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‎README.md‎

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<div style="text-align: center;">
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![Agentic Starter Kits](/images/ask_logo.png)
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# Agentic Starter Kits
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</div>
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## Purpose
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Production-ready agent templates to build and deploy LLM-powered agents. Run locally (e.g. with Ollama/Llama Stack) or deploy to Red Hat OpenShift. Each agent has step-by-step docs.
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Production-ready agent templates to build and deploy LLM-powered agents. Run locally (e.g. with Ollama/Llama Stack) or
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deploy to Red Hat OpenShift. Each agent has step-by-step docs.
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## Agents
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Choose an agent and follow its README for setup and deployment:
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- **[LangGraph ReAct](./agents/base/langgraph_react_agent/README.md)** – General-purpose agent using a ReAct loop: it reasons and calls tools (e.g. search, math) step by step. Built with LangGraph and LangChain.
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- **[LlamaIndex WebSearch](./agents/base/llamaindex_websearch_agent/README.md)** – Agent built on LlamaIndex that uses a web search tool to query the internet and use the results in its answers.
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- **[OpenAI Responses](./agents/base/openai_responses_agent/README.md)** – Minimal agent with no framework: only the OpenAI Python client and an Action/Observation loop with tools. Use with OpenAI or any compatible API.
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- **[LangGraph Agentic RAG](./agents/community/langgraph_agentic_rag/README.md)** – RAG agent that indexes documents in a vector store (Milvus) and retrieves relevant chunks to augment the LLM’s answers with your own data.
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- **[LangGraph ReAct with Database Memory](./agents/community/langgraph_react_with_database_memory/README.md)** – ReAct agent with PostgreSQL-backed conversation memory. Full chat history is persisted in the database while a FIFO sliding window keeps only the last N messages in the LLM context. Built with LangGraph and `create_agent` middleware.
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- **[LangGraph ReAct](./agents/base/langgraph_react_agent/README.md)** – General-purpose agent using a ReAct loop: it
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reasons and calls tools (e.g. search, math) step by step. Built with LangGraph and LangChain.
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- **[LlamaIndex WebSearch](./agents/base/llamaindex_websearch_agent/README.md)** – Agent built on LlamaIndex that uses a
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web search tool to query the internet and use the results in its answers.
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- **[OpenAI Responses](./agents/base/openai_responses_agent/README.md)** – Minimal agent with no framework: only the
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OpenAI Python client and an Action/Observation loop with tools. Use with OpenAI or any compatible API.
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- **[LangGraph Agentic RAG](./agents/community/langgraph_agentic_rag/README.md)** – RAG agent that indexes documents in
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a vector store (Milvus) and retrieves relevant chunks to augment the LLM’s answers with your own data.
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- **[LangGraph ReAct with Database Memory](./agents/community/langgraph_react_with_database_memory/README.md)** – ReAct
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agent with PostgreSQL-backed conversation memory. Full chat history is persisted in the database while a FIFO sliding
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window keeps only the last N messages in the LLM context. Built with LangGraph and `create_agent` middleware.
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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 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
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- Production-grade scaling and monitoring
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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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- 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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### Pre-requisitions to run that repo
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Run this script to set up repo stuff with a use of [UV](https://docs.astral.sh/uv/) and python 3.12
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Download repo
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```bash
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git clone https://github.com/red-hat-data-services/agentic-starter-kits
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```
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Get into root dir
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```bash
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cd agentic-starter-kits
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```
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Install UV
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```bash
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curl -LsSf https://astral.sh/uv/install.sh | sh
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```
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## Additional Resources
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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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- **Kubernetes**: https://kubernetes.io/docs/
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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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MIT License
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MIT License
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Copyright (c) 2026

‎agents/base/langgraph_react_agent/LICENSE‎

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MIT License
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Copyright (c) 2026

‎agents/base/llamaindex_websearch_agent/LICENSE‎

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MIT License
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Copyright (c) 2026

‎agents/base/openai_responses_agent/LICENSE‎

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MIT License
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Copyright (c) 2026

‎agents/community/langgraph_agentic_rag/LICENSE‎

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MIT License
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Copyright (c) 2024
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Copyright (c) 2026
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎agents/community/langgraph_react_with_database_memory/.env.example‎

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‎agents/community/langgraph_react_with_database_memory/LICENSE‎

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MIT License
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Copyright (c) 2025
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Copyright (c) 2026
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎agents/community/langgraph_react_with_database_memory/README.md‎

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## License
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MIT License
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Copyright (c) 2026

‎agents/community/langgraph_react_with_database_memory/template.env‎

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## RHOAI
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## LLM Configuration
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#BASE_URL=
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#API_KEY=
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#MODEL_ID=
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#API_KEY=
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#CONTAINER_IMAGE=
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#
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## PostgreSQL Database Configuration
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#POSTGRES_HOST=
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#POSTGRES_PORT=
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#POSTGRES_DB=
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#POSTGRES_USER=
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#POSTGRES_PASSWORD=
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#POSTGRES_DB=
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#POSTGRES_PORT=
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