langchainvsllama_index
LangChain is broader — chains, agents, memory, integrations. LlamaIndex is RAG-first, optimized for indexing documents and retrieval. Use LlamaIndex inside LangChain for the best of both.
langchain
github.com/langchain-ai/langchainThe biggest LLM framework. Chains, agents, RAG pipelines, integrations with every model, vector DB, and tool. Strong ecosystem with LangGraph for agentic workflows. The criticism: for simple use cases the abstractions feel heavy and the API has churned across versions. Worth it once your AI stack outgrows direct API calls.
Full review →llama_index
github.com/run-llama/llama_indexRAG specialist. Better document loading, chunking, and retrieval than LangChain for retrieval-first apps. Strong on advanced strategies — hybrid search, re-ranking, recursive retrieval. Now expanded into a general LLM toolkit but RAG is still the sweet spot.
Full review →Which should you pick?
Pick langchain if…
You are building multi-step AI pipelines (agents, complex RAG, tool use) and need the ecosystem.
Skip langchain if…
You are just calling an LLM API and processing the response — use the native SDK directly.
Pick llama_index if…
Your AI app is mostly retrieval over documents — knowledge base, support bot, internal search.
Skip llama_index if…
You need broad agent/tool use — LangChain has wider integration coverage.
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