



Leading framework for LLM applications with deep vector store integrations (e.g., Qdrant, Pinecone), tool calling, memory management, and agent orchestration for building chatbots and autonomous agents. Compared to LlamaIndex, it emphasizes general-purpose chains and multi-agent workflows over RAG-specific indexing.
LangChain enables developers to build context-aware LLM applications through composable chains, extensive integrations, and agentic capabilities.
Native support for Qdrant, Chroma, FAISS, Pinecone, Weaviate, and 50+ vector stores:
from langchain_community.vectorstores import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_documents(documents, embeddings, path=":memory:")
retriever = qdrant.as_retriever()
LangChain vs LlamaIndex:
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Primary Focus | Chains, agents, tools | RAG indexing, query engines |
| Vector Support | 50+ stores | 40+ data connectors |
| Best For | Complex orchestration | Data-heavy RAG pipelines |
Open-source (MIT). LangSmith/LangGraph have paid tiers.
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