LangChain

Overview

LangChain enables developers to build context-aware LLM applications through composable chains, extensive integrations, and agentic capabilities.

Vector Integration

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()

Features

• Tool Calling: LCEL for structured function calling and parallel tool execution
• Memory: ConversationBufferWindowMemory, EntityMemory, VectorStoreRetrieverMemory for persistent state
• Agents: ReAct, Plan-and-Execute agents with toolkits

Use Cases

• Stateful chatbots with conversation history
• Autonomous agents for multi-step tasks
• RAG-enhanced knowledge retrieval agents

Comparisons

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

Pricing

Open-source (MIT). LangSmith/LangGraph have paid tiers.