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

code

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

python

# 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

python

# 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

toml

# 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

bash

# 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

python

# 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

Next Steps

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