langchain
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/hoodini/ai-agents-skills --skill langchain- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads langchain automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
LangChain & LangGraph
Build sophisticated LLM applications with composable chains and agent graphs.
Quick Start
pip install langchain langchain-openai langchain-anthropic langgraph
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
# Simple chain
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
prompt = ChatPromptTemplate.from_template("Explain {topic} in simple terms.")
chain = prompt | llm
response = chain.invoke({"topic": "quantum computing"})
LCEL (LangChain Expression Language)
Compose chains with the pipe operator:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
# Chain with parsing
chain = (
{"topic": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
result = chain.invoke("machine learning")
RAG Pipeline
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
# RAG prompt
prompt = ChatPromptTemplate.from_template("""
Answer based on the following context:
{context}
Question: {question}
""")
# RAG chain
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
answer = rag_chain.invoke("What is the refund policy?")
LangGraph Agent
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool
from typing import TypedDict, Annotated
import operator
# Define state
class AgentState(TypedDict):
messages: Annotated[list, operator.add]
# Define tools
@tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
@tool
def calculator(expression: str) -> str:
"""Calculate mathematical expression."""
return str(eval(expression))
tools = [search, calculator]
# Create graph
graph = StateGraph(AgentState)
# Add nodes
graph.add_node("agent", call_model)
graph.add_node("tools", ToolNode(tools))
# Add edges
graph.set_entry_point("agent")
graph.add_conditional_edges(
"agent",
should_continue,
{"continue": "tools", "end": END}
)
graph.add_edge("tools", "agent")
# Compile
app = graph.compile()
# Run
result = app.invoke({"messages": [HumanMessage(content="What is 25 * 4?")]})
Structured Output
from langchain_core.pydantic_v1 import BaseModel, Field
class Person(BaseModel):
name: str = Field(description="Person's name")
age: int = Field(description="Person's age")
occupation: str = Field(description="Person's job")
# Structured LLM
structured_llm = llm.with_structured_output(Person)
result = structured_llm.invoke("John is a 30 year old engineer")
# Person(name='John', age=30, occupation='engineer')
Memory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
# Message history
store = {}
def get_session_history(session_id: str):
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
# Chain with memory
with_memory = RunnableWithMessageHistory(
chain,
get_session_history,
input_messages_key="input",
history_messages_key="history"
)
# Use with session
response = with_memory.invoke(
{"input": "My name is Alice"},
config={"configurable": {"session_id": "user123"}}
)
Streaming
# Stream tokens
async for chunk in chain.astream({"topic": "AI"}):
print(chunk.content, end="", flush=True)
# Stream events (for debugging)
async for event in chain.astream_events({"topic": "AI"}, version="v1"):
print(event)
LangSmith Tracing
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"
# All chains are now traced automatically
chain.invoke({"topic": "AI"})
Resources
- LangChain Docs: https://python.langchain.com/docs/introduction/
- LangGraph Docs: https://langchain-ai.github.io/langgraph/
- LangSmith: https://smith.langchain.com/
- LangChain Hub: https://smith.langchain.com/hub
- LangChain Templates: https://github.com/langchain-ai/langchain/tree/master/templates
FAQ
What does the langchain skill do?
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
How do I install langchain?
Run npx skills add https://github.com/hoodini/ai-agents-skills --skill langchain in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/langchain/ (personal) or .claude/skills/langchain/ (project).
Which AI agents can use langchain?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is langchain safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains langchain?
It is published by hoodini in hoodini/ai-agents-skills. It has 169 recorded installs.
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