Agentic Starter Kits

Purpose

Agentic Starter Kits is a collection of production-ready agent templates that demonstrate how to build and deploy LLM-powered agents using modern frameworks. This repository provides:

• Base Agent Templates: Ready-to-use agent implementations using LangGraph and LlamaIndex
• Community Agent Examples: Advanced agents like RAG (Retrieval-Augmented Generation) systems
• Dual Deployment Options: Run agents locally for development or deploy to Red Hat OpenShift Cluster for production
• Llama stack Integration: Unified model serving with Ollama for local LLM inference
• Clear Documentation: Step-by-step guides for setup, configuration, and deployment

Deployment Options

Agents in this repository can support two deployment modes:

🖥️ Local Development

• Run agents on your local machine
• Use Llama Stack server with Ollama for model serving
• Ideal for development, testing, and experimentation
• No cloud infrastructure required

☁️ Production Deployment

• Deploy agents to Red Hat OpenShift Cluster
• Containerized deployment with Kubernetes
• Production-grade scaling and monitoring
• CI/CD ready

Repository Structure

Agentic-Starter-Kits/
├── agents/
│   ├── base/
│   │   ├── langgraph_react_agent/       # LangGraph ReAct agent 
│   │   └── llamaindex_websearch_agent/  # LlamaIndex web search agent
│   │   └── llamastack_agent/            # Llamastack bare API 
│   └── community/
│       └── langgraph_agentic_rag/       # RAG agent with Milvus vector store
├── run_llama_server.yaml                # Llama Stack server configuration
├── utils.py                             # Shared utilities
└── README.md                            # This file

How to Use This Repository

1. Start Here: Read this README to understand the overall structure and install core dependencies
2. Choose an Agent: Select an agent from the agents/ directory based on your use case
3. Follow Agent README: Navigate to the agent's directory and follow its specific README for:
   • Agent-specific dependencies installation
   • Configuration and setup
   • Local development or OpenShift deployment
   • Usage examples and API endpoints

Core Technologies

This repository uses the following frameworks and libraries:

Agent Frameworks

• LangGraph: Graph-based agent orchestration with state management
• LlamaIndex: Data framework for LLM applications with workflow support

Model Serving

• Llama Stack: Unified API for model inference and vector operations
• Ollama: Local LLM inference engine

Vector Stores (for RAG agents)

• Milvus Lite: Lightweight vector database for local development

Web Frameworks

• FastAPI: Modern Python web framework for REST APIs
• Uvicorn: ASGI server for async Python applications

Dependencies

• Python >= 3.10
• langchain-core, langchain-openai
• langgraph, langgraph-prebuilt
• llama-index, llama-index-core, llama-index-llms-openai
• llama-stack, llama-stack-client
• fastapi, uvicorn[standard]
• pydantic, python-dotenv
• openai, numpy, nest-asyncio

### Next Steps

After completing the installation, choose an agent and follow its specific README:

**Base Agents:**
- **[LangGraph ReAct Agent](./agents/base/langgraph_react_agent/README.md)** - General-purpose agent with tool use
- **[LlamaIndex WebSearch Agent](./agents/base/llamaindex_websearch_agent/README.md)** - Web search capabilities
- **[Llama Stack bare API](./agents/base/llamastack_agent/README.md)** - Web search capabilities

**Community Agents:**
- **[LangGraph Agentic RAG](./agents/community/langgraph_agentic_rag/README.md)** - RAG with Milvus vector store

Each agent directory contains:
- `README.md` - Overview and deployment instructions
- `requirements.txt` - Agent-specific dependencies

## Production Deployment (Red Hat OpenShift)

This section covers deploying agents to a Red Hat OpenShift cluster for production use.

### Prerequisites

- OpenShift CLI (`oc`) installed and authenticated to your cluster
- Docker with buildx plugin installed (`docker buildx install`)
- `envsubst` utility (`brew install gettext` on macOS)
- Access to a container registry (Quay.io, Docker Hub, or GHCR)
- Container registry authentication (`docker login <registry>`)

### Step 1: Configure Environment Variables

Copy the template environment file to the agent directory:

```bash
cp template.env agents/base/langgraph_react_agent/.env

Edit the .env file and fill in all required values (see notes below):

API_KEY=your-api-key-here
BASE_URL=https://your-llama-stack-distribution.com/v1
MODEL_ID=llama-3.1-8b-instruct
CONTAINER_IMAGE=quay.io/your-username/langgraph-react-agent:latest

Notes:

• API_KEY - contact your cluster administrator

• BASE_URL - should end with /v1

• MODEL_ID - contact your cluster administrator

• CONTAINER_IMAGE - full image path where the agent container will be pushed and pulled from. The image is built locally, pushed to this registry, and then deployed to OpenShift.

  Format: <registry>/<namespace>/<image-name>:<tag>

  Examples:

  • Quay.io: quay.io/your-username/langgraph-react-agent:latest
  • Docker Hub: docker.io/your-username/langgraph-react-agent:latest
  • GHCR: ghcr.io/your-org/langgraph-react-agent:latest

Next Steps

After completing the installation, choose an agent and follow its specific README:

Base Agents:

• LangGraph ReAct Agent - General-purpose agent with tool use
• LlamaIndex WebSearch Agent - Web search capabilities
• Llama Stack bare API - Web search capabilities

Community Agents:

• LangGraph Agentic RAG - RAG with Milvus vector store

Additional Resources

• Sample Notebook: sample_ASK_notebook.ipynb - Jupyter notebook with usage examples
• Llama Stack Documentation: https://llama-stack.readthedocs.io/
• Ollama Documentation: https://docs.ollama.com/
• OpenShift Documentation: https://docs.openshift.com/

Contributing

Contributions are welcome! Please see individual agent READMEs for specific guidelines.

License

See LICENSE file for details.