> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/reference/sdk-reference/main.md).

# Main

## AMESA Main API Documentation

### Overview

The AMESA Main API is the primary interface for the AMESA SDK. It provides a unified wrapper that combines functionality from `composabl-core`, `composabl-train`, and `composabl-cli` into a single, convenient package.

### Installation

```bash
pip install composabl
```

This single installation provides access to all AMESA SDK components.

### Package Structure

The main package re-exports all public APIs from:

* **composabl\_core**: Core components and building blocks
* **composabl\_train**: Training infrastructure
* **composabl\_cli**: Command-line interface (available via `composabl` command)

### Basic Usage

#### Importing

All functionality is available through the main `composabl` import:

```python
from composabl import (
    # Core Components
    Agent, Skill, Sensor, Scenario, Perceptor,
    
    # Skill Types
    SkillTeacher, SkillController, SkillSelector,
    SkillCoordinatedSet, SkillCoordinatedPopulation,
    
    # Goals
    MaintainGoal, ApproachGoal, AvoidGoal, 
    MaximizeGoal, MinimizeGoal,
    
    # Training
    Trainer,
)
```

#### Environment Setup

Before using AMESA, configure your environment:

```python
import os

# Required: Set your license key
os.environ["AMESA_LICENSE"] = "your-license-key"

# Required: Accept the EULA
os.environ["AMESA_EULA_AGREED"] = "1"

# Optional: Set log level
os.environ["LOGLEVEL"] = "INFO"  # DEBUG, INFO, WARNING, ERROR
```

#### Goal Types

```python
# Maintain a value
MaintainGoal(sensor_name, description, target, stop_distance)

# Approach a target
ApproachGoal(sensor_name, description, target)

# Avoid a value
AvoidGoal(sensor_name, description, target, stop_distance)

# Maximize a metric
MaximizeGoal(sensor_name, description)

# Minimize a metric
MinimizeGoal(sensor_name, description)
```

#### Configuration Options

```python
config = {
    "license": "key",
    "target": {
        # Choose one:
        "local": {"address": "host:port"},
        "docker": {"image": "name:tag"}
    },
    "env": {
        "name": "environment-id",
        "init": {}  # Environment parameters
    },
    "resources": {
        "sim_count": 4,
        "num_workers": 2,
        "num_gpus": 0
    }
}
```

### Core Classes

```python
# Agent - Main orchestrator
agent = Agent()
agent.add_sensor(sensor)
agent.add_sensors([sensor1, sensor2])
agent.add_skill(skill)
agent.add_skills([skill1, skill2])
agent.add_perceptor(perceptor)
agent.export(path)
agent.draw()  # Visualize structure

# Skill - Behavior module
skill = Skill(name, implementation)

# Sensor - Data transformer
sensor = Sensor(name, description, extractor_fn)

# Scenario - Initial conditions
scenario = Scenario(variable_dict)

# Trainer - Training orchestrator
trainer = Trainer(config)
trainer.train(agent, train_cycles)
trainer.evaluate(agent, num_episodes)
trainer.package(agent)
trainer.close()
```

### Migration Guide

If migrating from separate imports:

```python
# Old way
from composabl_core import Agent, Skill
from composabl_train import Trainer

# New way (recommended)
from composabl import Agent, Skill, Trainer
```

#### Debug Mode

Enable detailed logging:

```python
import logging
logging.basicConfig(level=logging.DEBUG)

# Or via environment
os.environ["LOGLEVEL"] = "DEBUG"
```


---

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