home / skills / jeremylongshore / claude-code-plugins-plus-skills / langchain-ci-integration
/plugins/saas-packs/langchain-pack/skills/langchain-ci-integration
This skill helps you configure LangChain CI/CD pipelines with testing, linting, and deployment steps for robust projects.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill langchain-ci-integrationReview the files below or copy the command above to add this skill to your agents.
---
name: langchain-ci-integration
description: |
Configure LangChain CI/CD integration with GitHub Actions and testing.
Use when setting up automated testing, configuring CI pipelines,
or integrating LangChain tests into your build process.
Trigger with phrases like "langchain CI", "langchain GitHub Actions",
"langchain automated tests", "CI langchain", "langchain pipeline".
allowed-tools: Read, Write, Edit, Bash(gh:*)
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---
# LangChain CI Integration
## Overview
Configure comprehensive CI/CD pipelines for LangChain applications with testing, linting, and deployment automation.
## Prerequisites
- GitHub repository with Actions enabled
- LangChain application with test suite
- API keys for testing (stored as GitHub Secrets)
## Instructions
### Step 1: Create GitHub Actions Workflow
```yaml
# .github/workflows/langchain-ci.yml
name: LangChain CI
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
env:
PYTHON_VERSION: "3.11"
jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
pip install ruff mypy
- name: Lint with Ruff
run: ruff check .
- name: Type check with mypy
run: mypy src/
test-unit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
pip install -e ".[dev]"
- name: Run unit tests
run: |
pytest tests/unit -v --cov=src --cov-report=xml
- name: Upload coverage
uses: codecov/codecov-action@v4
with:
files: coverage.xml
test-integration:
runs-on: ubuntu-latest
needs: [lint, test-unit]
# Only run on main branch or manual trigger
if: github.ref == 'refs/heads/main' || github.event_name == 'workflow_dispatch'
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
pip install -e ".[dev]"
- name: Run integration tests
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: |
pytest tests/integration -v -m integration
```
### Step 2: Configure Test Markers
```python
# pyproject.toml
[tool.pytest.ini_options]
markers = [
"unit: Unit tests (no external API calls)",
"integration: Integration tests (requires API keys)",
"slow: Slow tests (skip in fast mode)",
]
asyncio_mode = "auto"
testpaths = ["tests"]
```
### Step 3: Create Mock Fixtures
```python
# tests/conftest.py
import pytest
from unittest.mock import MagicMock, AsyncMock
from langchain_core.messages import AIMessage
@pytest.fixture
def mock_llm():
"""Mock LLM for unit tests."""
mock = MagicMock()
mock.invoke.return_value = AIMessage(content="Mock response")
mock.ainvoke = AsyncMock(return_value=AIMessage(content="Mock response"))
return mock
@pytest.fixture
def mock_chain(mock_llm):
"""Mock chain for testing."""
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
prompt = ChatPromptTemplate.from_template("{input}")
return prompt | mock_llm | StrOutputParser()
```
### Step 4: Add Pre-commit Hooks
```yaml
# .pre-commit-config.yaml
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.1.6
hooks:
- id: ruff
args: [--fix]
- id: ruff-format
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.7.1
hooks:
- id: mypy
additional_dependencies:
- langchain-core
- pydantic
```
### Step 5: Add Deployment Stage
```yaml
# Add to .github/workflows/langchain-ci.yml
deploy:
runs-on: ubuntu-latest
needs: [test-integration]
if: github.ref == 'refs/heads/main'
environment: production
steps:
- uses: actions/checkout@v4
- name: Deploy to Cloud Run
uses: google-github-actions/deploy-cloudrun@v2
with:
service: langchain-api
source: .
env_vars: |
LANGCHAIN_PROJECT=production
```
## Output
- GitHub Actions workflow with lint, test, deploy stages
- pytest configuration with markers
- Mock fixtures for unit testing
- Pre-commit hooks for code quality
## Examples
### Running Tests Locally
```bash
# Run unit tests only (fast)
pytest tests/unit -v
# Run with coverage
pytest tests/unit --cov=src --cov-report=html
# Run integration tests (requires API key)
OPENAI_API_KEY=sk-... pytest tests/integration -v -m integration
# Skip slow tests
pytest tests/ -v -m "not slow"
```
### Integration Test Example
```python
# tests/integration/test_chain.py
import pytest
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
@pytest.mark.integration
def test_real_chain_invocation():
"""Test with real LLM (requires API key)."""
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_template("Say exactly: {word}")
chain = prompt | llm
result = chain.invoke({"word": "hello"})
assert "hello" in result.content.lower()
```
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Secret Not Found | Missing GitHub secret | Add OPENAI_API_KEY to repository secrets |
| Rate Limit in CI | Too many API calls | Use mocks for unit tests, limit integration tests |
| Timeout | Slow tests | Add timeout markers, parallelize tests |
| Import Error | Missing dev dependencies | Ensure `.[dev]` extras installed |
## Resources
- [GitHub Actions Documentation](https://docs.github.com/en/actions)
- [pytest Documentation](https://docs.pytest.org/)
- [Pre-commit](https://pre-commit.com/)
## Next Steps
Proceed to `langchain-deploy-integration` for deployment configuration.
This skill configures LangChain CI/CD integration using GitHub Actions, pytest, and pre-commit hooks to automate linting, unit and integration tests, and deployment. It provides ready-to-use workflow templates, pytest markers, mock fixtures, and deployment steps so teams can enforce quality gates and run reproducible tests in CI.
The skill installs and wires a GitHub Actions workflow with separate jobs for linting, unit tests, integration tests, and optional deployment. It uses pytest markers to separate unit and integration tests, supplies mock fixtures for fast offline unit testing, and reads API keys from GitHub Secrets for real LLM integration tests. Pre-commit hooks run Ruff and mypy locally to keep code quality consistent before pushing.
How do I avoid paying for LLM calls in CI?
Keep integration tests off by default, run them only on main or via manual trigger, and use mock fixtures for unit tests to verify behavior without external calls.
Where should I store API keys for integration tests?
Add them to GitHub repository or organization Secrets and reference them in workflow jobs via env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} so they are not exposed in logs.
What if my integration tests hit rate limits?
Reduce frequency by running them less often, batch requests, add retries with backoff, or mock more interactions and only keep a small smoke test that uses real calls.