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Examples Overview

The Agent Kernel repository includes a comprehensive set of examples demonstrating different deployment patterns, frameworks, and integrations. All examples are located in the examples directory and are organized by deployment method and use case.

Directory Structure​

The examples are organized into the following main categories:

📁 API Examples (/examples/api)​

Examples demonstrating Agent Kernel's API capabilities and integrations:

  • a2a/ - Agent-to-Agent (A2A) compatibility examples
    • multi/ - Multi-agent runtime with CrewAI and OpenAI agents exposed as A2A compatible
  • mcp/ - Model Context Protocol (MCP) integration examples
    • multi/ - Multi-agent runtime with agents exposed as MCP tools

📁 CLI Examples (/examples/cli)​

Command-line interface examples for local development and testing:

  • adk/ - Google ADK (Agent Development Kit) agents with CLI interaction
  • crewai/ - CrewAI framework integration examples
  • langgraph/ - LangGraph framework integration examples
  • multi/ - Multi-agent examples combining different frameworks
  • openai/ - OpenAI Agent SDK integration examples

📁 Containerized Examples (/examples/containerized)​

Docker-based deployment examples:

  • openai/ - OpenAI agents running in Docker containers with REST API access

📁 AWS Containerized Examples (/examples/aws-containerized)​

AWS ECS/Fargate deployment examples:

  • adk/ - Google ADK agents deployed on AWS container services
  • crewai/ - CrewAI agents deployed on AWS container services

📁 AWS Serverless Examples (/examples/aws-serverless)​

AWS Lambda serverless deployment examples:

  • adk/ - Google ADK agents running on AWS Lambda
  • crewai/ - CrewAI agents running on AWS Lambda
  • langgraph/ - LangGraph agents running on AWS Lambda
  • openai/ - OpenAI agents running on AWS Lambda

Supported Frameworks​

Agent Kernel supports multiple AI agent frameworks:

FrameworkDescriptionExamples Available
Google ADKGoogle's Agent Development KitCLI, AWS Containerized, AWS Serverless
CrewAIMulti-agent orchestration frameworkCLI, AWS Containerized, AWS Serverless, API
LangGraphGraph-based agent frameworkCLI, AWS Serverless
OpenAI Agent SDKOpenAI's official agent frameworkCLI, Containerized, AWS Serverless, API

Deployment Patterns​

Local Development​

  • CLI Examples: Perfect for local development, testing, and prototyping
  • Run agents directly from command line with immediate feedback

API Integration​

  • A2A Compatibility: Enable agent-to-agent communication
  • MCP Integration: Expose agents as Model Context Protocol tools
  • REST API: Standard HTTP API for agent interaction

Container Deployment​

  • Docker: Containerized agents with REST API endpoints
  • AWS ECS/Fargate: Scalable container deployment on AWS

Serverless Deployment​

  • AWS Lambda: Event-driven, serverless agent execution
  • Cost-effective for sporadic workloads
  • Automatic scaling based on demand

Getting Started​

Each example includes:

  • README.md: Detailed setup and usage instructions
  • build.sh: Dependency installation script
  • Demo files: Working example implementations
  • Tests: Validation and testing capabilities

Quick Start Steps​

  1. Choose your deployment pattern (CLI, Containerized, or Serverless)
  2. Select your preferred framework (ADK, CrewAI, LangGraph, or OpenAI)
  3. Navigate to the example directory
  4. Follow the README instructions for setup and execution

Common Setup Pattern​

Most examples follow this pattern:

# Install dependencies
./build.sh

# For local development
./build.sh local

# Run the example
python demo.py  # or server.py for API examples

Integration Features​

A2A (Agent-to-Agent) Compatibility​

Enable agent-to-agent communication by setting a2a.enabled = True in your configuration.

MCP (Model Context Protocol) Support​

Expose agents as MCP tools by setting:

mcp.enabled = True
mcp.expose_agents = True

Multi-Agent Runtimes​

Several examples demonstrate running multiple agent frameworks within a single Agent Kernel runtime, showcasing the platform's flexibility and interoperability.

Prerequisites​

Depending on the example you choose, you may need:

  • Python 3.12+ with UV package manager
  • Docker (for containerized examples)
  • AWS CLI and credentials (for AWS examples)
  • Terraform (for AWS infrastructure deployment)
  • Valid API keys for the respective AI services (OpenAI, etc.)

Next Steps​

  • Browse the specific framework examples that match your use case
  • Start with CLI examples for local development
  • Progress to containerized or serverless deployments for production
  • Explore multi-agent examples for complex orchestration scenarios

For detailed implementation guides, refer to the individual README files in each example directory.