llm = ChatOpenAI(model="gpt-4o") response = llm.invoke([HumanMessage(content="Hello!")]) print(response.content)


## Core Concepts

### Chains

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template(
    "Summarize this text in {language}: {text}"
)

chain = prompt | ChatOpenAI() | StrOutputParser()

result = chain.invoke({
    "language": "Spanish",
    "text": "Long English text..."
})

RAG Pipeline

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever()

# RAG chain
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke("What is the main topic?")

Common Patterns

Agents with Tools

from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.tools import tool

@tool
def search_database(query: str) -> str:
    """Search the database for information."""
    return db.search(query)

@tool
def calculate(expression: str) -> str:
    """Calculate a math expression."""
    return str(eval(expression))

tools = [search_database, calculate]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "What is 25 * 4?"})

Memory

from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

history = ChatMessageHistory()

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: history,
    input_messages_key="input",
    history_messages_key="history"
)

Best Practices

Do:

Use LCEL for chain composition
Implement proper error handling
Add observability with LangSmith
Cache expensive operations

Don't:

Chain too many models together
Skip input validation
Ignore token limits
Trust agent outputs blindly

Troubleshooting

Issue	Cause	Solution
Slow chain	Multiple LLM calls	Parallelize or cache
Context overflow	Too many documents	Implement summarization
Agent loops	Bad tool descriptions	Improve tool docstrings

RAG Pipeline

Common Patterns

Agents with Tools

Memory

Best Practices

Troubleshooting

References

langchain

LangChain

When to Use

Quick Start

More from g1joshi/agent-skills

template

mariadb

claude

javascript

typescript

python