LangChain vs LlamaIndex
The two most popular Python frameworks for building LLM applications. LangChain is the Swiss Army knife of LLM orchestration; LlamaIndex specializes in data ingestion and retrieval. Here's how to choose between them.
LangChain
General-purpose LLM orchestration framework for chains, agents, and workflows
LlamaIndex
Data framework specialized for indexing, retrieval, and RAG applications
Side-by-Side Comparison
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Open source | ||
| Free to use | ||
| Python SDK | ||
| JavaScript/TypeScript SDK | ||
| RAG / document indexing | ||
| Multi-step agents | ||
| Advanced retrieval strategies | Partial | |
| Data connectors / loaders | 100+ | 700+ |
| Chains / pipelines | Limited | |
| Tool calling / function calling | ||
| Memory management | ||
| Prompt management | ||
| Streaming support | ||
| LangSmith observability | ||
| Ollama integration | ||
| HuggingFace integration | ||
| Vector store integrations | 50+ | 30+ |
| LLM provider integrations | 75+ | 50+ |
| GitHub Stars | 100k+ | 58k+ |
Framework Deep Dives
LangChain
LangChain launched in late 2022 and quickly became the most popular LLM framework, reaching 100k+ GitHub stars. Its core abstraction is the "chain" — composable sequences of LLM calls, tool uses, and data transformations. LangChain Expression Language (LCEL) makes it easy to build complex pipelines declaratively. The framework provides primitives for memory, conversation history, retrieval, agents, and tool calling.
LangChain's ecosystem is massive: 75+ LLM integrations, 50+ vector stores, 100+ data loaders, and hundreds of community extensions. LangSmith provides observability and debugging for production LangChain apps. The framework also ships LangGraph for stateful multi-agent orchestration, which is now the recommended approach for complex agent systems.
Pros
- ✓ Most integrations of any LLM framework
- ✓ LangGraph for complex agent workflows
- ✓ LangSmith for production observability
- ✓ Largest community and ecosystem
- ✓ JavaScript/TypeScript support
- ✓ Battle-tested in production
Cons
- ✗ Complex abstraction layers
- ✗ Frequent breaking changes historically
- ✗ Can feel over-engineered for simple RAG
- ✗ Debugging chains can be difficult
LlamaIndex
LlamaIndex (formerly GPT Index) is laser-focused on the data and retrieval problem. Its core strength is connecting LLMs to your data: 700+ data connectors (PDFs, databases, APIs, Notion, Slack, GitHub, and more), multiple index types (vector, keyword, knowledge graph, summary), and advanced retrieval strategies (hybrid search, re-ranking, HyDE, parent-child chunking).
LlamaIndex's abstractions are more intuitive for RAG-specific workflows. You load documents, create an index, and query it — three steps instead of LangChain's larger mental model. LlamaIndex 0.10+ introduced a cleaner architecture and LlamaHub for community integrations. It also supports multi-modal RAG with images and tables, and has TypeScript bindings.
Pros
- ✓ Best-in-class RAG and retrieval
- ✓ 700+ data connectors (LlamaHub)
- ✓ Advanced indexing strategies
- ✓ Multi-modal RAG support
- ✓ Cleaner abstractions for data tasks
- ✓ Active research integration
Cons
- ✗ Less suited for complex agent orchestration
- ✗ Smaller community than LangChain
- ✗ Fewer LLM provider integrations
- ✗ Can be verbose for simple use cases
When to Use Each
- → Building complex multi-step agents
- → Need maximum LLM provider flexibility
- → Using LangGraph for agentic workflows
- → Want production observability (LangSmith)
- → Building chatbots with memory and tools
- → Building RAG over documents or databases
- → Need advanced retrieval (hybrid, re-ranking)
- → Have diverse data sources (700+ connectors)
- → Building knowledge graph applications
- → Want cleaner abstractions for data retrieval
Our Recommendation
There's no single winner here — it depends on your use case. LlamaIndex is the better choice for pure RAG and data retrieval applications. LangChain wins for complex agent orchestration, chains, and when you need maximum ecosystem support. Many production systems use both.
Frequently Asked Questions
Should I use LangChain or LlamaIndex for RAG?
LlamaIndex is purpose-built for RAG and data indexing — it wins for document Q&A and knowledge retrieval use cases. LangChain is a general-purpose framework better suited for building complex agents, chains, and multi-step workflows.
Can both work with local LLMs?
Yes — both integrate with Ollama, LM Studio, vLLM, and any OpenAI-compatible local server. Both also work with HuggingFace transformers directly.
Which has better documentation?
Both have excellent documentation. LangChain's docs cover a broader range of use cases. LlamaIndex's docs are more focused on data and retrieval patterns, making them easier to follow for RAG-specific projects.
Is LangChain harder to learn?
LangChain has more abstractions and components, which can be overwhelming initially. LlamaIndex has a gentler learning curve for its core RAG use case. Both are accessible with basic Python knowledge.
Can I use both in the same project?
Yes! Many projects use LlamaIndex for data indexing and retrieval while using LangChain for orchestration, agents, or chain logic. They complement each other well.