langchain-local-dev-loop
Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow".
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name: langchain-local-dev-loop description: | Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow". allowed-tools: Read, Write, Edit, Bash(pytest:), Bash(python:) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
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-authsetup - 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
Next Steps
Proceed to langchain-sdk-patterns for production-ready code patterns.
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