langchain

LangChain Best Practices

This guide outlines the definitive best practices for developing with LangChain. Adhere to these rules to ensure your LLM applications are modular, scalable, and production-ready.

1. Code Organization and Structure

Always structure your LangChain projects around core components, separating concerns into distinct modules. This enhances readability, testability, and maintainability.

✅ GOOD: Modular Structure Organize by component type (models, prompts, tools, agents, memory).

# my_project/
# ├── agents/
# │   └── flight_booking_agent.py
# ├── models/
# │   └── llm_config.py
# ├── prompts/
# │   └── flight_prompts.py
# ├── tools/
# │   └── flight_tools.py
# ├── memory/
# │   └── chat_memory.py
# └── main.py

❌ BAD: Monolithic Files Avoid dumping all logic into a single file.

# main.py (containing everything)
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
# ... many more imports and definitions
# ... LLM, prompt, tools, agent definition all in one file

2. Leverage LangChain Expression Language (LCEL)

LCEL is the modern, recommended way to compose chains. It offers first-class streaming, async support, and clear debugging. Never use deprecated LLMChain or older chain patterns.

✅ GOOD: LCEL for Chains Use the | operator for clear, composable pipelines.

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

# Define components
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{question}")
])
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
output_parser = StrOutputParser()

# Compose chain with LCEL
chain = prompt | llm | output_parser

# Invoke
response = chain.invoke({"question": "What is the capital of France?"})
print(response)

❌ BAD: Deprecated LLMChain This pattern is outdated and lacks modern features.