Both frameworks help developers build LLM applications, but they optimize for different layers of the stack. LangChain emphasizes orchestration and workflows, while LlamaIndex emphasizes retrieval and document indexing.
An orchestration framework for building LLM-powered workflows and agents.
A data framework for indexing, retrieving, and querying LLM applications.
| Aspect | Langchain | Llamaindex |
|---|---|---|
| Pricing | Core framework is open source; LangSmith offers paid plans for tracing, evaluation, and observability. | Core framework is open source; LlamaIndex Cloud offers hosted indexing and retrieval options with usage-based plans. |
| Ease of Use | Best when you want a common interface across many providers and workflow patterns, though abstractions can be verbose. | Often simpler for document indexing and retrieval tasks, with less ceremony around data ingestion and query engines. |
| Performance | Performance depends on chosen models and orchestration overhead; LangSmith helps profile and optimize chains. | Strong for retrieval latency and indexing throughput, especially when using optimized index types and caching. |
| Community | Large community, many examples, and broad third-party integrations. | Active community, especially around RAG, document AI, and data connectors. |
Choose LangChain if you need flexible orchestration, multi-step agents, broad provider integrations, and observability across complex workflows. Choose LlamaIndex if your primary challenge is indexing and retrieving document-heavy data with clean query pipelines. In many production stacks, they can complement each other: LangChain for workflow orchestration and LlamaIndex for retrieval.
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