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‎.gitignore‎

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.env
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.vscode
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__pycache__
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.pytest_cache
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.venv
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dist
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config.toml

‎LICENSE‎

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‎MAINTAINERS.md‎

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# Maintainers
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A repository maintainer is a committer with the additional privilege of managing issues, pull requests and some repository settings.
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## Current Maintainers
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| Name | GitHub Username |
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|-----------------|---------------------------------------|
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| Tomasz Guzik | [MRGuziX](https://github.com/MRGuziX) |
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| Wojciech Rębisz | [Wojciech-Rebisz](https://github.com/Wojciech-Rebisz) |
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‎README.md‎

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# agentic-starter-kits
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Starter kits for building and deploying AI agents. Run interactively locally or deploy to Red Hat OpenShift (including RHOAI) via LlamaStack.
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# Agentic Starter Kits
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## Purpose
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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:
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- **Base Agent Templates**: Ready-to-use agent implementations using LangGraph and LlamaIndex
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- **Community Agent Examples**: Advanced agents like RAG (Retrieval-Augmented Generation) systems
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- **Dual Deployment Options**: Run agents locally for development or deploy to [Red Hat OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) Cluster for production
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- **Llama stack Integration**: Unified model serving with [Ollama](https://ollama.com/) for local LLM inference
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- **Clear Documentation**: Step-by-step guides for setup, configuration, and deployment
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## Deployment Options
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Agents in this repository can support two deployment modes:
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### 🖥️ Local Development
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- Run agents on your local machine
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- Use [Llama Stack](https://llama-stack.readthedocs.io/) server with Ollama for model serving
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- Ideal for development, testing, and experimentation
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- No cloud infrastructure required
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### ☁️ Production Deployment
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- Deploy agents to Red Hat OpenShift Cluster
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- Containerized deployment with [Kubernetes](https://kubernetes.io/docs/)
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- Production-grade scaling and monitoring
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- CI/CD ready
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## Repository Structure
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```
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Agentic-Starter-Kits/
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├── agents/
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│ ├── base/
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│ │ ├── langgraph_react_agent/ # LangGraph ReAct agent
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│ │ └── llamaindex_websearch_agent/ # LlamaIndex web search agent
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│ │ └── llamastack_agent/ # Llamastack bare API
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│ └── community/
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│ └── langgraph_agentic_rag/ # RAG agent with Milvus vector store
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├── run_llama_server.yaml # Llama Stack server configuration
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├── utils.py # Shared utilities
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└── README.md # This file
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```
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## How to Use This Repository
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1. **Start Here**: Read this README to understand the overall structure and install core dependencies
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2. **Choose an Agent**: Select an agent from the `agents/` directory based on your use case
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3. **Follow Agent README**: Navigate to the agent's directory and follow its specific README for:
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- Agent-specific dependencies installation
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- Configuration and setup
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- Local development or OpenShift deployment
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- Usage examples and API endpoints
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## Core Technologies
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This repository uses the following frameworks and libraries:
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### Agent Frameworks
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- **LangGraph**: Graph-based agent orchestration with state management
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- **LlamaIndex**: Data framework for LLM applications with workflow support
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### Model Serving
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- **Llama Stack**: Unified API for model inference and vector operations
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- **Ollama**: Local LLM inference engine
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### Vector Stores (for RAG agents)
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- **Milvus Lite**: Lightweight vector database for local development
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### Web Frameworks
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- **FastAPI**: Modern Python web framework for REST APIs
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- **Uvicorn**: ASGI server for async Python applications
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### Dependencies
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- Python >= 3.10
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- langchain-core, langchain-openai
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- langgraph, langgraph-prebuilt
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- llama-index, llama-index-core, llama-index-llms-openai
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- llama-stack, llama-stack-client
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- fastapi, uvicorn[standard]
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- pydantic, python-dotenv
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- openai, numpy, nest-asyncio
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```
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### Next Steps
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After completing the installation, choose an agent and follow its specific README:
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**Base Agents:**
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- **[LangGraph ReAct Agent](./agents/base/langgraph_react_agent/README.md)** - General-purpose agent with tool use
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- **[LlamaIndex WebSearch Agent](./agents/base/llamaindex_websearch_agent/README.md)** - Web search capabilities
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- **[Llama Stack bare API](./agents/base/llamastack_agent/README.md)** - Web search capabilities
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**Community Agents:**
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- **[LangGraph Agentic RAG](./agents/community/langgraph_agentic_rag/README.md)** - RAG with Milvus vector store
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Each agent directory contains:
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- `README.md` - Overview and deployment instructions
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- `requirements.txt` - Agent-specific dependencies
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## Production Deployment (Red Hat OpenShift)
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This section covers deploying agents to a Red Hat OpenShift cluster for production use.
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### Prerequisites
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- OpenShift CLI (`oc`) installed and authenticated to your cluster
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- Docker with buildx plugin installed (`docker buildx install`)
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- `envsubst` utility (`brew install gettext` on macOS)
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- Access to a container registry (Quay.io, Docker Hub, or GHCR)
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- Container registry authentication (`docker login <registry>`)
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### Step 1: Configure Environment Variables
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Copy the template environment file to the agent directory:
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```bash
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cp template.env agents/base/langgraph_react_agent/.env
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```
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Edit the `.env` file and fill in all required values (see notes below):
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```
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API_KEY=your-api-key-here
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BASE_URL=https://your-llama-stack-distribution.com/v1
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MODEL_ID=llama-3.1-8b-instruct
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CONTAINER_IMAGE=quay.io/your-username/langgraph-react-agent:latest
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```
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**Notes:**
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- `API_KEY` - contact your cluster administrator
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- `BASE_URL` - should end with `/v1`
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- `MODEL_ID` - contact your cluster administrator
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- `CONTAINER_IMAGE` - full image path where the agent container will be pushed and pulled from.
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The image is built locally, pushed to this registry, and then deployed to OpenShift.
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Format: `<registry>/<namespace>/<image-name>:<tag>`
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Examples:
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- Quay.io: `quay.io/your-username/langgraph-react-agent:latest`
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- Docker Hub: `docker.io/your-username/langgraph-react-agent:latest`
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- GHCR: `ghcr.io/your-org/langgraph-react-agent:latest`
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## Next Steps
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After completing the installation, choose an agent and follow its specific README:
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**Base Agents:**
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- **[LangGraph ReAct Agent](./agents/base/langgraph_react_agent/README.md)** - General-purpose agent with tool use
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- **[LlamaIndex WebSearch Agent](./agents/base/llamaindex_websearch_agent/README.md)** - Web search capabilities
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- **[Llama Stack bare API](./agents/base/llamastack_agent/README.md)** - Web search capabilities
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**Community Agents:**
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- **[LangGraph Agentic RAG](./agents/community/langgraph_agentic_rag/README.md)** - RAG with Milvus vector store
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## Additional Resources
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- **Sample Notebook**: `sample_ASK_notebook.ipynb` - Jupyter notebook with usage examples
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- **Llama Stack Documentation**: https://llama-stack.readthedocs.io/
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- **Ollama Documentation**: https://docs.ollama.com/
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- **OpenShift Documentation**: https://docs.openshift.com/
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## Contributing
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Contributions are welcome! Please see individual agent READMEs for specific guidelines.
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## License
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See LICENSE file for details.

‎agents/__init__.py‎

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‎agents/base/__init__.py‎

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