Step by Step Guide to Building a Linear API Integration in Python

Introduction

Hey there, fellow dev! Ready to supercharge your project management with Linear? Let's dive into building a slick API integration using Python and the awesome linear-py package. Linear's API is a powerhouse, and we're about to harness it like pros.

Prerequisites

Before we jump in, make sure you've got:

• A Python environment (3.7+)
• A Linear account with an API key (grab it from your account settings)

Got those? Great! Let's roll.

Installation

First things first, let's get linear-py on board:

pip install linear-py

Easy peasy, right?

Authentication

Now, let's get that Linear client up and running:

from linear_py import LinearClient

client = LinearClient('your_api_key_here')

Boom! You're authenticated and ready to rock.

Basic Operations

Fetching Issues

Want to see what's on your plate? Here's how to fetch issues:

issues = client.issues.list()
for issue in issues:
    print(f"{issue.id}: {issue.title}")

Creating Issues

Got a new task? Let's add it to Linear:

new_issue = client.issues.create({"title": "Fix that pesky bug", "teamId": "your_team_id"})
print(f"Created issue: {new_issue.id}")

Updating Issues

Need to tweak an issue? No sweat:

client.issues.update("issue_id", {"status": "In Progress"})

Deleting Issues

Oops, duplicate issue? Let's zap it:

client.issues.delete("issue_id")

Advanced Features

Working with Teams

Get your team info like a boss:

teams = client.teams.list()
for team in teams:
    print(f"{team.id}: {team.name}")

Managing Projects

Keep those projects in check:

projects = client.projects.list()
for project in projects:
    print(f"{project.id}: {project.name}")

Handling Comments

Let's add some context to our issues:

client.comments.create({"issueId": "issue_id", "body": "Great progress on this!"})

Using Webhooks

Stay in the loop with webhooks:

webhook = client.webhooks.create({
    "url": "https://your-webhook-url.com",
    "resourceTypes": ["Issue", "Comment"]
})

Error Handling and Best Practices

Always wrap your API calls in try-except blocks to catch any hiccups:

try:
    issues = client.issues.list()
except Exception as e:
    print(f"Oops! Something went wrong: {e}")

And hey, play nice with rate limits. Linear's pretty generous, but let's not push it.

Example Use Case: Simple Issue Tracker

Let's put it all together with a quick issue tracker:

def track_issues():
    issues = client.issues.list({"state": "In Progress"})
    for issue in issues:
        print(f"Issue {issue.id} is in progress. Assigned to: {issue.assignee.name}")
        if issue.dueDate and issue.dueDate < datetime.now():
            print(f"  Warning: Issue {issue.id} is overdue!")

track_issues()

Performance Optimization

Pagination

Don't let large datasets slow you down. Use pagination:

all_issues = []
cursor = None
while True:
    issues, cursor = client.issues.list({"first": 100, "after": cursor})
    all_issues.extend(issues)
    if not cursor:
        break

Caching

Save those API calls. Cache frequently accessed data:

import functools

@functools.lru_cache(maxsize=None)
def get_team(team_id):
    return client.teams.get(team_id)

Conclusion

And there you have it! You're now equipped to build a robust Linear API integration in Python. Remember, the linear-py package is your friend – it's got your back with a ton of features we didn't even touch on here.

Keep exploring, keep building, and most importantly, keep crushing those tasks in Linear. Happy coding!