| LangChain | VS | LlamaIndex |
|---|---|---|
| Full-featured LLM framework for building chains, agents, RAG pipelines, and tool-using AI systems. | Data-focused LLM framework specializing in indexing, RAG, and structured data retrieval. |
LangChain
Pros
- Massive ecosystem (LangSmith, LangGraph)
- Rich agent and tool abstractions
- Active community and docs
- Production-ready patterns
Cons
- Steep learning curve
- Heavy abstractions can obscure logic
- Frequent breaking changes
LlamaIndex
Pros
- Best-in-class RAG pipelines
- Simple data ingestion
- Query engine abstractions
- Structured output support
Cons
- Narrower than LangChain
- Less suited for complex agents
- Smaller community
Verdict
LangChain is the go-to for complex agent systems and production tooling. LlamaIndex excels when your primary need is data retrieval and RAG.
When to use which
Use LangChain for multi-step agent workflows, tool use, and complex orchestration. Use LlamaIndex for document Q&A, search, and data-heavy RAG apps.
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