agentcube-cli 0.1.1

AgentCube CLI - A developer tool for packaging, building, and deploying AI agents to AgentCube

AgentCube CLI

AgentCube CLI is a developer tool that streamlines the development, packaging, building, and deployment of AI agents to AgentCube. It provides a unified interface for managing the complete agent lifecycle from local development to cloud deployment.

Quick Start

Prerequisites

• Python 3.8+
• Git
• Docker (optional, for container builds)

Installation

# Clone the repository
git clone https://github.com/volcano-sh/agentcube.git
cd agentcube/cmd/cli

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

Your First Agent

1. Package an existing agent:

   kubectl agentcube pack -f examples/hello-agent --agent-name "my-agent"
   

2. Build the container image:

   kubectl agentcube build -f examples/hello-agent
   

3. Publish to AgentCube:

   kubectl agentcube publish \
      -f examples/hello-agent \
      --image-url "docker.io/username/my-agent" \
   

4. Invoke your agent:

   kubectl agentcube invoke -f examples/hello-agent --payload '{"prompt": "Hello World!"}'
   

5. Check status:

   kubectl agentcube status -f examples/hello-agent
   

Features

• Multi-language Support: Python, Java (with more languages planned)
• Flexible Build Modes: Local Docker builds and cloud builds
• Multi-Provider Deployment: Support for AgentCube CRDs and standard Kubernetes deployments
• AgentCube Integration: Seamless publishing and management
• Developer-friendly: Rich CLI experience with detailed feedback
• CI/CD Ready: Python SDK for programmatic access
• Extensible Architecture: Plugin system for custom providers

Installation

From PyPI (Recommended)

pip install agentcube

From Source

git clone https://github.com/volcano-sh/agentcube.git
cd agentcube/cmd/cli
pip install -e .

Documentation

• Design
• Examples

Configuration

AgentCube uses a agent_metadata.yaml file to configure your agent:

agent_name: my-agent
description: "A sample AI agent"
language: python
entrypoint: python main.py
port: 8080
build_mode: local
requirements_file: requirements.txt

Architecture

AgentCube CLI follows a modular four-layer architecture:

1. CLI Layer: Typer-based command interface
2. Runtime Layer: Business logic and Python SDK
3. Operations Layer: Core domain logic
4. Services Layer: External integrations

Deployment Providers

AgentCube supports two deployment providers:

AgentCube Provider (Default)

Deploys agents using AgentCube's custom AgentRuntime CRDs, providing enhanced agent lifecycle management and integration with the AgentCube ecosystem.

kubectl agentcube publish -f examples/hello-agent --provider agentcube

Standard Kubernetes Provider

Deploys agents as standard Kubernetes Deployments and Services, suitable for environments without AgentCube CRDs installed.

kubectl agentcube publish -f examples/hello-agent --provider k8s \
  --node-port 30080 \
  --replicas 3

Command Reference

kubectl agentcube pack

Package the agent application into a standardized workspace.

kubectl agentcube pack [OPTIONS]

Options:
  -f, --workspace TEXT    Path to the agent workspace directory [default: .]
  --agent-name TEXT       Override the agent name
  --language TEXT         Programming language (python, java)
  --entrypoint TEXT       Override the entrypoint command
  --port INTEGER          Port to expose in the Dockerfile
  --build-mode TEXT       Build strategy: local or cloud
  --description TEXT      Agent description
  --output TEXT           Output path for packaged workspace
  --verbose               Enable detailed logging

kubectl agentcube build

Build the agent image based on the packaged workspace.

kubectl agentcube build [OPTIONS]

Options:
  -f, --workspace TEXT    Path to the agent workspace directory [default: .]
  -p, --proxy TEXT        Custom proxy URL for dependency resolution
  --cloud-provider TEXT   Cloud provider name (e.g., huawei)
  --output TEXT           Output path for build artifacts
  --verbose               Enable detailed logging

kubectl agentcube publish

Publish the agent to AgentCube

kubectl agentcube publish [OPTIONS]

Options:
  -f, --workspace TEXT    Path to the agent workspace directory [default: .]
  --version TEXT          Semantic version string (e.g., v1.0.0)
  --image-url TEXT        Image repository URL (required in local build mode)
  --image-username TEXT   Username for image repository
  --image-password TEXT   Password for image repository
  --description TEXT      Agent description
  --region TEXT           Deployment region
  --cloud-provider TEXT   Cloud provider name (e.g., huawei)
  --provider TEXT         Target provider for deployment (agentcube, k8s). 'agentcube' deploys AgentRuntime CR, 'k8s' deploys standard K8s Deployment/Service. [default: agentcube]
  --node-port INTEGER     Specific NodePort to use (30000-32767) for K8s deployment
  --replicas INTEGER      Number of replicas for K8s deployment (default: 1)
  --endpoint TEXT         Custom API endpoint for AgentCube or Kubernetes cluster
  --namespace TEXT        Kubernetes namespace to use for deployment [default: default]
  --verbose               Enable detailed logging

kubectl agentcube invoke

Invoke a published agent via AgentCube or Kubernetes.

kubectl agentcube invoke [OPTIONS]

Options:
  -f, --workspace TEXT    Path to the agent workspace directory [default: .]
  --payload TEXT          JSON-formatted input passed to the agent [default: {}]
  --header TEXT           Custom HTTP headers (e.g., 'Authorization: Bearer token')
  --provider TEXT         Target provider for deployment (agentcube, k8s). 'agentcube' deploys AgentRuntime CR, 'k8s' deploys standard K8s Deployment/Service. [default: agentcube]
  --verbose               Enable detailed logging

kubectl agentcube status

Check the status of a published agent.

kubectl agentcube status [OPTIONS]

Options:
  -f, --workspace TEXT    Path to the agent workspace directory [default: .]
  --provider TEXT         Target provider for deployment (agentcube, k8s). 'agentcube' deploys AgentRuntime CR, 'k8s' deploys standard K8s Deployment/Service. [default: agentcube]
  --verbose               Enable detailed logging

Agent Structure

An AgentCube workspace typically contains:

my-agent/
├── agent_metadata.yaml    # Agent configuration (auto-generated)
├── Dockerfile            # Container definition (auto-generated)
├── requirements.txt      # Python dependencies
├── main.py              # Agent entrypoint
└── src/                 # Source code

agent_metadata.yaml

agent_name: my-agent
description: "My AI agent"
language: python
entrypoint: python main.py
port: 8080
build_mode: local
requirements_file: requirements.txt

Language Support

Python

Fully supported with automatic dependency management and Dockerfile generation.

• Supported versions: Python 3.8+
• Package manager: pip
• Dependencies file: requirements.txt
• Example: examples/hello-agent

Java

Supported with Maven-based builds and OpenJDK runtime.

• Supported versions: Java 17+
• Build tool: Maven
• Dependencies file: pom.xml
• Note: Java example coming soon

Troubleshooting

Common Issues

1. "Docker is not available"

   • Install Docker and make sure it's running
   • Use --build-mode cloud for cloud builds

2. "Metadata file not found"

   • Run kubectl agentcube pack first to generate metadata
   • Ensure you're in the correct workspace directory

3. "Agent not published yet"

   • Run kubectl agentcube publish before trying to invoke
   • Check that the build completed successfully

4. "Provider not found"

   • Ensure you're using a valid provider: agentcube or k8s
   • Check that your Kubernetes cluster has the required CRDs (for agentcube provider)

Getting Help

# General help
kubectl agentcube --help

# Command-specific help
kubectl agentcube pack --help
kubectl agentcube build --help
kubectl agentcube publish --help
kubectl agentcube invoke --help
kubectl agentcube status --help

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Links

• AgentCube Main Project
• Volcano Scheduler
• Issue Tracker