Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
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 ChatAnthropic
() -> :
() -> :
agent = create_agent(
model=ChatAnthropic(model=),
tools=[get_weather, search_web],
system_prompt=
)
result = agent.invoke({: [{: , : }]})
(result[][-].content)
import
# 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 (<10 lines!)
"claude-sonnet-4-5-20250929"
"You are a helpful assistant. Use tools when needed."
# Run agent
"messages"
"role"
"user"
"content"
"What's the weather in Paris?"
print
"messages"
1
Core concepts
1. Models - LLM abstraction
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
# Swap providers easily
llm = ChatOpenAI(model="gpt-4o")
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp")
# Streamingfor chunk in llm.stream("Write a poem"):
print(chunk.content, end="", flush=True)
2. Chains - Sequential operations
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# Define prompt template
prompt = PromptTemplate(
input_variables=["topic"],
template="Write a 3-sentence summary about {topic}"
)
# Create chain
chain = LLMChain(llm=llm, prompt=prompt)
# Run chain
result = chain.run(topic="machine learning")
3. Agents - Tool-using reasoning
ReAct (Reasoning + Acting) pattern:
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.tools import Tool
# Define custom tool
calculator = Tool(
name="Calculator",
func=lambda x: eval(x),
description="Useful for math calculations. Input: valid Python expression."
)
# Create agent with tools
agent = create_tool_calling_agent(
llm=llm,
tools=[calculator, search_web],
prompt="Answer questions using available tools"
)
# Create executor
agent_executor = AgentExecutor(agent=agent, tools=[calculator], verbose=True)
# Run with reasoning
result = agent_executor.invoke({"input": "What is 25 * 17 + 142?"})
4. Memory - Conversation history
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
# Add memory to track conversation
memory = ConversationBufferMemory()
conversation = ConversationChain(
llm=llm,
memory=memory,
verbose=True
)
# Multi-turn conversation
conversation.predict(input="Hi, I'm Alice")
conversation.predict(input="What's my name?") # Remembers "Alice"
RAG (Retrieval-Augmented Generation)
Basic RAG pipeline
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.chains import RetrievalQA
# 1. Load documents
loader = WebBaseLoader("https://docs.python.org/3/tutorial/")
docs = loader.load()
# 2. Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
splits = text_splitter.split_documents(docs)
# 3. Create embeddings and vector store
vectorstore = Chroma.from_documents(
documents=splits,
embedding=OpenAIEmbeddings()
)
# 4. Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
# 5. Create QA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
# 6. Query
result = qa_chain({"query": "What are Python decorators?"})
print(result["result"])
print(f"Sources: {result['source_documents']}")
Conversational RAG with memory
from langchain.chains import ConversationalRetrievalChain
# RAG with conversation memory
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
)
# Multi-turn RAG
qa({"question": "What is Python used for?"})
qa({"question": "Can you elaborate on web development?"}) # Remembers context
Advanced agent patterns
Structured output
from langchain_core.pydantic_v1 import BaseModel, Field
# Define schemaclassWeatherReport(BaseModel):
city: str = Field(description="City name")
temperature: float = Field(description="Temperature in Fahrenheit")
condition: str = Field(description="Weather condition")
# Get structured response
structured_llm = llm.with_structured_output(WeatherReport)
result = structured_llm.invoke("What's the weather in SF? It's 65F and sunny")
print(result.city, result.temperature, result.condition)
Parallel tool execution
from langchain.agents import create_tool_calling_agent
# Agent automatically parallelizes independent tool calls
agent = create_tool_calling_agent(
llm=llm,
tools=[get_weather, search_web, calculator]
)
# This will call get_weather("Paris") and get_weather("London") in parallel
result = agent.invoke({
"messages": [{"role": "user", "content": "Compare weather in Paris and London"}]
})
Streaming agent execution
# Stream agent stepsfor step in agent_executor.stream({"input": "Research AI trends"}):
if"actions"in step:
print(f"Tool: {step['actions'][0].tool}")
if"output"in step:
print(f"Output: {step['output']}")
Common patterns
Multi-document QA
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
# Load multiple documents
docs = [
loader.load("https://docs.python.org"),
loader.load("https://docs.numpy.org")
]
# QA with source citations
chain = load_qa_with_sources_chain(llm, chain_type="stuff")
result = chain({"input_documents": docs, "question": "How to use numpy arrays?"})
print(result["output_text"]) # Includes source citations
Custom tools with error handling
from langchain.tools import tool
@tooldefrisky_operation(query: str) -> str:
"""Perform a risky operation that might fail."""try:
# Your operation here
result = perform_operation(query)
returnf"Success: {result}"except Exception as e:
returnf"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
