agently-cli 0.2.0.dev2

Declarative AI Agent Framework

Agently - Declarative AI Agent Framework

Agently is the batteries included framework for creating AI agents in a declarative way with a simple CLI tool to initialize and run agents. Define your agents using YAML configurations and bring them to life with minimal code.

Core Design Principles

• Declarative Configuration: Define complete agents using simple YAML files
• Flexible Plugin Ecosystem: Extend agent capabilities with MCP, Semantic Kernel, and Agently plugins
• Community Sharing: Share and reuse plugins across the Agently community
• Provider Agnostic: Support for multiple model providers including OpenAI, Ollama, and more
• Streamlined CLI: Simple init and run commands to manage your agent lifecycle

Installation

Prerequisites

• Python 3.8 or newer

Mac

# Create a virtual environment (optional but recommended)
python3 -m venv venv
source venv/bin/activate

# Install from PyPI
pip install agently

# Or install from source
git clone https://github.com/onwardplatforms/agently.git
cd agently
make install

Windows

# Create a virtual environment (optional but recommended)
python -m venv venv
venv\Scripts\activate

# Install from PyPI
pip install agently

# Or install from source
git clone https://github.com/onwardplatforms/agently.git
cd agently
python -m pip install -r requirements.txt
python -m pip install -e .

Environment Setup

Copy the example environment file and update with your API keys:

# Mac/Linux
cp .env.example .env

# Windows
copy .env.example .env

Edit the .env file to include your API keys:

OPENAI_API_KEY=your_key_here
# Other API keys as needed

Quick Start

# Create a simple agent configuration (agently.yaml)
cat > agently.yaml << EOF
version: "1"
name: "Hello Agent"
description: "A simple greeting agent"
system_prompt: "You are a friendly assistant that helps with greetings."
model:
  provider: "openai"
  model: "gpt-4o"
  temperature: 0.7
env:
  OPENAI_API_KEY: ${{ env.OPENAI_API_KEY }}
EOF

# Run the agent
agently run

Plugin System

Agently features a unified plugin system that makes it easy to extend your agent's capabilities through various plugin types.

Supported Plugin Types

Semantic Kernel (SK) Plugins

Standard plugins that provide function-based capabilities to your agent:

plugins:
  github:
    - source: "username/plugin-name"  # Will use agently-plugin- prefix
      version: "main"
      variables:
        key: "value"
  local:
    - source: "./plugins/my-local-plugin"
      variables:
        key: "value"

Multi-Command Protocol (MCP) Servers

External servers that enable complex, stateful interactions:

plugins:
  github:
    - source: "username/mcp-server-name"
      type: "mcp"  # Identifies this as an MCP server
      version: "main"
      command: "python"  # How to start the server
      args: ["server.py"]
  local:
    - source: "./mcp-servers/my-local-server"
      type: "mcp"
      command: "python"
      args: ["server.py"]

Developing Plugins

Using the Agently SDK

For easier plugin development, we recommend using the Agently SDK, which provides base classes and utilities for creating plugins:

from agently_sdk.plugins import Plugin, PluginVariable, agently_function

class MyPlugin(Plugin):
    name = "my_plugin"
    description = "A useful description of what this plugin does"
    
    # Define configurable variables for your plugin
    api_key = PluginVariable(
        description="API key for the service",
        sensitive=True  # Marks as sensitive info
    )
    
    max_results = PluginVariable(
        description="Maximum number of results to return",
        default=10,
        type=int
    )
    
    @agently_function
    def my_function(self, param1: str, param2: int = 5) -> str:
        """Function description that will be used by the agent.
        
        Args:
            param1: First parameter description
            param2: Second parameter description
            
        Returns:
            Description of the return value
        """
        # Implementation using plugin variables
        return f"Processed {param1} with {self.max_results} results"

Plugin Variables

Plugins can define variables that:

• Allow for runtime configuration
• Can have default values, validation rules, and type constraints
• Are set via the variables section in the agent config

Plugin Installation & Management

Plugins are managed using a structured workflow similar to Terraform:

# Initialize and install all plugins defined in your config
agently init

# List all installed plugins
agently list

# Run your agent with the installed plugins
agently run

Plugin Storage

Plugins are stored in the .agently/plugins directory, organized by type:

• SK plugins: .agently/plugins/sk/
• MCP servers: .agently/plugins/mcp/

For more advanced usage and detailed documentation, check out the full plugin documentation.

Plugin Naming Conventions

When creating plugins to share with the community, follow these naming conventions for GitHub repositories:

• Semantic Kernel Plugins: Use the prefix agently-plugin-

  • Example: agently-plugin-weather for a weather plugin

• MCP Servers: Use the prefix agently-mcp-

  • Example: agently-mcp-database for a database MCP server

These conventions help with discoverability and make it clear what type of plugin a repository contains.

Coder Agent Quick Start

The Coder Agent is a powerful AI coding assistant with Git-backed code editing capabilities:

# Install in development mode
pip install -e .

# Navigate to the coder agent example
cd examples/coder_agent

# Run the coder agent
agently run

With the Coder Agent, you can:

• Create and modify files
• Search across codebases
• Find references to symbols
• Format and lint code
• Track changes with Git-based version control

Example interactions:

> create a fibonacci.py file
> add types and Google style docstrings
> search for all references to a function

For more details, see the Coder Agent documentation.

CLI Commands

Agently provides a convenient command-line interface for managing and interacting with your agents:

agently run

Run an agent using its configuration file.

# Basic usage with default configuration file (agently.yaml)
agently run

# Specify a different configuration file
agently run --agent path/to/config.yaml

# Set log level
agently run --log-level info

Options:

• --agent, -a: Path to agent configuration file (default: "agently.yaml")
• --log-level: Set the logging level (options: none, debug, info, warning, error, critical)

agently init

Initialize the agent and install required plugins based on configuration.

# Initialize using default configuration
agently init

# Force reinstallation of all plugins
agently init --force

# Suppress verbose output
agently init --quiet

Options:

• --agent, -a: Path to agent configuration file (default: "agently.yaml")
• --force: Force reinstallation of all plugins
• --quiet: Reduce output verbosity
• --log-level: Set the logging level

agently list

List available plugins or configurations.

# List all installed plugins
agently list

Documentation

For full documentation, visit docs.agently.run.

Examples

Check out the examples directory for complete working examples:

• Coder Agent: A powerful AI coding assistant with Git-backed changes
• Multi-Plugin Agent: An agent using multiple plugin sources

Development

Mac

# Clone the repository
git clone https://github.com/onwardplatforms/agently.git
cd agently

# Set up development environment
make install-dev

# Run tests
make test

# Format code
make format

# Run linters
make lint

Windows

# Clone the repository
git clone https://github.com/onwardplatforms/agently.git
cd agently

# Set up development environment
python -m pip install -r requirements.txt
python -m pip install -r requirements-dev.txt
python -m pip install -e .
pre-commit install

# Run tests
python -m pytest tests/ -v --cov=. --cov-report=term-missing

# Format code
python -m black agently
python -m isort agently

# Run linters
python -m flake8 agently

Creating Your Own Agent

1. Create a configuration file

   Create an agently.yaml file with your agent's configuration:

   version: "1"
   name: "My Custom Agent"
   description: "An agent that performs specific tasks"
   system_prompt: |
     You are a specialized assistant that helps with [YOUR SPECIFIC TASK].
     Please provide helpful, accurate, and concise responses.
   
   model:
     provider: "openai"
     model: "gpt-4o"  # or another model of your choice
     temperature: 0.7
   
   plugins:
     github:
       - source: "username/plugin-name"
         version: "main"
         variables:
           api_key: ${{ env.SERVICE_API_KEY }}
     local:
       - source: "./plugins/my-local-plugin"
         variables:
           max_results: 20
   
   env:
     OPENAI_API_KEY: ${{ env.OPENAI_API_KEY }}
     # Add other environment variables as needed
   

2. Initialize your agent

   agently init
   

3. Run your agent

   agently run
   

Troubleshooting

Mac

• If you encounter permission issues: sudo pip install agently
• For M1/M2/M3 Macs, you may need to install Rosetta 2: softwareupdate --install-rosetta

Windows

• If you see "Command not found" errors, ensure Python is in your PATH or use python -m prefix (e.g., python -m pip)
• If you get DLL load errors, try installing the Visual C++ Redistributable

License

MIT

Contributing

Contributions are welcome! Here's how you can contribute to Agently:

Code Contributions

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature/your-feature-name
5. Submit a pull request

Creating Plugins

1. Use the Agently SDK for developing plugins
2. Follow the naming conventions for GitHub repositories
3. Add comprehensive documentation in your plugin's README
4. Include example usage in your plugin's documentation

Documentation

Help improve our documentation by submitting PRs for:

• Fixes for unclear instructions
• Additional examples of using Agently
• Tutorials for specific use cases

Bug Reports

Found a bug? Please open an issue with:

• A clear description of the problem
• Steps to reproduce the issue
• Expected vs actual behavior
• Environment details (OS, Python version, etc.)

For more details, see CONTRIBUTING.md.