# LangChain

> 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.

- **URL:** https://www.langchain.com/
- **Category:** LLM Frameworks
- **Tags:** LLM Vector Store, Agentic AI, Tool Integration
- **Featured:** yes
- **Updated:** 2026-04-22 13:39
- **Canonical page:** https://vectordb.ever.works/items/langchain

## Details

## 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:

```python
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.

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