Examples Overview

The Agent Kernel repository includes a comprehensive set of examples demonstrating different multi-cloud deployment patterns, frameworks, and integrations for AWS, Azure, and GCP. 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
• slack/ - Slack integration example
• whatsapp/ - WhatsApp integration example
• instagram/ - Instagram integration example
• telegram/ - Telegram integration example

📁 CLI Examples (/examples/cli)

Command-line interface examples for local development and testing:

• adk/ - Google ADK (Agent Development Kit) agents with CLI interaction
• a2a/ - Agent-to-Agent (A2A) communication examples
• crewai/ - CrewAI framework integration examples
• guardrail/ - Content safety and compliance validation examples
  • openai/ - OpenAI Guardrails integration with LangGraph agents
• langgraph/ - LangGraph framework integration examples
• multi/ - Multi-agent examples combining different frameworks
• openai/ - OpenAI Agent SDK integration examples
• knowledgebase/openai/ - OpenAI Agents knowledge base demos split into chromadb/, neo4j/, starburst/, and multi/

📁 Sandbox Examples (/examples/sandbox)

Sandbox capability examples (execute code/commands in an isolated, permission-bounded environment). See the Sandbox guide:

• basic/ - Enable the sandbox, run code, persist a workspace across turns, manage named sessions
• profiles/ - Multiple named workload profiles (provider + scope routing: a docker-backed workspace and a local throwaway sandbox)
• policy/ - Policy/permissions on the docker provider: an enforced envelope (network deny, resource limits) and the fail-closed strict model for what docker cannot enforce (egress allowlist)
• docker/ - The docker provider: container-isolated execution, package installs, and enforced network policy (requires a Docker daemon)
• daytona/ - The daytona provider: cloud container sandboxes with enforced network and resource policy and native idle auto-stop (requires a Daytona API key)
• e2b/ - The e2b provider: Firecracker micro-VM sandboxes with a stateful Jupyter kernel (variables persist across executions) and enforced network policy (requires an E2B API key)
• identity/ - Sandbox code running under the authenticated end user's identity, end-to-end over REST (custom pre-hook, principal resolver, and bring-your-own provider)
• ec2-ssm/ - The ec2_ssm provider (mode-3 attach): execute code on an existing EC2 instance over SSM (manual; requires a real instance and AWS credentials)

📁 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
• openai-dynamodb-scalable/ - OpenAI agents on AWS ECS with SQS queue mode for scalable, asynchronous request processing and DynamoDB response storage

📁 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
• websocket-openai/ - OpenAI agents with WebSocket API for real-time bidirectional communication
• streaming-openai/ - OpenAI agents with WebSocket token-level streaming (execution.mode: stream)

📁 Azure Containerized Examples (/examples/azure-containerized)

Azure Container Apps deployment examples:

• adk/ - Google ADK agents deployed on Azure Container Apps
• crewai/ - CrewAI agents deployed on Azure Container Apps

📁 Azure Serverless Examples (/examples/azure-serverless)

Azure Functions serverless deployment examples:

• adk/ - Google ADK agents running on Azure Functions
• crewai/ - CrewAI agents running on Azure Functions
• langgraph/ - LangGraph agents running on Azure Functions
• openai/ - OpenAI agents running on Azure Functions

📁 GCP Serverless Examples (/examples/gcp-serverless)

GCP Cloud Run serverless deployment examples (scale-to-zero):

• openai/ - OpenAI agents on Cloud Run with Redis sessions
• openai-auth/ - OpenAI agents with JWT authentication via API Gateway
• openai-firestore/ - OpenAI agents with Firestore session storage

📁 GCP Containerized Examples (/examples/gcp-containerized)

GCP Cloud Run containerized deployment examples (always-on):

• openai/ - OpenAI agents on Cloud Run with Redis sessions
• openai-auth/ - OpenAI agents with JWT authentication via API Gateway

Supported Frameworks

Agent Kernel supports multiple AI agent frameworks:

Framework	Description	Examples Available
Google ADK	Google's Agent Development Kit	CLI, AWS Containerized, AWS Serverless, Azure Containerized, Azure Serverless
CrewAI	Multi-agent orchestration framework	CLI, AWS Containerized, AWS Serverless, Azure Containerized, Azure Serverless, API
LangGraph	Graph-based agent framework	CLI, AWS Serverless, Azure Serverless
OpenAI Agent SDK	OpenAI's official agent framework	CLI, Containerized, AWS Serverless, AWS Containerized, Azure Serverless, Azure Containerized, GCP Serverless, GCP Containerized, 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 (Multi-Cloud)

• Docker: Containerized agents with REST API endpoints
• AWS ECS/Fargate: Scalable container deployment on AWS
• Azure Container Apps: Scalable container deployment on Azure
• GCP Cloud Run (Containerized): Always-on container deployment on GCP

Serverless Deployment (Multi-Cloud)

• AWS Lambda: Event-driven, serverless agent execution on AWS
• Azure Functions: Event-driven, serverless agent execution on Azure
• GCP Cloud Run (Serverless): Scale-to-zero agent execution on GCP
• Cost-effective for sporadic workloads
• Automatic scaling based on demand across all cloud platforms

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)
• Azure CLI and credentials (for Azure examples)
• GCP CLI (gcloud) and credentials (for GCP examples)
• Terraform (for multi-cloud infrastructure deployment)
• Valid API keys for the respective AI services (OpenAI, etc.)

Use Cases: Skills-Driven End-to-End Agents

The use-cases/ directory contains complete agent projects built end-to-end using Agent Kernel skills and a coding assistant. Each use case starts from a SPEC.md describing the agent's purpose and requirements, then uses the ak-init, ak-build, ak-add-capabilities, ak-cloud-deploy, and ak-test skills to generate all project files.

Available Use Cases

• waste-sorting-assistant/: A waste sorting advisor agent that recommends disposal categories (recycle, compost, landfill, hazardous waste) based on item material and the user's local recycling rules. Includes OpenAI Agents SDK integration, session memory for region-specific rules, and AWS Lambda deployment with DynamoDB-backed session persistence.

How to Use the Use Cases

See use-cases/README.md for the full workflow, from installing Agent Kernel skills to asking a coding assistant to generate a complete project from a SPEC.md.

Unlike the examples/ directory (which demonstrates specific deployment patterns and integrations), the use-cases/ directory shows complete domain-specific agents that were built by a coding agent using the Agent Kernel skills pack.

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
• See use-cases/ for complete agents built with Agent Kernel skills

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