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LangChain
The framework for building LLM apps — chains, agents, RAG, and 600+ integrations.
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At a Glance
| Attribute | LangChain | LlamaIndex |
|---|---|---|
| License / Pricing | Free | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.6/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 6 listed | 6 listed |
| Categories | AI AgentsRAG Frameworks | AI AgentsRAG Frameworks |
Key Features
LangChain
- LCEL (LangChain Expression Language) for composing chains
- LangGraph for stateful, multi-actor agent workflows
- 600+ integrations with LLMs, vector stores, tools
- RAG primitives: document loaders, splitters, retrievers
- Memory and conversation history management
- LangSmith for tracing, debugging, and evaluation
LlamaIndex
- 150+ data connectors (Notion, Slack, databases, S3, etc.)
- Advanced RAG with query routing and re-ranking
- Agentic workflows with multi-step reasoning
- Structured output and data extraction from documents
- Workflow engine for complex, event-driven agent pipelines
- LlamaCloud for managed RAG pipelines
Real-World Use Cases
LangChain
Build a document Q&A bot with RAG
Load documents using LangChain document loaders (PDF, web, S3)
Build a ReAct agent with tools
Define tools (web search, calculator, database query)
LlamaIndex
Enterprise knowledge base over internal docs
Connect data sources with SimpleDirectoryReader or Notion/Confluence loaders
Integrations
LangChain
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LlamaIndex
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🏆 Which should you choose?
Choose LangChain if…
- → you want a managed or commercial offering with enterprise support and SLAs
Choose LlamaIndex if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in