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Dec 19, 2025
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docs: Add quickstart plans #80

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12 changes: 8 additions & 4 deletions docs/quickstart/README.md
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<!--
IMPORTANT: All ```bash commands in this file are tested as part of e2e test suite: /testing/e2e/tests/test_quickstart_readme.py
-->
# Getting Started with Asya🎭
# Getting Started with Asya🎭 Locally

**5-minute guide to running Asya🎭 locally**
**Core idea**: Build multi-step AI/ML pipelines where each step deployed as an [actor](https://en.wikipedia.org/wiki/Actor_model) and scales independently. No infrastructure code in your code - just pure Python.
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@gemini-code-assist gemini-code-assist bot Dec 19, 2025

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medium

There's a small grammatical error here. It should be "is deployed" to be grammatically correct.

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**Core idea**: Build multi-step AI/ML pipelines where each step deployed as an [actor](https://en.wikipedia.org/wiki/Actor_model) and scales independently. No infrastructure code in your code - just pure Python.
**Core idea**: Build multi-step AI/ML pipelines where each step is deployed as an [actor](https://en.wikipedia.org/wiki/Actor_model) and scales independently. No infrastructure code in your code - just pure Python.


Asya🎭 is a Kubernetes-native queue-based actor framework for AI/ML workloads. Write pure Python functions, deploy them as actors, and let Asya🎭 handle queues, routing, and autoscaling (0→N pods based on queue depth).
## What You'll Learn

**Core idea**: Build multi-step AI/ML pipelines where each step scales independently. No infrastructure code in your handlers - just pure Python.
- Create a Kind cluster to run Kubernetes locally in Docker, and install KEDA for autoscaling
- Deploy the Asya operator with SQS transport (running via LocalStack)
- Build and deploy your first actor with scale-to-zero capability
- Test autoscaling by sending messages to actor queues
- Optionally add S3 storage, MCP gateway, and Prometheus monitoring

## Prerequisites

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8 changes: 8 additions & 0 deletions docs/quickstart/for-data-scientists.md
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Build and deploy your first Asya actor.

## What You'll Learn

- Write pure Python handlers (functions or classes) for ML pipelines
- Test handlers locally and package them in Docker images
- Deploy actors using AsyncActor CRDs with autoscaling
- Use Flow DSL to build multi-step pipelines with conditional routing
- Handle dynamic routing with envelope mode for AI agents

## Overview

As a data scientist, you focus on writing pure Python functions. Asya handles infrastructure, routing, scaling, and monitoring.
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8 changes: 8 additions & 0 deletions docs/quickstart/for-platform-engineers.md
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Deploy and manage Asya🎭 infrastructure.

## What You'll Learn

- Install and configure Asya operator with transports (SQS/RabbitMQ)
- Deploy gateway and crew actors for pipeline completion
- Support data science teams with templates and IAM configuration
- Set up monitoring with Prometheus and troubleshoot common issues
- Optimize scaling and costs for production workloads

## Overview

As platform engineer, you:
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- GETTING STARTED:
- Motivation: motivation.md
- Concepts: concepts.md
- Quickstart: quickstart/README.md
- Getting Started: quickstart/README.md
- For Data Scientists 🧑‍🔬: quickstart/for-data-scientists.md
- For Platform Engineers ⚙️: quickstart/for-platform-engineers.md
- INSTALLATION:
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