Langchain

LangChain - Build LLM Applications with Agents & RAG

The most popular framework for building LLM-powered applications.

When to use LangChain

Use LangChain when:

• Building agents with tool calling and reasoning (ReAct pattern)
• Implementing RAG (retrieval-augmented generation) pipelines
• Need to swap LLM providers easily (OpenAI, Anthropic, Google)
• Creating chatbots with conversation memory
• Rapid prototyping of LLM applications
• Production deployments with LangSmith observability

Metrics:

• 119,000+ GitHub stars
• 272,000+ repositories use LangChain
• 500+ integrations (models, vector stores, tools)
• 3,800+ contributors

Use alternatives instead:

• LlamaIndex: RAG-focused, better for document Q&A
• LangGraph: Complex stateful workflows, more control
• Haystack: Production search pipelines
• Semantic Kernel: Microsoft ecosystem

Quick start

Installation

# Core library (Python 3.10+)
pip install -U langchain

# With OpenAI
pip install langchain-openai

# With Anthropic
pip install langchain-anthropic

# Common extras
pip install langchain-community  # 500+ integrations
pip install langchain-chroma     # Vector store

Basic LLM usage

from langchain_anthropic import ChatAnthropic

# Initialize model
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")

# Simple completion
response = llm.invoke("Explain quantum computing in 2 sentences")
print(response.content)

Create an agent (ReAct pattern)

from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

# Define tools
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"It's sunny in {city}, 72°F"

def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Search results for: {query}"

# Create agent ( str:
    """Perform a risky operation that might fail."""
    try:
        # Your operation here
        result = perform_operation(query)
        return f"Success: {result}"
    except Exception as e:
        return f"Error: {str(e)}"

# Agent handles errors gracefully
agent = create_agent(model=llm, tools=[risky_operation])

LangSmith observability

import os

# Enable tracing
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"

# All chains/agents automatically traced
agent = create_agent(model=llm, tools=[calculator])
result = agent.invoke({"input": "Calculate 123 * 456"})

# View traces at smith.langchain.com

Vector stores

Chroma (local)

from langchain_chroma import Chroma

vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    persist_directory="./chroma_db"
)

Pinecone (cloud)

from langchain_pinecone import PineconeVectorStore

vectorstore = PineconeVectorStore.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    index_name="my-index"
)

FAISS (similarity search)

from langchain_community.vectorstores import FAISS

vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
vectorstore.save_local("faiss_index")

# Load later
vectorstore = FAISS.load_local("faiss_index", OpenAIEmbeddings())

Document loaders

# Web pages
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://example.com")

# PDFs
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("paper.pdf")

# GitHub
from langchain_community.document_loaders import GithubFileLoader
loader = GithubFileLoader(repo="user/repo", file_filter=lambda x: x.endswith(".py"))

# CSV
from langchain_community.document_loaders import CSVLoader
loader = CSVLoader("data.csv")

Text splitters

# Recursive (recommended for general text)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", " ", ""]
)

# Code-aware
from langchain.text_splitter import PythonCodeTextSplitter
splitter = PythonCodeTextSplitter(chunk_size=500)

# Semantic (by meaning)
from langchain_experimental.text_splitter import SemanticChunker
splitter = SemanticChunker(OpenAIEmbeddings())

Best practices

1. Start simple - Use create_agent() for most cases
2. Enable streaming - Better UX for long responses
3. Add error handling - Tools can fail, handle gracefully
4. Use LangSmith - Essential for debugging agents
5. Optimize chunk size - 500-1000 chars for RAG
6. Version prompts - Track changes in production
7. Cache embeddings - Expensive, cache when possible
8. Monitor costs - Track token usage with LangSmith

Performance benchmarks

| Operation | Latency | Notes | |-----------|---------|-------| | Simple LLM call | ~1-2s | Depends on provider | | Agent with 1 tool | ~3-5s | ReAct reasoning overhead | | RAG retrieval | ~0.5-1s | Vector search + LLM | | Embedding 1000 docs | ~10-30s | Depends on model |

LangChain vs LangGraph

| Feature | LangChain | LangGraph | |---------|-----------|-----------| | Best for | Quick agents, RAG | Complex workflows | | Abstraction level | High | Low | | Code to start | <10 lines | ~30 lines | | Control | Simple | Full control | | Stateful workflows | Limited | Native | | Cyclic graphs | No | Yes | | Human-in-loop | Basic | Advanced |

Use LangGraph when:

• Need stateful workflows with cycles
• Require fine-grained control
• Building multi-agent systems
• Production apps with complex logic

References

• [Agents Guide](references/agents.md) - ReAct, tool calling, streaming
• [RAG Guide](references/rag.md) - Document loaders, retrievers, QA chains
• [Integration Guide](references/integration.md) - Vector stores, LangSmith, deployment

Resources

• GitHub: https://github.com/langchain-ai/langchain ⭐ 119,000+
• Docs: https://docs.langchain.com
• API Reference: https://reference.langchain.com/python
• LangSmith: https://smith.langchain.com (observability)
• Version: 0.3+ (stable)
• License: MIT

Related Skills

• langgraph — stateful agent workflows
• rag-pipeline — RAG with LangChain
• llm-caching-strategies — caching in LangChain

GitNexus Index

This skill is indexed by GitNexus for knowledge graph traversal. Index path: /Users/localuser/.claude/skills/langchain/.gitnexus Last indexed: 2026-05-23

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: UltronCore
• Source: UltronCore/claude-skill-vault
• License: MIT

Install and usage instructions live in the source repository linked above.