Cycls SDK turns your AI-based code into instant web apps.

Your apps are streamed directly from your infrastructure, giving you full control over your data and deployment. Apps can seamlessly interact with each other, acting as both clients and servers in the network.

With app streaming, you can:

• Turn existing code into instant web apps

• Generate UIs on-the-fly with LLMs

• Allow apps to call and utilize each other

• Integrate with any model, framework, or infrastructure

The streaming architecture radically simplifies and accelerates the development of AI applications. Learn more in what is Cycls?.

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Creating an App

In this example, the @spark app simply echoes the user’s input by accessing message.content string and returning it back:

from cycls import Cycls

cycls = Cycls()

@cycls("@spark")
def app(message):
    return message.content

cycls.push()

The @cycls(handle) decorator registers the app function with the unique handle @spark.

cycls.push() command publishes the app on cycls.com/@spark:dev in development mode.

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Asynchronous Apps

For improved performance, the app function can be made asynchronous.

...
@cycls("@spark")
async def app(message):
    return message.content
...

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App State

Developing LLM based apps requires session-specific details like session id, and message history, which can be accessed as follows:

from cycls import Cycls

cycls = Cycls()

@cycls("@spark")
async def app(message):
    print(message.id)      # session id
    print(message.history) # message history
    return message.content

cycls.push()

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LLM Example

Here is working implementation of Meta’s open-source llama3-70b model (running on Groq) as a Cycls app.

from cycls import Cycls
from groq import AsyncGroq

cycls = Cycls(api_key="API_KEY")
groq = AsyncGroq(api_key="API_KEY")

async def groq_llm(x):
    stream = await groq.chat.completions.create(
        messages=x,
        model="llama3-70b-8192",
        temperature=0.5, max_tokens=1024, top_p=1, stop=None, 
        stream=True,
    )
    async def event_stream():
        async for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                yield content
    return event_stream()

@cycls("@spark")
async def groq_app(message):
    history = [{"role": "system", "content": "you are a helpful assistant."}]
    history +=  message.history
    history += [{"role": "user", "content": message.content}]
    return await groq_llm(history)
  
cycls.push()

Try it live cycls.com/@spark

Visit the cookbook for more examples.

Check out this replit video to publish a chat app in < 60 seconds.