🚀 FlowKit - Simplify Async Workflows
FlowKit provides fluent async workflows with lazy execution and minimal boilerplate.
⚡ Why FlowKit?
Before (Traditional Async):
async def fetch_user_data(user_id):
async with aiohttp.ClientSession() as session:
async with session.get(f"https://api.example.com/users/{user_id}") as response:
user = await response.json()
async with session.get("https://api.example.com/posts", params={"userId": user_id}) as posts_response:
posts = await posts_response.json()
return {"user": user, "posts": posts}
After (FlowKit):
@flowkit.simple
def fetch_user_data(user_id):
user = flowkit.get(f"https://api.example.com/users/{user_id}").json()
posts = flowkit.get("https://api.example.com/posts", params={"userId": user_id}).json()
return {"user": user, "posts": posts}
🎯 Key Features
- 🔗 Fluent API - Chain operations with
.pipe()method - 🛡️ Type Safe - Full IDE support and type hints
- 🚀 Modern Foundation - Built on battle-tested httpx
- 🔧 Framework Ready - FastAPI, Django, Flask examples
- 📦 Simple Installation -
pip install flowkit-async
📦 Installation
pip install flowkit-async
🔧 How FlowKit Works
FlowKit provides lazy async pipelines - operations are queued and executed when awaited.
import flowkit
# FlowKit operations return awaitable Flow objects
user_flow = flowkit.get("https://api.example.com/users/1")
posts_flow = flowkit.get("https://api.example.com/posts")
# Execute both operations
user = await user_flow.json()
posts = await posts_flow.json()
Core Concept: Flow Objects
Flow objects are awaitable pipelines that:
- Chain operations without immediate execution
- Execute when awaited in the current event loop
- Enable fluent, readable async patterns
This is structured async workflows, not sync-to-async magic.
🔧 How FlowKit Works
FlowKit provides lazy async pipelines. API calls return awaitable Flow objects that record operations and execute asynchronously when awaited.
FlowKit does NOT:
- convert blocking code into async
- use threads or greenlets
- bypass the event loop
This is structured async composition, not async magic.
🏆 Real-World Examples
Data Processing Pipeline
@flowkit.simple
def fetch_user_data(user_id: int):
"""Fetch and process user data."""
return (flowkit.get(f"https://jsonplaceholder.typicode.com/users/{user_id}")
.json()
.pipe(lambda user: {**user, "processed": True}))
Note: @flowkit.simple is intended for synchronous functions.
If used on async functions, it acts as a pass-through wrapper.
Concurrent Operations
@flowkit.simple
def fetch_dashboard():
"""Fetch multiple data sources concurrently."""
users = flowkit.get("https://jsonplaceholder.typicode.com/users").json()
posts = flowkit.get("https://jsonplaceholder.typicode.com/posts").json()
comments = flowkit.get("https://jsonplaceholder.typicode.com/comments").json()
# These execute concurrently when awaited
return {"users": users, "posts": posts, "comments": comments}
🛠 When to Use FlowKit
FlowKit is ideal for:
- API-heavy applications with multiple calls
- Data pipelines and processing workflows
- Teams wanting consistent async patterns
- Rapid prototyping with clean syntax
⚠️ Important Notes
FlowKit does NOT:
- Block the event loop or use thread pools
- Make synchronous code magically non-blocking
- Replace understanding of asyncio fundamentals
- Work with CPU-bound operations
Always use async/await with CPU-intensive tasks.
🆚 How FlowKit Compares
| Feature | FlowKit | aiohttp | httpx | requests |
|---|---|---|---|---|
| Fluent API | ✅ | ❌ | ❌ | ❌ |
| Type Safe | ✅ | ✅ | ✅ | ✅ |
| Sessions | ✅ | ✅ | ✅ | ❌ |
| Pipelines | ✅ | ❌ | ❌ | ❌ |
FlowKit provides structured async workflows with minimal overhead on top of httpx and is comparable in performance for typical I/O-bound workloads.
📋 License
MIT License - see LICENSE file for details.
🤝 Contributing
We welcome contributions! Please see our Contributing Guide for details.
🚀 Try FlowKit today for cleaner async workflows:
pip install flowkit-async
⭐ Star us on GitHub! github.com/flowkit/flowkit
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