Quick Start Guide

Get started with AEnvironment in 5 minutes - complete workflow from environment creation to cloud deployment

AEnvironment Overview

AEnvironment is a revolutionary Environment-as-Code development framework designed for modern AI applications and reinforcement learning training scenarios. Define reusable intelligent environments using native Python syntax with perfect MCP (Model Context Protocol) support and one-click cloud deployment.

🚀 5-Minute Quick Start

Step 1: Environment Initialization

Use the AEnvironment CLI tool to quickly create your first environment:

# Create project directory
mkdir my-aenv-project && cd my-aenv-project

# Initialize environment template
aenv init helloworld

Project Structure Analysis

After initialization, you'll get a standard AEnvironment project structure:

helloworld/                    # Project root directory
├── 📄 config.json            # Environment configuration file
├── 🐳 Dockerfile             # Container build file
├── 📋 requirements.txt       # Python dependencies list
└── 📁 src/                   # Source code directory
    ├── __init__.py          # Package initialization file
    └── custom_env.py        # Custom environment logic

💡 Tip: The scaffolding has generated a complete basic template for you to start development immediately

Step 2: Implement Custom Logic

Edit the src/custom_env.py file to implement your environment functionality:

from typing import Any, Dict
from aenv import register_tool, register_reward

@register_tool
def echo_tool(content: str) -> Dict[str, Any]:
    """
    Intelligent echo tool - demonstrates basic tool development

    Function: Receives any input content and returns structured response

    Parameters:
        content: Text content to echo

    Returns:
        Dictionary containing original content and processing results
    """
    return {
        "original_content": content,
        "processed_content": f"🎯 Processing result: {content}",
        "timestamp": "2024-12-12T17:30:00Z"
    }

@register_reward
def simple_reward(task: str) -> Dict[str, Any]:
    """
    Basic reward function - demonstrates reward mechanism implementation

    Function: Provides standardized feedback for task execution

    Parameters:
        task: Task description

    Returns:
        Reward information including score and feedback
    """
    return {
        "task_name": task,
        "status": "success",
        "score": 1.0,
        "feedback": f"✅ Task '{task}' executed successfully",
        "metadata": {"execution_time": 0.1}
    }

🔧 Extension Guide: To add dependencies or custom build logic, modify requirements.txt and Dockerfile

Step 3: Local Testing

Start the local testing environment from the project root directory (containing config.json file):

# Enter project directory
cd helloworld

# Start local environment (includes MCP server and debugging interface)
aenv run

Startup Process Logs

Debug Interface Preview

After successful startup, the system will automatically open the MCP Inspector debugging interface where you can:

• 🔍 Tool Testing: Directly call registered tools
• 📊 Real-time Monitoring: View tool execution status and results
• 🎯 Interactive Debugging: Test different parameter combinations

Step 4: Build Environment Image

After local testing, build the environment into a deployable Docker image:

# Build environment image
aenv build

# Build and push to image registry (requires registry configuration)
aenv build --push

Build Process

📦 Building environment image...
├── 🐳 Building Docker image: hello:1.0.0
├── 🔍 Running test suite: pytest tests/
├── ✅ All tests passed
└── 🚀 Image build complete

🔐 Image Push: To push to a private registry, ensure registry authentication is configured

Step 5: Publish to Hub

Publish environment metadata to AEnvironment Hub for cloud sharing:

# Publish environment to registry
aenv push

# View publish results
aenv get hello --version 1.0.0

Environment Metadata Example

{
  "id": "hello-1.0.0",
  "name": "hello",
  "description": "AEnvironment getting started example environment",
  "version": "1.0.0",
  "tags": ["getting-started", "example", "Python"],
  "artifacts": [
    {
      "type": "image",
      "content": "hello:1.0.0"
    }
  ],
  "build_config": {
    "dockerfile": "./Dockerfile"
  },
  "deploy_config": {
    "cpu": "1",
    "memory": "2G",
    "os": "linux"
  }
}

Development Workflow

🎯 Environment Usage Guide

Local Mode

Environment Configuration

Environment Variable	Description	Default Value
DUMMY_INSTANCE_IP	Local MCP service address	http://localhost:8081

Usage Example

import asyncio
from aenv import Environment

async def local_usage():
    # Connect to local environment
    async with Environment("helloworld") as env:
        # Discover available tools
        tools = await env.list_tools()
        print(f"🔧 Available tools: {[t["name"] for t in tools]}")

        # Call custom tool
        result = await env.call_tool(
            "echo_tool",
            {"content": "Hello from AEnvironment!"}
        )
        print(f"📤 Tool response: {result.content}")

# Run example
if __name__ == "__main__":
    asyncio.run(local_usage())

Cloud Production Mode

Cloud Environment Configuration

Environment Variable	Description	Example
AENV_SCHEDULER_URL	AEnvironment platform address	https://aenv.your-company.com
AENV_API_KEY	API authentication key	sk-xxx...xxx
AENV_TIMEOUT	Request timeout	30

Cloud Usage Example

import asyncio
import os
from aenv import Environment

async def cloud_usage():
    # Create cloud environment instance
    async with Environment("helloworld@1.0.0", timeout=60) as env:
        # Cloud environment auto-initializes
        tools = await env.list_tools()
        print(f"☁️ Cloud tools: {[t.name for t in tools]}")

        # Execute cloud task
        result = await env.call_tool(
            "echo_tool",
            {"content": "Hello from Cloud!"}
        )
        print(f"🚀 Cloud response: {result.content}")

if __name__ == "__main__":
    asyncio.run(cloud_usage())

🚀 Next Steps

After completing the quick start, we recommend the following learning path:

📚 Learning Path

1. Core Concepts - {doc}concepts - Deep dive into AEnvironment architecture design
2. SDK Guide - {doc}../guide/sdk - Master advanced Python SDK usage

---