LangChain Local Dev Loop

Overview

Configure a rapid local development workflow for LangChain applications with testing, debugging, and hot reload capabilities.

Prerequisites

• Completed langchain-install-auth setup

• Python 3.9+ with virtual environment

• pytest and related testing tools

• IDE with Python support (VS Code recommended)

Instructions

Step 1: Set Up Project Structure

my-langchain-app/ ├── src/ │ ├── init.py │ ├── chains/ │ │ └── init.py │ ├── agents/ │ │ └── init.py │ └── prompts/ │ └── init.py ├── tests/ │ ├── init.py │ ├── conftest.py │ └── test_chains.py ├── .env ├── .env.example ├── pyproject.toml └── README.md

Step 2: Configure Testing

tests/conftest.py

import pytest from unittest.mock import MagicMock from langchain_core.messages import AIMessage

@pytest.fixture def mock_llm(): """Mock LLM for unit tests without API calls.""" mock = MagicMock() mock.invoke.return_value = AIMessage(content="Mocked response") return mock

@pytest.fixture def sample_prompt(): """Sample prompt for testing.""" from langchain_core.prompts import ChatPromptTemplate return ChatPromptTemplate.from_template("Test: {input}")

Step 3: Create Test File

tests/test_chains.py

def test_chain_construction(mock_llm, sample_prompt): """Test that chain can be constructed.""" from langchain_core.output_parsers import StrOutputParser

chain = sample_prompt | mock_llm | StrOutputParser()
assert chain is not None

def test_chain_invoke(mock_llm, sample_prompt): """Test chain invocation with mock.""" from langchain_core.output_parsers import StrOutputParser

chain = sample_prompt | mock_llm | StrOutputParser()
result = chain.invoke({"input": "test"})
assert result == "Mocked response"

Step 4: Set Up Development Tools

pyproject.toml

[project] name = "my-langchain-app" version = "0.1.0" requires-python = ">=3.9" dependencies = [ "langchain>=0.3.0", "langchain-openai>=0.2.0", "python-dotenv>=1.0.0", ]

[project.optional-dependencies] dev = [ "pytest>=8.0.0", "pytest-asyncio>=0.23.0", "pytest-cov>=4.0.0", "ruff>=0.1.0", "mypy>=1.0.0", ]

[tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"]

[tool.ruff] line-length = 100

Output

• Organized project structure with separation of concerns

• pytest configuration with fixtures for mocking LLMs

• Development dependencies configured

• Ready for rapid iteration

Error Handling

Error Cause Solution

Import Error Missing package Install with pip install -e ".[dev]"

Fixture Not Found conftest.py issue Ensure conftest.py is in tests/ directory

Async Test Error Missing marker Add @pytest.mark.asyncio decorator

Env Var Missing .env not loaded Use python-dotenv and load_dotenv()

Examples

Running Tests

Run all tests

pytest

Run with coverage

pytest --cov=src --cov-report=html

Run specific test

pytest tests/test_chains.py::test_chain_invoke -v

Watch mode (requires pytest-watch)

ptw

Integration Test Example

tests/test_integration.py

import pytest from dotenv import load_dotenv

load_dotenv()

@pytest.mark.integration def test_real_llm_call(): """Integration test with real LLM (requires API key).""" from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
response = llm.invoke("Say 'test passed'")
assert "test" in response.content.lower()

Resources

• pytest Documentation

• LangChain Testing Guide

• python-dotenv

Next Steps

Proceed to langchain-sdk-patterns for production-ready code patterns.