds more pain than value
Key Takeaway
Don't pick a framework on hype. Pick it on fit. LlamaIndex is better for retrieval; LangChain is better for agents. If you're building both, use both — they compose well.
Related: What is RAG? · What is an AI Agent? · Build vs Buy Your AI MVP
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